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Blur Interpolation Transformer for Real-World Motion From Blur | Zhihang Zhong, Mingdeng Cao, Xiang Ji, Yinqiang Zheng, Imari Sato | This paper studies the challenging problem of recovering motion from blur, also known as joint deblurring and interpolation or blur temporal super-resolution. The challenges are twofold: 1) the current methods still leave considerable room for improvement in terms of visual quality even on the synthetic dataset, and 2) poor generalization to real-world data. To this end, we propose a blur interpolation transformer (BiT) to effectively unravel the underlying temporal correlation encoded in blur. Based on multi-scale residual Swin transformer blocks, we introduce dual-end temporal supervision and temporally symmetric ensembling strategies to generate effective features for time-varying motion rendering. In addition, we design a hybrid camera system to collect the first real-world dataset of one-to-many blur-sharp video pairs. Experimental results show that BiT has a significant gain over the state-of-the-art methods on the public dataset Adobe240. Besides, the proposed real-world dataset effectively helps the model generalize well to real blurry scenarios. Code and data are available at https://github.com/zzh-tech/BiT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhong_Blur_Interpolation_Transformer_for_Real-World_Motion_From_Blur_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhong_Blur_Interpolation_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11423 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhong_Blur_Interpolation_Transformer_for_Real-World_Motion_From_Blur_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhong_Blur_Interpolation_Transformer_for_Real-World_Motion_From_Blur_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Few-Shot Medical Segmentation: A Vector Quantization View | Shiqi Huang, Tingfa Xu, Ning Shen, Feng Mu, Jianan Li | The existing few-shot medical segmentation networks share the same practice that the more prototypes, the better performance. This phenomenon can be theoretically interpreted in Vector Quantization (VQ) view: the more prototypes, the more clusters are separated from pixel-wise feature points distributed over the full space. However, as we further think about few-shot segmentation with this perspective, it is found that the clusterization of feature points and the adaptation to unseen tasks have not received enough attention. Motivated by the observation, we propose a learning VQ mechanism consisting of grid-format VQ (GFVQ), self-organized VQ (SOVQ) and residual oriented VQ (ROVQ). To be specific, GFVQ generates the prototype matrix by averaging square grids over the spatial extent, which uniformly quantizes the local details; SOVQ adaptively assigns the feature points to different local classes and creates a new representation space where the learnable local prototypes are updated with a global view; ROVQ introduces residual information to fine-tune the aforementioned learned local prototypes without re-training, which benefits the generalization performance for the irrelevance to the training task. We empirically show that our VQ framework yields the state-of-the-art performance over abdomen, cardiac and prostate MRI datasets and expect this work will provoke a rethink of the current few-shot medical segmentation model design. Our code will soon be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Rethinking_Few-Shot_Medical_Segmentation_A_Vector_Quantization_View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Rethinking_Few-Shot_Medical_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Rethinking_Few-Shot_Medical_Segmentation_A_Vector_Quantization_View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Rethinking_Few-Shot_Medical_Segmentation_A_Vector_Quantization_View_CVPR_2023_paper.html | CVPR 2023 | null |
Event-Based Shape From Polarization | Manasi Muglikar, Leonard Bauersfeld, Diederik Paul Moeys, Davide Scaramuzza | State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a speed-resolution tradeoff: they either sacrifice the number of polarization angles measured or necessitate lengthy acquisition times due to framerate constraints, thus compromising either accuracy or latency. We tackle this tradeoff using event cameras. Event cameras operate at microseconds resolution with negligible motion blur, and output a continuous stream of events that precisely measures how light changes over time asynchronously. We propose a setup that consists of a linear polarizer rotating at high speeds in front of an event camera. Our method uses the continuous event stream caused by the rotation to reconstruct relative intensities at multiple polarizer angles. Experiments demonstrate that our method outperforms physics-based baselines using frames, reducing the MAE by 25% in synthetic and real-world datasets. In the real world, we observe, however, that the challenging conditions (i.e., when few events are generated) harm the performance of physics-based solutions. To overcome this, we propose a learning-based approach that learns to estimate surface normals even at low event-rates, improving the physics-based approach by 52% on the real world dataset. The proposed system achieves an acquisition speed equivalent to 50 fps (>twice the framerate of the commercial polarization sensor) while retaining the spatial resolution of 1MP. Our evaluation is based on the first large-scale dataset for event-based SfP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Muglikar_Event-Based_Shape_From_Polarization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Muglikar_Event-Based_Shape_From_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2301.06855 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Muglikar_Event-Based_Shape_From_Polarization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Muglikar_Event-Based_Shape_From_Polarization_CVPR_2023_paper.html | CVPR 2023 | null |
Architectural Backdoors in Neural Networks | Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot | Machine learning is vulnerable to adversarial manipulation. Previous literature has demonstrated that at the training stage attackers can manipulate data (Gu et al.) and data sampling procedures (Shumailov et al.) to control model behaviour. A common attack goal is to plant backdoors i.e. force the victim model to learn to recognise a trigger known only by the adversary. In this paper, we introduce a new class of backdoor attacks that hide inside model architectures i.e. in the inductive bias of the functions used to train. These backdoors are simple to implement, for instance by publishing open-source code for a backdoored model architecture that others will reuse unknowingly. We demonstrate that model architectural backdoors represent a real threat and, unlike other approaches, can survive a complete re-training from scratch. We formalise the main construction principles behind architectural backdoors, such as a connection between the input and the output, and describe some possible protections against them. We evaluate our attacks on computer vision benchmarks of different scales and demonstrate the underlying vulnerability is pervasive in a variety of common training settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bober-Irizar_Architectural_Backdoors_in_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bober-Irizar_Architectural_Backdoors_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.07840 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bober-Irizar_Architectural_Backdoors_in_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bober-Irizar_Architectural_Backdoors_in_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
ARO-Net: Learning Implicit Fields From Anchored Radial Observations | Yizhi Wang, Zeyu Huang, Ariel Shamir, Hui Huang, Hao Zhang, Ruizhen Hu | We introduce anchored radial observations (ARO), a novel shape encoding for learning implicit field representation of 3D shapes that is category-agnostic and generalizable amid significant shape variations. The main idea behind our work is to reason about shapes through partial observations from a set of viewpoints, called anchors. We develop a general and unified shape representation by employing a fixed set of anchors, via Fibonacci sampling, and designing a coordinate-based deep neural network to predict the occupancy value of a query point in space. Differently from prior neural implicit models that use global shape feature, our shape encoder operates on contextual, query-specific features. To predict point occupancy, locally observed shape information from the perspective of the anchors surrounding the input query point are encoded and aggregated through an attention module, before implicit decoding is performed. We demonstrate the quality and generality of our network, coined ARO-Net, on surface reconstruction from sparse point clouds, with tests on novel and unseen object categories, "one-shape" training, and comparisons to state-of-the-art neural and classical methods for reconstruction and tessellation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_ARO-Net_Learning_Implicit_Fields_From_Anchored_Radial_Observations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_ARO-Net_Learning_Implicit_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ARO-Net_Learning_Implicit_Fields_From_Anchored_Radial_Observations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ARO-Net_Learning_Implicit_Fields_From_Anchored_Radial_Observations_CVPR_2023_paper.html | CVPR 2023 | null |
All in One: Exploring Unified Video-Language Pre-Training | Jinpeng Wang, Yixiao Ge, Rui Yan, Yuying Ge, Kevin Qinghong Lin, Satoshi Tsutsui, Xudong Lin, Guanyu Cai, Jianping Wu, Ying Shan, Xiaohu Qie, Mike Zheng Shou | Mainstream Video-Language Pre-training models consist of three parts, a video encoder, a text encoder, and a video-text fusion Transformer. They pursue better performance via utilizing heavier unimodal encoders or multimodal fusion Transformers, resulting in increased parameters with lower efficiency in downstream tasks. In this work, we for the first time introduce an end-to-end video-language model, namely all-in-one Transformer, that embeds raw video and textual signals into joint representations using a unified backbone architecture. We argue that the unique temporal information of video data turns out to be a key barrier hindering the design of a modality-agnostic Transformer. To overcome the challenge, we introduce a novel and effective token rolling operation to encode temporal representations from video clips in a non-parametric manner. The careful design enables the representation learning of both video-text multimodal inputs and unimodal inputs using a unified backbone model. Our pre-trained all-in-one Transformer is transferred to various downstream video-text tasks after fine-tuning, including text-video retrieval, video-question answering, multiple choice and visual commonsense reasoning. State-of-the-art performances with the minimal model FLOPs on nine datasets demonstrate the superiority of our method compared to the competitive counterparts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_All_in_One_Exploring_Unified_Video-Language_Pre-Training_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2203.07303 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_All_in_One_Exploring_Unified_Video-Language_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_All_in_One_Exploring_Unified_Video-Language_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Parametric Implicit Face Representation for Audio-Driven Facial Reenactment | Ricong Huang, Peiwen Lai, Yipeng Qin, Guanbin Li | Audio-driven facial reenactment is a crucial technique that has a range of applications in film-making, virtual avatars and video conferences. Existing works either employ explicit intermediate face representations (e.g., 2D facial landmarks or 3D face models) or implicit ones (e.g., Neural Radiance Fields), thus suffering from the trade-offs between interpretability and expressive power, hence between controllability and quality of the results. In this work, we break these trade-offs with our novel parametric implicit face representation and propose a novel audio-driven facial reenactment framework that is both controllable and can generate high-quality talking heads. Specifically, our parametric implicit representation parameterizes the implicit representation with interpretable parameters of 3D face models, thereby taking the best of both explicit and implicit methods. In addition, we propose several new techniques to improve the three components of our framework, including i) incorporating contextual information into the audio-to-expression parameters encoding; ii) using conditional image synthesis to parameterize the implicit representation and implementing it with an innovative tri-plane structure for efficient learning; iii) formulating facial reenactment as a conditional image inpainting problem and proposing a novel data augmentation technique to improve model generalizability. Extensive experiments demonstrate that our method can generate more realistic results than previous methods with greater fidelity to the identities and talking styles of speakers. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Parametric_Implicit_Face_Representation_for_Audio-Driven_Facial_Reenactment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Parametric_Implicit_Face_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Parametric_Implicit_Face_Representation_for_Audio-Driven_Facial_Reenactment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Parametric_Implicit_Face_Representation_for_Audio-Driven_Facial_Reenactment_CVPR_2023_paper.html | CVPR 2023 | null |
Semantic Human Parsing via Scalable Semantic Transfer Over Multiple Label Domains | Jie Yang, Chaoqun Wang, Zhen Li, Junle Wang, Ruimao Zhang | This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i.e. various levels of label granularity) to train a powerful human parsing network. In practice, two common application scenarios are addressed, termed universal parsing and dedicated parsing, where the former aims to learn homogeneous human representations from multiple label domains and switch predictions by only using different segmentation heads, and the latter aims to learn a specific domain prediction while distilling the semantic knowledge from other domains. The proposed SST has the following appealing benefits: (1) it can capably serve as an effective training scheme to embed semantic associations of human body parts from multiple label domains into the human representation learning process; (2) it is an extensible semantic transfer framework without predetermining the overall relations of multiple label domains, which allows continuously adding human parsing datasets to promote the training. (3) the relevant modules are only used for auxiliary training and can be removed during inference, eliminating the extra reasoning cost. Experimental results demonstrate SST can effectively achieve promising universal human parsing performance as well as impressive improvements compared to its counterparts on three human parsing benchmarks (i.e., PASCAL-Person-Part, ATR, and CIHP). Code is available at https://github.com/yangjie-cv/SST. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Semantic_Human_Parsing_via_Scalable_Semantic_Transfer_Over_Multiple_Label_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.04140 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Semantic_Human_Parsing_via_Scalable_Semantic_Transfer_Over_Multiple_Label_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Semantic_Human_Parsing_via_Scalable_Semantic_Transfer_Over_Multiple_Label_CVPR_2023_paper.html | CVPR 2023 | null |
Making Vision Transformers Efficient From a Token Sparsification View | Shuning Chang, Pichao Wang, Ming Lin, Fan Wang, David Junhao Zhang, Rong Jin, Mike Zheng Shou | The quadratic computational complexity to the number of tokens limits the practical applications of Vision Transformers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) application difficulty in the local vision transformer, and (iii) non-general-purpose networks for downstream tasks. In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks. The semantic tokens represent cluster centers, and they are initialized by pooling image tokens in space and recovered by attention, which can adaptively represent global or local semantic information. Due to the cluster properties, a few semantic tokens can attain the same effect as vast image tokens, for both global and local vision transformers. For instance, only 16 semantic tokens on DeiT-(Tiny,Small,Base) can achieve the same accuracy with more than 100% inference speed improvement and nearly 60% FLOPs reduction; on Swin-(Tiny,Small,Base), we can employ 16 semantic tokens in each window to further speed it up by around 20% with slight accuracy increase. Besides great success in image classification, we also extend our method to video recognition. In addition, we design a STViT-R(ecovery) network to restore the detailed spatial information based on the STViT, making it work for downstream tasks, which is powerless for previous token sparsification methods. Experiments demonstrate that our method can achieve competitive results compared to the original networks in object detection and instance segmentation, with over 30% FLOPs reduction for backbone. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chang_Making_Vision_Transformers_Efficient_From_a_Token_Sparsification_View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chang_Making_Vision_Transformers_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08685 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Making_Vision_Transformers_Efficient_From_a_Token_Sparsification_View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Making_Vision_Transformers_Efficient_From_a_Token_Sparsification_View_CVPR_2023_paper.html | CVPR 2023 | null |
GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection | Xixi Liu, Yaroslava Lochman, Christopher Zach | Out-of-distribution (OOD) detection has been extensively studied in order to successfully deploy neural networks, in particular, for safety-critical applications. Moreover, performing OOD detection on large-scale datasets is closer to reality, but is also more challenging. Several approaches need to either access the training data for score design or expose models to outliers during training. Some post-hoc methods are able to avoid the aforementioned constraints, but are less competitive. In this work, we propose Generalized ENtropy score (GEN), a simple but effective entropy-based score function, which can be applied to any pre-trained softmax-based classifier. Its performance is demonstrated on the large-scale ImageNet-1k OOD detection benchmark. It consistently improves the average AUROC across six commonly-used CNN-based and visual transformer classifiers over a number of state-of-the-art post-hoc methods. The average AUROC improvement is at least 3.5%. Furthermore, we used GEN on top of feature-based enhancing methods as well as methods using training statistics to further improve the OOD detection performance. The code is available at: https://github.com/XixiLiu95/GEN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_GEN_Pushing_the_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
RefCLIP: A Universal Teacher for Weakly Supervised Referring Expression Comprehension | Lei Jin, Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Guannan Jiang, Annan Shu, Rongrong Ji | Referring Expression Comprehension (REC) is a task of grounding the referent based on an expression, and its development is greatly limited by expensive instance-level annotations. Most existing weakly supervised methods are built based on two-stage detection networks, which are computationally expensive. In this paper, we resort to the efficient one-stage detector and propose a novel weakly supervised model called RefCLIP. Specifically, RefCLIP redefines weakly supervised REC as an anchor-text matching problem, which can avoid the complex post-processing in existing methods. To achieve weakly supervised learning, we introduce anchor-based contrastive loss to optimize RefCLIP via numerous anchor-text pairs. Based on RefCLIP, we further propose the first model-agnostic weakly supervised training scheme for existing REC models, where RefCLIP acts as a mature teacher to generate pseudo-labels for teaching common REC models. With our careful designs, this scheme can even help existing REC models achieve better weakly supervised performance than RefCLIP, e.g., TransVG and SimREC. To validate our approaches, we conduct extensive experiments on four REC benchmarks, i.e., RefCOCO, RefCOCO+, RefCOCOg and ReferItGame. Experimental results not only report our significant performance gains over existing weakly supervised models, e.g., +24.87% on RefCOCO, but also show the 5x faster inference speed. Project: https://refclip.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_RefCLIP_A_Universal_Teacher_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_RefCLIP_A_Universal_Teacher_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_RefCLIP_A_Universal_Teacher_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.html | CVPR 2023 | null |
VILA: Learning Image Aesthetics From User Comments With Vision-Language Pretraining | Junjie Ke, Keren Ye, Jiahui Yu, Yonghui Wu, Peyman Milanfar, Feng Yang | Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal finetuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ke_VILA_Learning_Image_Aesthetics_From_User_Comments_With_Vision-Language_Pretraining_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ke_VILA_Learning_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_VILA_Learning_Image_Aesthetics_From_User_Comments_With_Vision-Language_Pretraining_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_VILA_Learning_Image_Aesthetics_From_User_Comments_With_Vision-Language_Pretraining_CVPR_2023_paper.html | CVPR 2023 | null |
Learnable Skeleton-Aware 3D Point Cloud Sampling | Cheng Wen, Baosheng Yu, Dacheng Tao | Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-to-sample methods have recently received increasing attention from the community for jointly training with downstream tasks. However, the above-mentioned task-specific sampling methods usually fail to explore the geometries of objects in an explicit manner. In this paper, we introduce a new skeleton-aware learning-to-sample method by learning object skeletons as the prior knowledge to preserve the object geometry and topology information during sampling. Specifically, without labor-intensive annotations per object category, we first learn category-agnostic object skeletons via the medial axis transform definition in an unsupervised manner. With object skeleton, we then evaluate the histogram of the local feature size as the prior knowledge to formulate skeleton-aware sampling from a probabilistic perspective. Additionally, the proposed skeleton-aware sampling pipeline with the task network is thus end-to-end trainable by exploring the reparameterization trick. Extensive experiments on three popular downstream tasks, point cloud classification, retrieval, and reconstruction, demonstrate the effectiveness of the proposed method for efficient point cloud analysis. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wen_Learnable_Skeleton-Aware_3D_Point_Cloud_Sampling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wen_Learnable_Skeleton-Aware_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Learnable_Skeleton-Aware_3D_Point_Cloud_Sampling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Learnable_Skeleton-Aware_3D_Point_Cloud_Sampling_CVPR_2023_paper.html | CVPR 2023 | null |
Boundary-Enhanced Co-Training for Weakly Supervised Semantic Segmentation | Shenghai Rong, Bohai Tu, Zilei Wang, Junjie Li | The existing weakly supervised semantic segmentation (WSSS) methods pay much attention to generating accurate and complete class activation maps (CAMs) as pseudo-labels, while ignoring the importance of training the segmentation networks. In this work, we observe that there is an inconsistency between the quality of the pseudo-labels in CAMs and the performance of the final segmentation model, and the mislabeled pixels mainly lie on the boundary areas. Inspired by these findings, we argue that the focus of WSSS should be shifted to robust learning given the noisy pseudo-labels, and further propose a boundary-enhanced co-training (BECO) method for training the segmentation model. To be specific, we first propose to use a co-training paradigm with two interactive networks to improve the learning of uncertain pixels. Then we propose a boundary-enhanced strategy to boost the prediction of difficult boundary areas, which utilizes reliable predictions to construct artificial boundaries. Benefiting from the design of co-training and boundary enhancement, our method can achieve promising segmentation performance for different CAMs. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate the superiority of our BECO over other state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rong_Boundary-Enhanced_Co-Training_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rong_Boundary-Enhanced_Co-Training_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rong_Boundary-Enhanced_Co-Training_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rong_Boundary-Enhanced_Co-Training_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild | Avinab Saha, Sandeep Mishra, Alan C. Bovik | Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting. The unique novelty of our approach is its ability to generate low-level representations of image quality that are complementary to high-level features representing image content. We refer to the framework used to train the two encoders as Re-IQA. For Image Quality Assessment in the Wild, we deploy the complementary low and high-level image representations obtained from the Re-IQA framework to train a linear regression model, which is used to map the image representations to the ground truth quality scores, refer Figure 1. Our method achieves state-of-the-art performance on multiple large-scale image quality assessment databases containing both real and synthetic distortions, demonstrating how deep neural networks can be trained in an unsupervised setting to produce perceptually relevant representations. We conclude from our experiments that the low and high-level features obtained are indeed complementary and positively impact the performance of the linear regressor. A public release of all the codes associated with this work will be made available on GitHub. | https://openaccess.thecvf.com/content/CVPR2023/papers/Saha_Re-IQA_Unsupervised_Learning_for_Image_Quality_Assessment_in_the_Wild_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Saha_Re-IQA_Unsupervised_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Saha_Re-IQA_Unsupervised_Learning_for_Image_Quality_Assessment_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Saha_Re-IQA_Unsupervised_Learning_for_Image_Quality_Assessment_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
Procedure-Aware Pretraining for Instructional Video Understanding | Honglu Zhou, Roberto Martín-Martín, Mubbasir Kapadia, Silvio Savarese, Juan Carlos Niebles | Our goal is to learn a video representation that is useful for downstream procedure understanding tasks in instructional videos. Due to the small amount of available annotations, a key challenge in procedure understanding is to be able to extract from unlabeled videos the procedural knowledge such as the identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or the potential next steps given partial progress in its execution. Our main insight is that instructional videos depict sequences of steps that repeat between instances of the same or different tasks, and that this structure can be well represented by a Procedural Knowledge Graph (PKG), where nodes are discrete steps and edges connect steps that occur sequentially in the instructional activities. This graph can then be used to generate pseudo labels to train a video representation that encodes the procedural knowledge in a more accessible form to generalize to multiple procedure understanding tasks. We build a PKG by combining information from a text-based procedural knowledge database and an unlabeled instructional video corpus and then use it to generate training pseudo labels with four novel pre-training objectives. We call this PKG-based pre-training procedure and the resulting model Paprika, Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We evaluate Paprika on COIN and CrossTask for procedure understanding tasks such as task recognition, step recognition, and step forecasting. Paprika yields a video representation that improves over the state of the art: up to 11.23% gains in accuracy in 12 evaluation settings. Implementation is available at https://github.com/salesforce/paprika. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Procedure-Aware_Pretraining_for_Instructional_Video_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Procedure-Aware_Pretraining_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Procedure-Aware_Pretraining_for_Instructional_Video_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Procedure-Aware_Pretraining_for_Instructional_Video_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Sample-Level Multi-View Graph Clustering | Yuze Tan, Yixi Liu, Shudong Huang, Wentao Feng, Jiancheng Lv | Multi-view clustering have hitherto been studied due to their effectiveness in dealing with heterogeneous data. Despite the empirical success made by recent works, there still exists several severe challenges. Particularly, previous multi-view clustering algorithms seldom consider the topological structure in data, which is essential for clustering data on manifold. Moreover, existing methods cannot fully consistency the consistency of local structures between different views as they explore the clustering structure in a view-wise manner. In this paper, we propose to exploit the implied data manifold by learning the topological structure of data. Besides, considering that the consistency of multiple views is manifested in the generally similar local structure while the inconsistent structures are minority, we further explore the intersections of multiple views in the sample level such that the cross-view consistency can be better maintained. We model the above concerns in a unified framework and design an efficient algorithm to solve the corresponding optimization problem. Experimental results on various multi-view datasets certificate the effectiveness of the proposed method and verify its superiority over other SOTA approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Sample-Level_Multi-View_Graph_Clustering_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Sample-Level_Multi-View_Graph_Clustering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Sample-Level_Multi-View_Graph_Clustering_CVPR_2023_paper.html | CVPR 2023 | null |
Fine-Grained Audible Video Description | Xuyang Shen, Dong Li, Jinxing Zhou, Zhen Qin, Bowen He, Xiaodong Han, Aixuan Li, Yuchao Dai, Lingpeng Kong, Meng Wang, Yu Qiao, Yiran Zhong | We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audible videos, including the appearance and spatial locations of each object, the actions of moving objects, and the sounds in videos. Existing visual-language modeling tasks often concentrate on visual cues in videos while undervaluing the language and audio modalities. On the other hand, FAVD requires not only audio-visual-language modeling skills but also paragraph-level language generation abilities. We construct the first fine-grained audible video description benchmark (FAVDBench) to facilitate this research. For each video clip, we first provide a one-sentence summary of the video, ie, the caption, followed by 4-6 sentences describing the visual details and 1-2 audio-related descriptions at the end. The descriptions are provided in both English and Chinese. We create two new metrics for this task: an EntityScore to gauge the completeness of entities in the visual descriptions, and an AudioScore to assess the audio descriptions. As a preliminary approach to this task, we propose an audio-visual-language transformer that extends existing video captioning model with an additional audio branch. We combine the masked language modeling and auto-regressive language modeling losses to optimize our model so that it can produce paragraph-level descriptions. We illustrate the efficiency of our model in audio-visual-language modeling by evaluating it against the proposed benchmark using both conventional captioning metrics and our proposed metrics. We further put our benchmark to the test in video generation models, demonstrating that employing fine-grained video descriptions can create more intricate videos than using captions. Code and dataset are available at https://github.com/OpenNLPLab/FAVDBench. Our online benchmark is available at www.avlbench.opennlplab.cn. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_Fine-Grained_Audible_Video_Description_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_Fine-Grained_Audible_Video_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.15616 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Fine-Grained_Audible_Video_Description_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Fine-Grained_Audible_Video_Description_CVPR_2023_paper.html | CVPR 2023 | null |
3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds | Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric P. Xing | Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) model is largely neglected as most existing benchmarks are dominated by point clouds captured under normal weather. We introduce SemanticSTF, an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3DSS under various adverse weather conditions. We investigate universal 3DSS modeling with two tasks: 1) domain adaptive 3DSS that adapts from normal-weather data to adverse-weather data; 2) domain generalized 3DSS that learns a generalizable model from normal-weather data. Our studies reveal the challenge while existing 3DSS methods encounter adverse-weather data, showing the great value of SemanticSTF in steering the future endeavor along this very meaningful research direction. In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their encoded embeddings, ultimately leading to a generalizable model that effectively improves 3DSS under various adverse weather. The SemanticSTF and related codes are available at https://github.com/xiaoaoran/SemanticSTF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_3D_Semantic_Segmentation_in_the_Wild_Learning_Generalized_Models_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiao_3D_Semantic_Segmentation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_3D_Semantic_Segmentation_in_the_Wild_Learning_Generalized_Models_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_3D_Semantic_Segmentation_in_the_Wild_Learning_Generalized_Models_for_CVPR_2023_paper.html | CVPR 2023 | null |
Catch Missing Details: Image Reconstruction With Frequency Augmented Variational Autoencoder | Xinmiao Lin, Yikang Li, Jenhao Hsiao, Chiuman Ho, Yu Kong | The popular VQ-VAE models reconstruct images through learning a discrete codebook but suffer from a significant issue in the rapid quality degradation of image reconstruction as the compression rate rises. One major reason is that a higher compression rate induces more loss of visual signals on the higher frequency spectrum, which reflect the details on pixel space. In this paper, a Frequency Complement Module (FCM) architecture is proposed to capture the missing frequency information for enhancing reconstruction quality. The FCM can be easily incorporated into the VQ-VAE structure, and we refer to the new model as Frequancy Augmented VAE (FA-VAE). In addition, a Dynamic Spectrum Loss (DSL) is introduced to guide the FCMs to balance between various frequencies dynamically for optimal reconstruction. FA-VAE is further extended to the text-to-image synthesis task, and a Cross-attention Autoregressive Transformer (CAT) is proposed to obtain more precise semantic attributes in texts. Extensive reconstruction experiments with different compression rates are conducted on several benchmark datasets, and the results demonstrate that the proposed FA-VAE is able to restore more faithfully the details compared to SOTA methods. CAT also shows improved generation quality with better image-text semantic alignment. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Catch_Missing_Details_Image_Reconstruction_With_Frequency_Augmented_Variational_Autoencoder_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Catch_Missing_Details_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.02541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Catch_Missing_Details_Image_Reconstruction_With_Frequency_Augmented_Variational_Autoencoder_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Catch_Missing_Details_Image_Reconstruction_With_Frequency_Augmented_Variational_Autoencoder_CVPR_2023_paper.html | CVPR 2023 | null |
RaBit: Parametric Modeling of 3D Biped Cartoon Characters With a Topological-Consistent Dataset | Zhongjin Luo, Shengcai Cai, Jinguo Dong, Ruibo Ming, Liangdong Qiu, Xiaohang Zhan, Xiaoguang Han | Assisting people in efficiently producing visually plausible 3D characters has always been a fundamental research topic in computer vision and computer graphics. Recent learning-based approaches have achieved unprecedented accuracy and efficiency in the area of 3D real human digitization. However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming. In this paper, we introduce 3DBiCar, the first large-scale dataset of 3D biped cartoon characters, and RaBit, the corresponding parametric model. Our dataset contains 1,500 topologically consistent high-quality 3D textured models which are manually crafted by professional artists. Built upon the data, RaBit is thus designed with a SMPL-like linear blend shape model and a StyleGAN-based neural UV-texture generator, simultaneously expressing the shape, pose, and texture. To demonstrate the practicality of 3DBiCar and RaBit, various applications are conducted, including single-view reconstruction, sketch-based modeling, and 3D cartoon animation. For the single-view reconstruction setting, we find a straightforward global mapping from input images to the output UV-based texture maps tends to lose detailed appearances of some local parts (e.g., nose, ears). Thus, a part-sensitive texture reasoner is adopted to make all important local areas perceived. Experiments further demonstrate the effectiveness of our method both qualitatively and quantitatively. 3DBiCar and RaBit are available at gaplab.cuhk.edu.cn/projects/RaBit. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_RaBit_Parametric_Modeling_of_3D_Biped_Cartoon_Characters_With_a_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_RaBit_Parametric_Modeling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12564 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_RaBit_Parametric_Modeling_of_3D_Biped_Cartoon_Characters_With_a_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_RaBit_Parametric_Modeling_of_3D_Biped_Cartoon_Characters_With_a_CVPR_2023_paper.html | CVPR 2023 | null |
Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars | Jingxiang Sun, Xuan Wang, Lizhen Wang, Xiaoyu Li, Yong Zhang, Hongwen Zhang, Yebin Liu | 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Towards fine-grained control over facial attributes, recent efforts incorporate 3D Morphable Face Model (3DMM) to describe deformation in generative radiance fields either explicitly or implicitly. Explicit methods provide fine-grained expression control but cannot handle topological changes caused by hair and accessories, while implicit ones can model varied topologies but have limited generalization caused by the unconstrained deformation fields. We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images. To achieve both deformation accuracy and topological flexibility, we propose a 3D representation called Generative Texture-Rasterized Tri-planes. The proposed representation learns Generative Neural Textures on top of parametric mesh templates and then projects them into three orthogonal-viewed feature planes through rasterization, forming a tri-plane feature representation for volume rendering. In this way, we combine both fine-grained expression control of mesh-guided explicit deformation and the flexibility of implicit volumetric representation. We further propose specific modules for modeling mouth interior which is not taken into account by 3DMM. Our method demonstrates state-of-the-art 3Daware synthesis quality and animation ability through extensive experiments. Furthermore, serving as 3D prior, our animatable 3D representation boosts multiple applications including one-shot facial avatars and 3D-aware stylization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Next3D_Generative_Neural_Texture_Rasterization_for_3D-Aware_Head_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Next3D_Generative_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11208 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Next3D_Generative_Neural_Texture_Rasterization_for_3D-Aware_Head_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Next3D_Generative_Neural_Texture_Rasterization_for_3D-Aware_Head_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
Uni3D: A Unified Baseline for Multi-Dataset 3D Object Detection | Bo Zhang, Jiakang Yuan, Botian Shi, Tao Chen, Yikang Li, Yu Qiao | Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deployed in another dataset. In this paper, we study the task of training a unified 3D detector from multiple datasets. We observe that this appears to be a challenging task, which is mainly due to that these datasets present substantial data-level differences and taxonomy-level variations caused by different LiDAR types and data acquisition standards. Inspired by such observation, we present a Uni3D which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data-level and taxonomy-level differences, respectively. Our method is simple and easily combined with many 3D object detection baselines such as PV-RCNN and Voxel-RCNN, enabling them to effectively learn from multiple off-the-shelf 3D datasets to obtain more discriminative and generalizable representations. Experiments are conducted on many dataset consolidation settings. Their results demonstrate that Uni3D exceeds a series of individual detectors trained on a single dataset, with a 1.04x parameter increase over a selected baseline detector. We expect this work will inspire the research of 3D generalization since it will push the limits of perceptual performance. Our code is available at: https://github.com/PJLab-ADG/3DTrans | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Uni3D_A_Unified_Baseline_for_Multi-Dataset_3D_Object_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.06880 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Uni3D_A_Unified_Baseline_for_Multi-Dataset_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Uni3D_A_Unified_Baseline_for_Multi-Dataset_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Linking Garment With Person via Semantically Associated Landmarks for Virtual Try-On | Keyu Yan, Tingwei Gao, Hui Zhang, Chengjun Xie | In this paper, a novel virtual try-on algorithm, dubbed SAL-VTON, is proposed, which links the garment with the person via semantically associated landmarks to alleviate misalignment. The semantically associated landmarks are a series of landmark pairs with the same local semantics on the in-shop garment image and the try-on image. Based on the semantically associated landmarks, SAL-VTON effectively models the local semantic association between garment and person, making up for the misalignment in the overall deformation of the garment. The outcome is achieved with a three-stage framework: 1) the semantically associated landmarks are estimated using the landmark localization model; 2) taking the landmarks as input, the warping model explicitly associates the corresponding parts of the garment and person for obtaining the local flow, thus refining the alignment in the global flow; 3) finally, a generator consumes the landmarks to better capture local semantics and control the try-on results.Moreover, we propose a new landmark dataset with a unified labelling rule of landmarks for diverse styles of garments. Extensive experimental results on popular datasets demonstrate that SAL-VTON can handle misalignment and outperform state-of-the-art methods both qualitatively and quantitatively. The dataset is available on https://modelscope.cn/datasets/damo/SAL-HG/summary. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_Linking_Garment_With_Person_via_Semantically_Associated_Landmarks_for_Virtual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_Linking_Garment_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Linking_Garment_With_Person_via_Semantically_Associated_Landmarks_for_Virtual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Linking_Garment_With_Person_via_Semantically_Associated_Landmarks_for_Virtual_CVPR_2023_paper.html | CVPR 2023 | null |
ACR: Attention Collaboration-Based Regressor for Arbitrary Two-Hand Reconstruction | Zhengdi Yu, Shaoli Huang, Chen Fang, Toby P. Breckon, Jue Wang | Reconstructing two hands from monocular RGB images is challenging due to frequent occlusion and mutual confusion. Existing methods mainly learn an entangled representation to encode two interacting hands, which are incredibly fragile to impaired interaction, such as truncated hands, separate hands, or external occlusion. This paper presents ACR (Attention Collaboration-based Regressor), which makes the first attempt to reconstruct hands in arbitrary scenarios. To achieve this, ACR explicitly mitigates interdependencies between hands and between parts by leveraging center and part-based attention for feature extraction. However, reducing interdependence helps release the input constraint while weakening the mutual reasoning about reconstructing the interacting hands. Thus, based on center attention, ACR also learns cross-hand prior that handle the interacting hands better. We evaluate our method on various types of hand reconstruction datasets. Our method significantly outperforms the best interacting-hand approaches on the InterHand2.6M dataset while yielding comparable performance with the state-of-the-art single-hand methods on the FreiHand dataset. More qualitative results on in-the-wild and hand-object interaction datasets and web images/videos further demonstrate the effectiveness of our approach for arbitrary hand reconstruction. Our code is available at https://github.com/ZhengdiYu/Arbitrary-Hands-3D-Reconstruction | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_ACR_Attention_Collaboration-Based_Regressor_for_Arbitrary_Two-Hand_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_ACR_Attention_Collaboration-Based_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.05938 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_ACR_Attention_Collaboration-Based_Regressor_for_Arbitrary_Two-Hand_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_ACR_Attention_Collaboration-Based_Regressor_for_Arbitrary_Two-Hand_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Rotation-Invariant Transformer for Point Cloud Matching | Hao Yu, Zheng Qin, Ji Hou, Mahdi Saleh, Dongsheng Li, Benjamin Busam, Slobodan Ilic | The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rotation invariance extrinsically via data augmentation. As the finite number of augmented rotations can never span the continuous SO(3) space, these methods usually show instability when facing rotations that are rarely seen. To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task. We contribute both on the local and global levels. Starting from the local level, we introduce an attention mechanism embedded with Point Pair Feature (PPF)-based coordinates to describe the pose-invariant geometry, upon which a novel attention-based encoder-decoder architecture is constructed. We further propose a global transformer with rotation-invariant cross-frame spatial awareness learned by the self-attention mechanism, which significantly improves the feature distinctiveness and makes the model robust with respect to the low overlap. Experiments are conducted on both the rigid and non-rigid public benchmarks, where RoITr outperforms all the state-of-the-art models by a considerable margin in the low-overlapping scenarios. Especially when the rotations are enlarged on the challenging 3DLoMatch benchmark, RoITr surpasses the existing methods by at least 13 and 5 percentage points in terms of Inlier Ratio and Registration Recall, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Rotation-Invariant_Transformer_for_Point_Cloud_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Rotation-Invariant_Transformer_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08231 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Rotation-Invariant_Transformer_for_Point_Cloud_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Rotation-Invariant_Transformer_for_Point_Cloud_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation | Deunsol Jung, Sanghyun Kim, Won Hwa Kim, Minsu Cho | Scene graph generation aims to construct a semantic graph structure from an image such that its nodes and edges respectively represent objects and their relationships. One of the major challenges for the task lies in the presence of distracting objects and relationships in images; contextual reasoning is strongly distracted by irrelevant objects or backgrounds and, more importantly, a vast number of irrelevant candidate relations. To tackle the issue, we propose the Selective Quad Attention Network (SQUAT) that learns to select relevant object pairs and disambiguate them via diverse contextual interactions. SQUAT consists of two main components: edge selection and quad attention. The edge selection module selects relevant object pairs, i.e., edges in the scene graph, which helps contextual reasoning, and the quad attention module then updates the edge features using both edge-to-node and edge-to-edge cross-attentions to capture contextual information between objects and object pairs. Experiments demonstrate the strong performance and robustness of SQUAT, achieving the state of the art on the Visual Genome and Open Images v6 benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jung_Devils_on_the_Edges_Selective_Quad_Attention_for_Scene_Graph_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jung_Devils_on_the_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_Devils_on_the_Edges_Selective_Quad_Attention_for_Scene_Graph_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_Devils_on_the_Edges_Selective_Quad_Attention_for_Scene_Graph_CVPR_2023_paper.html | CVPR 2023 | null |
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging | Karim Guirguis, Johannes Meier, George Eskandar, Matthias Kayser, Bin Yang, Jürgen Beyerer | Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neural networks. A promise of Generalized Few-shot Object Detection (G-FSOD), a learning paradigm in AI, is to alleviate the need for collecting abundant training samples of novel classes we wish to detect by leveraging prior knowledge from old classes (i.e., base classes). G-FSOD strives to learn these novel classes while alleviating catastrophic forgetting of the base classes. However, existing approaches assume that the base images are accessible, an assumption that does not hold when sharing and storing data is problematic. In this work, we propose the first data-free knowledge distillation (DFKD) approach for G-FSOD that leverages the statistics of the region of interest (RoI) features from the base model to forge instance-level features without accessing the base images. Our contribution is three-fold: (1) we design a standalone lightweight generator with (2) class-wise heads (3) to generate and replay diverse instance-level base features to the RoI head while finetuning on the novel data. This stands in contrast to standard DFKD approaches in image classification, which invert the entire network to generate base images. Moreover, we make careful design choices in the novel finetuning pipeline to regularize the model. We show that our approach can dramatically reduce the base memory requirements, all while setting a new standard for G-FSOD on the challenging MS-COCO and PASCAL-VOC benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guirguis_NIFF_Alleviating_Forgetting_in_Generalized_Few-Shot_Object_Detection_via_Neural_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guirguis_NIFF_Alleviating_Forgetting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.04958 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guirguis_NIFF_Alleviating_Forgetting_in_Generalized_Few-Shot_Object_Detection_via_Neural_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guirguis_NIFF_Alleviating_Forgetting_in_Generalized_Few-Shot_Object_Detection_via_Neural_CVPR_2023_paper.html | CVPR 2023 | null |
Habitat-Matterport 3D Semantics Dataset | Karmesh Yadav, Ram Ramrakhya, Santhosh Kumar Ramakrishnan, Theo Gervet, John Turner, Aaron Gokaslan, Noah Maestre, Angel Xuan Chang, Dhruv Batra, Manolis Savva, Alexander William Clegg, Devendra Singh Chaplot | We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of object annotations far exceed those of prior datasets. A key difference setting apart HM3DSEM from other datasets is the use of texture information to annotate pixel-accurate object boundaries. We demonstrate the effectiveness of HM3DSEM dataset for the Object Goal Navigation task using different methods. Policies trained using HM3DSEM perform outperform those trained on prior datasets. Introduction of HM3DSEM in the Habitat ObjectNav Challenge lead to an increase in participation from 400 submissions in 2021 to 1022 submissions in 2022. Project page: https://aihabitat.org/datasets/hm3d-semantics/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Yadav_Habitat-Matterport_3D_Semantics_Dataset_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yadav_Habitat-Matterport_3D_Semantics_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2210.05633 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yadav_Habitat-Matterport_3D_Semantics_Dataset_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yadav_Habitat-Matterport_3D_Semantics_Dataset_CVPR_2023_paper.html | CVPR 2023 | null |
Post-Processing Temporal Action Detection | Sauradip Nag, Xiatian Zhu, Yi-Zhe Song, Tao Xiang | Existing Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequence, before temporal boundary estimation and action classification. This pre-processing step would temporally downsample the video, reducing the inference resolution and hampering the detection performance in the original temporal resolution. In essence, this is due to a temporal quantization error introduced during the resolution downsampling and recovery. This could negatively impact the TAD performance, but is largely ignored by existing methods. To address this problem, in this work we introduce a novel model-agnostic post-processing method without model redesign and retraining. Specifically, we model the start and end points of action instances with a Gaussian distribution for enabling temporal boundary inference at a sub-snippet level. We further introduce an efficient Taylor-expansion based approximation, dubbed as Gaussian Approximated Post-processing (GAP). Extensive experiments demonstrate that our GAP can consistently improve a wide variety of pre-trained off-the-shelf TAD models on the challenging ActivityNet (+0.2% 0.7% in average mAP) and THUMOS (+0.2% 0.5% in average mAP) benchmarks. Such performance gains are already significant and highly comparable to those achieved by novel model designs. Also, GAP can be integrated with model training for further performance gain. Importantly, GAP enables lower temporal resolutions for more efficient inference, facilitating low-resource applications. The code is available in https://github.com/sauradip/GAP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nag_Post-Processing_Temporal_Action_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nag_Post-Processing_Temporal_Action_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14924 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nag_Post-Processing_Temporal_Action_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nag_Post-Processing_Temporal_Action_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
ConZIC: Controllable Zero-Shot Image Captioning by Sampling-Based Polishing | Zequn Zeng, Hao Zhang, Ruiying Lu, Dongsheng Wang, Bo Chen, Zhengjue Wang | Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searching every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named GibbsBERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about 5x faster generation speed than ZeroCap, and about 1.5x higher diversity scores, with accurate generation given different control signals. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_ConZIC_Controllable_Zero-Shot_Image_Captioning_by_Sampling-Based_Polishing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_ConZIC_Controllable_Zero-Shot_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_ConZIC_Controllable_Zero-Shot_Image_Captioning_by_Sampling-Based_Polishing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_ConZIC_Controllable_Zero-Shot_Image_Captioning_by_Sampling-Based_Polishing_CVPR_2023_paper.html | CVPR 2023 | null |
EDGE: Editable Dance Generation From Music | Jonathan Tseng, Rodrigo Castellon, Karen Liu | Dance is an important human art form, but creating new dances can be difficult and time-consuming. In this work, we introduce Editable Dance GEneration (EDGE), a state-of-the-art method for editable dance generation that is capable of creating realistic, physically-plausible dances while remaining faithful to the input music. EDGE uses a transformer-based diffusion model paired with Jukebox, a strong music feature extractor, and confers powerful editing capabilities well-suited to dance, including joint-wise conditioning, and in-betweening. We introduce a new metric for physical plausibility, and evaluate dance quality generated by our method extensively through (1) multiple quantitative metrics on physical plausibility, alignment, and diversity benchmarks, and more importantly, (2) a large-scale user study, demonstrating a significant improvement over previous state-of-the-art methods. Qualitative samples from our model can be found at our website. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tseng_EDGE_Editable_Dance_Generation_From_Music_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tseng_EDGE_Editable_Dance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10658 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tseng_EDGE_Editable_Dance_Generation_From_Music_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tseng_EDGE_Editable_Dance_Generation_From_Music_CVPR_2023_paper.html | CVPR 2023 | null |
Curricular Contrastive Regularization for Physics-Aware Single Image Dehazing | Yu Zheng, Jiahui Zhan, Shengfeng He, Junyu Dong, Yong Du | Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are nonconsensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy image, and 2) corresponding restorations by other existing methods. Further, due to the different similarities between the embeddings of the clear image and negatives, the learning difficulty of the multiple components is intrinsically imbalanced. To tackle this issue, we customize a curriculum learning strategy to reweight the importance of different negatives. In addition, to improve the interpretability in the feature space, we build a physics-aware dual-branch unit according to the atmospheric scattering model. With the unit, as well as curricular contrastive regularization, we establish our dehazing network, named C2PNet. Extensive experiments demonstrate that our C2PNet significantly outperforms state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB, respectively, on SOTS-indoor and SOTS-outdoor datasets. Code is available at https://github.com/YuZheng9/C2PNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_Curricular_Contrastive_Regularization_for_Physics-Aware_Single_Image_Dehazing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_Curricular_Contrastive_Regularization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14218 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Curricular_Contrastive_Regularization_for_Physics-Aware_Single_Image_Dehazing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Curricular_Contrastive_Regularization_for_Physics-Aware_Single_Image_Dehazing_CVPR_2023_paper.html | CVPR 2023 | null |
Learning From Noisy Labels With Decoupled Meta Label Purifier | Yuanpeng Tu, Boshen Zhang, Yuxi Li, Liang Liu, Jian Li, Yabiao Wang, Chengjie Wang, Cai Rong Zhao | Training deep neural networks (DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-learning based label correction strategy is widely adopted to tackle this problem via identifying and correcting potential noisy labels with the help of a small set of clean validation data. Although training with purified labels can effectively improve performance, solving the meta-learning problem inevitably involves a nested loop of bi-level optimization between model weights and hyperparameters (i.e., label distribution). As compromise, previous methods resort toa coupled learning process with alternating update. In this paper, we empirically find such simultaneous optimization over both model weights and label distribution can not achieve an optimal routine, consequently limiting the representation ability of backbone and accuracy of corrected labels. From this observation, a novel multi-stage label purifier named DMLP is proposed. DMLP decouples the label correction process into label-free representation learning and a simple meta label purifier, In this way, DMLP can focus on extracting discriminative feature and label correction in two distinctive stages. DMLP is a plug-and-play label purifier, the purified labels can be directly reused in naive end-to-end network retraining or other robust learning methods, where state-of-the-art results are obtained on several synthetic and real-world noisy datasets, especially under high noise levels. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tu_Learning_From_Noisy_Labels_With_Decoupled_Meta_Label_Purifier_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tu_Learning_From_Noisy_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.06810 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Learning_From_Noisy_Labels_With_Decoupled_Meta_Label_Purifier_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Learning_From_Noisy_Labels_With_Decoupled_Meta_Label_Purifier_CVPR_2023_paper.html | CVPR 2023 | null |
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification | Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, Mark Yatskar | Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-stakes applications. CBMs require manually specified concepts and often under-perform their black box counterparts, preventing their broad adoption. We address these shortcomings and are first to show how to construct high-performance CBMs without manual specification of similar accuracy to black box models. Our approach, Language Guided Bottlenecks (LaBo), leverages a language model, GPT-3, to define a large space of possible bottlenecks. Given a problem domain, LaBo uses GPT-3 to produce factual sentences about categories to form candidate concepts. LaBo efficiently searches possible bottlenecks through a novel submodular utility that promotes the selection of discriminative and diverse information. Ultimately, GPT-3's sentential concepts can be aligned to images using CLIP, to form a bottleneck layer. Experiments demonstrate that LaBo is a highly effective prior for concepts important to visual recognition. In the evaluation with 11 diverse datasets, LaBo bottlenecks excel at few-shot classification: they are 11.7% more accurate than black box linear probes at 1 shot and comparable with more data. Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Language_in_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11158 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.html | CVPR 2023 | null |
Sharpness-Aware Gradient Matching for Domain Generalization | Pengfei Wang, Zhaoxiang Zhang, Zhen Lei, Lei Zhang | The goal of domain generalization (DG) is to enhance the generalization capability of the model learned from a source domain to other unseen domains. The recently developed Sharpness-Aware Minimization (SAM) method aims to achieve this goal by minimizing the sharpness measure of the loss landscape. Though SAM and its variants have demonstrated impressive DG performance, they may not always converge to the desired flat region with a small loss value. In this paper, we present two conditions to ensure that the model could converge to a flat minimum with a small loss, and present an algorithm, named Sharpness-Aware Gradient Matching (SAGM), to meet the two conditions for improving model generalization capability. Specifically, the optimization objective of SAGM will simultaneously minimize the empirical risk, the perturbed loss (i.e., the maximum loss within a neighborhood in the parameter space), and the gap between them. By implicitly aligning the gradient directions between the empirical risk and the perturbed loss, SAGM improves the generalization capability over SAM and its variants without increasing the computational cost. Extensive experimental results show that our proposed SAGM method consistently outperforms the state-of-the-art methods on five DG benchmarks, including PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet. Codes are available at https://github.com/Wang-pengfei/SAGM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Sharpness-Aware_Gradient_Matching_for_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Sharpness-Aware_Gradient_Matching_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10353 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Sharpness-Aware_Gradient_Matching_for_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Sharpness-Aware_Gradient_Matching_for_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
ViPLO: Vision Transformer Based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection | Jeeseung Park, Jin-Woo Park, Jong-Seok Lee | Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to the old backbone networks and the lack of considerations for the HOI perception process of humans in the interaction classifiers. In this paper, we propose Vision Transformer based Pose-Conditioned Self-Loop Graph (ViPLO) to resolve these problems. First, we propose a novel feature extraction method suitable for the Vision Transformer backbone, called masking with overlapped area (MOA) module. The MOA module utilizes the overlapped area between each patch and the given region in the attention function, which addresses the quantization problem when using the Vision Transformer backbone. In addition, we design a graph with a pose-conditioned self-loop structure, which updates the human node encoding with local features of human joints. This allows the classifier to focus on specific human joints to effectively identify the type of interaction, which is motivated by the human perception process for HOI. As a result, ViPLO achieves the state-of-the-art results on two public benchmarks, especially obtaining a +2.07 mAP performance gain on the HICO-DET dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_ViPLO_Vision_Transformer_Based_Pose-Conditioned_Self-Loop_Graph_for_Human-Object_Interaction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_ViPLO_Vision_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08114 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_ViPLO_Vision_Transformer_Based_Pose-Conditioned_Self-Loop_Graph_for_Human-Object_Interaction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_ViPLO_Vision_Transformer_Based_Pose-Conditioned_Self-Loop_Graph_for_Human-Object_Interaction_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Table Structure Recognition With Visual-Alignment Sequential Coordinate Modeling | Yongshuai Huang, Ning Lu, Dapeng Chen, Yibo Li, Zecheng Xie, Shenggao Zhu, Liangcai Gao, Wei Peng | Table structure recognition aims to extract the logical and physical structure of unstructured table images into a machine-readable format. The latest end-to-end image-to-text approaches simultaneously predict the two structures by two decoders, where the prediction of the physical structure (the bounding boxes of the cells) is based on the representation of the logical structure. However, as the logical representation lacks the local visual information, the previous methods often produce imprecise bounding boxes. To address this issue, we propose an end-to-end sequential modeling framework for table structure recognition called VAST. It contains a novel coordinate sequence decoder triggered by the representation of the non-empty cell from the logical structure decoder. In the coordinate sequence decoder, we model the bounding box coordinates as a language sequence, where the left, top, right and bottom coordinates are decoded sequentially to leverage the inter-coordinate dependency. Furthermore, we propose an auxiliary visual-alignment loss to enforce the logical representation of the non-empty cells to contain more local visual details, which helps produce better cell bounding boxes. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art results in both logical and physical structure recognition. The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Improving_Table_Structure_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06949 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID | Jianyang Gu, Kai Wang, Hao Luo, Chen Chen, Wei Jiang, Yuqiang Fang, Shanghang Zhang, Yang You, Jian Zhao | Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance. Previous works explore new optimizing targets and search spaces for NAS ReID, yet they neglect the difference of training schemes between image classification and ReID. In this work, we propose a novel Twins Contrastive Mechanism (TCM) to provide more appropriate supervision for ReID architecture search. TCM reduces the category overlaps between the training and validation data, and assists NAS in simulating real-world ReID training schemes. We then design a Multi-Scale Interaction (MSI) search space to search for rational interaction operations between multi-scale features. In addition, we introduce a Spatial Alignment Module (SAM) to further enhance the attention consistency confronted with images from different sources. Under the proposed NAS scheme, a specific architecture is automatically searched, named as MSINet. Extensive experiments demonstrate that our method surpasses state-of-the-art ReID methods on both in-domain and cross-domain scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gu_MSINet_Twins_Contrastive_Search_of_Multi-Scale_Interaction_for_Object_ReID_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gu_MSINet_Twins_Contrastive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07065 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_MSINet_Twins_Contrastive_Search_of_Multi-Scale_Interaction_for_Object_ReID_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_MSINet_Twins_Contrastive_Search_of_Multi-Scale_Interaction_for_Object_ReID_CVPR_2023_paper.html | CVPR 2023 | null |
WIRE: Wavelet Implicit Neural Representations | Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk | Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of activation function employed in its MLP network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs designed to have high accuracy also suffer from poor robustness (to signal noise, parameter variation, etc.). Inspired by harmonic analysis, we develop a new, highly accurate and robust INR that does not exhibit this tradeoff. Our Wavelet Implicit neural REpresentation (WIRE) uses as its activation function the complex Gabor wavelet that is well-known to be optimally concentrated in space--frequency and to have excellent biases for representing images. A wide range of experiments (image denoising, image inpainting, super-resolution, computed tomography reconstruction, image overfitting, and novel view synthesis with neural radiance fields) demonstrate that WIRE defines the new state of the art in INR accuracy, training time, and robustness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Saragadam_WIRE_Wavelet_Implicit_Neural_Representations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Saragadam_WIRE_Wavelet_Implicit_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05187 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Saragadam_WIRE_Wavelet_Implicit_Neural_Representations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Saragadam_WIRE_Wavelet_Implicit_Neural_Representations_CVPR_2023_paper.html | CVPR 2023 | null |
Bi-Directional Feature Fusion Generative Adversarial Network for Ultra-High Resolution Pathological Image Virtual Re-Staining | Kexin Sun, Zhineng Chen, Gongwei Wang, Jun Liu, Xiongjun Ye, Yu-Gang Jiang | The cost of pathological examination makes virtual re-staining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual re-staining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple post-processing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bi-directional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the inter-patch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Bi-Directional_Feature_Fusion_Generative_Adversarial_Network_for_Ultra-High_Resolution_Pathological_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Bi-Directional_Feature_Fusion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Bi-Directional_Feature_Fusion_Generative_Adversarial_Network_for_Ultra-High_Resolution_Pathological_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Bi-Directional_Feature_Fusion_Generative_Adversarial_Network_for_Ultra-High_Resolution_Pathological_CVPR_2023_paper.html | CVPR 2023 | null |
HumanGen: Generating Human Radiance Fields With Explicit Priors | Suyi Jiang, Haoran Jiang, Ziyu Wang, Haimin Luo, Wenzheng Chen, Lan Xu | Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of human radiance fields remains challenging, partially due to the limited human-related priors adopted in existing methods. We present HumanGen, a novel 3D human generation scheme with detailed geometry and 360deg realistic free-view rendering. It explicitly marries the 3D human generation with various priors from the 2D generator and 3D reconstructor of humans through the design of "anchor image". We introduce a hybrid feature representation using the anchor image to bridge the latent space of HumanGen with the existing 2D generator. We then adopt a pronged design to disentangle the generation of geometry and appearance. With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation. Extensive experiments demonstrate our effectiveness for state-of-the-art 3D human generation regarding geometry details, texture quality, and free-view performance. Notably, HumanGen can also incorporate various off-the-shelf 2D latent editing methods, seamlessly lifting them into 3D. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_HumanGen_Generating_Human_Radiance_Fields_With_Explicit_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_HumanGen_Generating_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05321 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_HumanGen_Generating_Human_Radiance_Fields_With_Explicit_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_HumanGen_Generating_Human_Radiance_Fields_With_Explicit_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
Bringing Inputs to Shared Domains for 3D Interacting Hands Recovery in the Wild | Gyeongsik Moon | Despite recent achievements, existing 3D interacting hands recovery methods have shown results mainly on motion capture (MoCap) environments, not on in-the-wild (ITW) ones. This is because collecting 3D interacting hands data in the wild is extremely challenging, even for the 2D data. We present InterWild, which brings MoCap and ITW samples to shared domains for robust 3D interacting hands recovery in the wild with a limited amount of ITW 2D/3D interacting hands data. 3D interacting hands recovery consists of two sub-problems: 1) 3D recovery of each hand and 2) 3D relative translation recovery between two hands. For the first sub-problem, we bring MoCap and ITW samples to a shared 2D scale space. Although ITW datasets provide a limited amount of 2D/3D interacting hands, they contain large-scale 2D single hand data. Motivated by this, we use a single hand image as an input for the first sub-problem regardless of whether two hands are interacting. Hence, interacting hands of MoCap datasets are brought to the 2D scale space of single hands of ITW datasets. For the second sub-problem, we bring MoCap and ITW samples to a shared appearance-invariant space. Unlike the first sub-problem, 2D labels of ITW datasets are not helpful for the second sub-problem due to the 3D translation's ambiguity. Hence, instead of relying on ITW samples, we amplify the generalizability of MoCap samples by taking only a geometric feature without an image as an input for the second sub-problem. As the geometric feature is invariant to appearances, MoCap and ITW samples do not suffer from a huge appearance gap between the two datasets. The code is available in https://github.com/facebookresearch/InterWild. | https://openaccess.thecvf.com/content/CVPR2023/papers/Moon_Bringing_Inputs_to_Shared_Domains_for_3D_Interacting_Hands_Recovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Moon_Bringing_Inputs_to_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Moon_Bringing_Inputs_to_Shared_Domains_for_3D_Interacting_Hands_Recovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Moon_Bringing_Inputs_to_Shared_Domains_for_3D_Interacting_Hands_Recovery_CVPR_2023_paper.html | CVPR 2023 | null |
Local Connectivity-Based Density Estimation for Face Clustering | Junho Shin, Hyo-Jun Lee, Hyunseop Kim, Jong-Hyeon Baek, Daehyun Kim, Yeong Jun Koh | Recent graph-based face clustering methods predict the connectivity of enormous edges, including false positive edges that link nodes with different classes. However, those false positive edges, which connect negative node pairs, have the risk of integration of different clusters when their connectivity is incorrectly estimated. This paper proposes a novel face clustering method to address this problem. The proposed clustering method employs density-based clustering, which maintains edges that have higher density. For this purpose, we propose a reliable density estimation algorithm based on local connectivity between K nearest neighbors (KNN). We effectively exclude negative pairs from the KNN graph based on the reliable density while maintaining sufficient positive pairs. Furthermore, we develop a pairwise connectivity estimation network to predict the connectivity of the selected edges. Experimental results demonstrate that the proposed clustering method significantly outperforms the state-of-the-art clustering methods on large-scale face clustering datasets and fashion image clustering datasets. Our code is available at https://github.com/illian01/LCE-PCENet | https://openaccess.thecvf.com/content/CVPR2023/papers/Shin_Local_Connectivity-Based_Density_Estimation_for_Face_Clustering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shin_Local_Connectivity-Based_Density_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_Local_Connectivity-Based_Density_Estimation_for_Face_Clustering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_Local_Connectivity-Based_Density_Estimation_for_Face_Clustering_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Zone-Aware Hierarchical Planner for Vision-Language Navigation | Chen Gao, Xingyu Peng, Mi Yan, He Wang, Lirong Yang, Haibing Ren, Hongsheng Li, Si Liu | The task of Vision-Language Navigation (VLN) is for an embodied agent to reach the global goal according to the instruction. Essentially, during navigation, a series of sub-goals need to be adaptively set and achieved, which is naturally a hierarchical navigation process. However, previous methods leverage a single-step planning scheme, i.e., directly performing navigation action at each step, which is unsuitable for such a hierarchical navigation process. In this paper, we propose an Adaptive Zone-aware Hierarchical Planner (AZHP) to explicitly divides the navigation process into two heterogeneous phases, i.e., sub-goal setting via zone partition/selection (high-level action) and sub-goal executing (low-level action), for hierarchical planning. Specifically, AZHP asynchronously performs two levels of action via the designed State-Switcher Module (SSM). For high-level action, we devise a Scene-aware adaptive Zone Partition (SZP) method to adaptively divide the whole navigation area into different zones on-the-fly. Then the Goal-oriented Zone Selection (GZS) method is proposed to select a proper zone for the current sub-goal. For low-level action, the agent conducts navigation-decision multi-steps in the selected zone. Moreover, we design a Hierarchical RL (HRL) strategy and auxiliary losses with curriculum learning to train the AZHP framework, which provides effective supervision signals for each stage. Extensive experiments demonstrate the superiority of our proposed method, which achieves state-of-the-art performance on three VLN benchmarks (REVERIE, SOON, R2R). | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Adaptive_Zone-Aware_Hierarchical_Planner_for_Vision-Language_Navigation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Adaptive_Zone-Aware_Hierarchical_Planner_for_Vision-Language_Navigation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Adaptive_Zone-Aware_Hierarchical_Planner_for_Vision-Language_Navigation_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Practical Plug-and-Play Diffusion Models | Hyojun Go, Yunsung Lee, Jin-Young Kim, Seunghyun Lee, Myeongho Jeong, Hyun Seung Lee, Seungtaek Choi | Diffusion-based generative models have achieved remarkable success in image generation. Their guidance formulation allows an external model to plug-and-play control the generation process for various tasks without fine-tuning the diffusion model. However, the direct use of publicly available off-the-shelf models for guidance fails due to their poor performance on noisy inputs. For that, the existing practice is to fine-tune the guidance models with labeled data corrupted with noises. In this paper, we argue that this practice has limitations in two aspects: (1) performing on inputs with extremely various noises is too hard for a single guidance model; (2) collecting labeled datasets hinders scaling up for various tasks. To tackle the limitations, we propose a novel strategy that leverages multiple experts where each expert is specialized in a particular noise range and guides the reverse process of the diffusion at its corresponding timesteps. However, as it is infeasible to manage multiple networks and utilize labeled data, we present a practical guidance framework termed Practical Plug-And-Play (PPAP), which leverages parameter-efficient fine-tuning and data-free knowledge transfer. We exhaustively conduct ImageNet class conditional generation experiments to show that our method can successfully guide diffusion with small trainable parameters and no labeled data. Finally, we show that image classifiers, depth estimators, and semantic segmentation models can guide publicly available GLIDE through our framework in a plug-and-play manner. Our code is available at https://github.com/riiid/PPAP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Go_Towards_Practical_Plug-and-Play_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Go_Towards_Practical_Plug-and-Play_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05973 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Go_Towards_Practical_Plug-and-Play_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Go_Towards_Practical_Plug-and-Play_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis | Jin Lin, Xiaotong Luo, Ming Hong, Yanyun Qu, Yuan Xie, Zongze Wu | Scalable deep Super-Resolution (SR) models are increasingly in demand, whose memory can be customized and tuned to the computational recourse of the platform. The existing dynamic scalable SR methods are not memory-friendly enough because multi-scale models have to be saved with a fixed size for each model. Inspired by the success of Lottery Tickets Hypothesis (LTH) on image classification, we explore the existence of unstructured scalable SR deep models, that is, we find gradual shrinkage sub-networks of extreme sparsity named winning tickets. In this paper, we propose a Memory-friendly Scalable SR framework (MSSR). The advantage is that only a single scalable model covers multiple SR models with different sizes, instead of reloading SR models of different sizes. Concretely, MSSR consists of the forward and backward stages, the former for model compression and the latter for model expansion. In the forward stage, we take advantage of LTH with rewinding weights to progressively shrink the SR model and the pruning-out masks that form nested sets. Moreover, stochastic self-distillation (SSD) is conducted to boost the performance of sub-networks. By stochastically selecting multiple depths, the current model inputs the selected features into the corresponding parts in the larger model and improves the performance of the current model based on the feedback results of the larger model. In the backward stage, the smaller SR model could be expanded by recovering and fine-tuning the pruned parameters according to the pruning-out masks obtained in the forward. Extensive experiments show the effectiveness of MMSR. The smallest-scale sub-network could achieve the sparsity of 94% and outperforms the compared lightweight SR methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Memory-Friendly_Scalable_Super-Resolution_via_Rewinding_Lottery_Ticket_Hypothesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Memory-Friendly_Scalable_Super-Resolution_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Memory-Friendly_Scalable_Super-Resolution_via_Rewinding_Lottery_Ticket_Hypothesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Memory-Friendly_Scalable_Super-Resolution_via_Rewinding_Lottery_Ticket_Hypothesis_CVPR_2023_paper.html | CVPR 2023 | null |
YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors | Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao | Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the-art methods. To address the topics, we propose a trainable bag-of-freebies oriented solution. We combine the flexible and efficient training tools with the proposed architecture and the compound scaling method. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 120 FPS and has the highest accuracy 56.8% AP among all known realtime object detectors with 30 FPS or higher on GPU V100. Source code is released in https://github.com/ WongKinYiu/yolov7. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_YOLOv7_Trainable_Bag-of-Freebies_Sets_New_State-of-the-Art_for_Real-Time_Object_Detectors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_YOLOv7_Trainable_Bag-of-Freebies_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.02696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_YOLOv7_Trainable_Bag-of-Freebies_Sets_New_State-of-the-Art_for_Real-Time_Object_Detectors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_YOLOv7_Trainable_Bag-of-Freebies_Sets_New_State-of-the-Art_for_Real-Time_Object_Detectors_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Deterministic Uncertainty: A New Simple Baseline | Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H.S. Torr, Yarin Gal | Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass uncertainty approaches, DUQ and SNGP, and examine whether they mainly rely on a well-regularized feature space. Crucially, without using their more complex methods for estimating uncertainty, we find that a single softmax neural net with such a regularized feature-space, achieved via residual connections and spectral normalization, outperforms DUQ and SNGP's epistemic uncertainty predictions using simple Gaussian Discriminant Analysis post-training as a separate feature-space density estimator---without fine-tuning on OoD data, feature ensembling, or input pre-procressing. Our conceptually simple Deep Deterministic Uncertainty (DDU) baseline can also be used to disentangle aleatoric and epistemic uncertainty and performs as well as Deep Ensembles, the state-of-the art for uncertainty prediction, on several OoD benchmarks (CIFAR-10/100 vs SVHN/Tiny-ImageNet, ImageNet vs ImageNet-O), active learning settings across different model architectures, as well as in large scale vision tasks like semantic segmentation, while being computationally cheaper. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mukhoti_Deep_Deterministic_Uncertainty_A_New_Simple_Baseline_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mukhoti_Deep_Deterministic_Uncertainty_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mukhoti_Deep_Deterministic_Uncertainty_A_New_Simple_Baseline_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mukhoti_Deep_Deterministic_Uncertainty_A_New_Simple_Baseline_CVPR_2023_paper.html | CVPR 2023 | null |
PartDistillation: Learning Parts From Instance Segmentation | Jang Hyun Cho, Philipp Krähenbühl, Vignesh Ramanathan | We present a scalable framework to learn part segmentation from object instance labels. State-of-the-art instance segmentation models contain a surprising amount of part information. However, much of this information is hidden from plain view. For each object instance, the part information is noisy, inconsistent, and incomplete. PartDistillation transfers the part information of an instance segmentation model into a part segmentation model through self-supervised self-training on a large dataset. The resulting segmentation model is robust, accurate, and generalizes well. We evaluate the model on various part segmentation datasets. Our model outperforms supervised part segmentation in zero-shot generalization performance by a large margin. Our model outperforms when finetuned on target datasets compared to supervised counterpart and other baselines especially in few-shot regime. Finally, our model provides a wider coverage of rare parts when evaluated over 10K object classes. Code is at https://github.com/facebookresearch/PartDistillation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_PartDistillation_Learning_Parts_From_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cho_PartDistillation_Learning_Parts_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_PartDistillation_Learning_Parts_From_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_PartDistillation_Learning_Parts_From_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Video Object Segmentation via Space-Time Correspondence Learning | Yurong Zhang, Liulei Li, Wenguan Wang, Rong Xie, Li Song, Wenjun Zhang | Current top-leading solutions for video object segmentation (VOS) typically follow a matching-based regime: for each query frame, the segmentation mask is inferred according to its correspondence to previously processed and the first annotated frames. They simply exploit the supervisory signals from the groundtruth masks for learning mask prediction only, without posing any constraint on the space-time correspondence matching, which, however, is the fundamental building block of such regime. To alleviate this crucial yet commonly ignored issue, we devise a correspondence-aware training framework, which boosts matching-based VOS solutions by explicitly encouraging robust correspondence matching during network learning. Through comprehensively exploring the intrinsic coherence in videos on pixel and object levels, our algorithm reinforces the standard, fully supervised training of mask segmentation with label-free, contrastive correspondence learning. Without neither requiring extra annotation cost during training, nor causing speed delay during deployment, nor incurring architectural modification, our algorithm provides solid performance gains on four widely used benchmarks, i.e., DAVIS2016&2017, and YouTube-VOS2018&2019, on the top of famous matching-based VOS solutions. Our implementation will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Boosting_Video_Object_Segmentation_via_Space-Time_Correspondence_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Boosting_Video_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Boosting_Video_Object_Segmentation_via_Space-Time_Correspondence_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Boosting_Video_Object_Segmentation_via_Space-Time_Correspondence_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency Is All You Need | Tong Wei, Kai Gan | While long-tailed semi-supervised learning (LTSSL) has received tremendous attention in many real-world classification problems, existing LTSSL algorithms typically assume that the class distributions of labeled and unlabeled data are almost identical. Those LTSSL algorithms built upon the assumption can severely suffer when the class distributions of labeled and unlabeled data are mismatched since they utilize biased pseudo-labels from the model. To alleviate this issue, we propose a new simple method that can effectively utilize unlabeled data of unknown class distributions by introducing the adaptive consistency regularizer (ACR). ACR realizes the dynamic refinery of pseudo-labels for various distributions in a unified formula by estimating the true class distribution of unlabeled data. Despite its simplicity, we show that ACR achieves state-of-the-art performance on a variety of standard LTSSL benchmarks, e.g., an averaged 10% absolute increase of test accuracy against existing algorithms when the class distributions of labeled and unlabeled data are mismatched. Even when the class distributions are identical, ACR consistently outperforms many sophisticated LTSSL algorithms. We carry out extensive ablation studies to tease apart the factors that are most important to ACR's success. Source code is available at https://github.com/Gank0078/ACR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Towards_Realistic_Long-Tailed_Semi-Supervised_Learning_Consistency_Is_All_You_Need_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Towards_Realistic_Long-Tailed_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Towards_Realistic_Long-Tailed_Semi-Supervised_Learning_Consistency_Is_All_You_Need_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Towards_Realistic_Long-Tailed_Semi-Supervised_Learning_Consistency_Is_All_You_Need_CVPR_2023_paper.html | CVPR 2023 | null |
GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts | Haoran Geng, Helin Xu, Chengyang Zhao, Chao Xu, Li Yi, Siyuan Huang, He Wang | For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world. | https://openaccess.thecvf.com/content/CVPR2023/papers/Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Geng_GAPartNet_Cross-Category_Domain-Generalizable_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.05272 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023_paper.html | CVPR 2023 | null |
NeRDi: Single-View NeRF Synthesis With Language-Guided Diffusion As General Image Priors | Congyue Deng, Chiyu “Max” Jiang, Charles R. Qi, Xinchen Yan, Yin Zhou, Leonidas Guibas, Dragomir Anguelov | 2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this observation, we propose NeRDi, a single-view NeRF synthesis framework with general image priors from 2D diffusion models. Formulating single-view reconstruction as an image-conditioned 3D generation problem, we optimize the NeRF representations by minimizing a diffusion loss on its arbitrary view renderings with a pretrained image diffusion model under the input-view constraint. We leverage off-the-shelf vision-language models and introduce a two-section language guidance as conditioning inputs to the diffusion model. This is essentially helpful for improving multiview content coherence as it narrows down the general image prior conditioned on the semantic and visual features of the single-view input image. Additionally, we introduce a geometric loss based on estimated depth maps to regularize the underlying 3D geometry of the NeRF. Experimental results on the DTU MVS dataset show that our method can synthesize novel views with higher quality even compared to existing methods trained on this dataset. We also demonstrate our generalizability in zero-shot NeRF synthesis for in-the-wild images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Deng_NeRDi_Single-View_NeRF_Synthesis_With_Language-Guided_Diffusion_As_General_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Deng_NeRDi_Single-View_NeRF_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_NeRDi_Single-View_NeRF_Synthesis_With_Language-Guided_Diffusion_As_General_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_NeRDi_Single-View_NeRF_Synthesis_With_Language-Guided_Diffusion_As_General_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Therbligs in Action: Video Understanding Through Motion Primitives | Eadom Dessalene, Michael Maynord, Cornelia Fermüller, Yiannis Aloimonos | In this paper we introduce a rule-based, compositional, and hierarchical modeling of action using Therbligs as our atoms. Introducing these atoms provides us with a consistent, expressive, contact-centered representation of action. Over the atoms we introduce a differentiable method of rule-based reasoning to regularize for logical consistency. Our approach is complementary to other approaches in that the Therblig-based representations produced by our architecture augment rather than replace existing architectures' representations. We release the first Therblig-centered annotations over two popular video datasets - EPIC Kitchens 100 and 50-Salads. We also broadly demonstrate benefits to adopting Therblig representations through evaluation on the following tasks: action segmentation, action anticipation, and action recognition - observing an average 10.5%/7.53%/6.5% relative improvement, respectively, over EPIC Kitchens and an average 8.9%/6.63%/4.8% relative improvement, respectively, over 50 Salads. Code and data will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023_paper.html | CVPR 2023 | null |
InstantAvatar: Learning Avatars From Monocular Video in 60 Seconds | Tianjian Jiang, Xu Chen, Jie Song, Otmar Hilliges | In this paper, we take one step further towards real-world applicability of monocular neural avatar reconstruction by contributing InstantAvatar, a system that can reconstruct human avatars from a monocular video within seconds, and these avatars can be animated and rendered at an interactive rate. To achieve this efficiency we propose a carefully designed and engineered system, that leverages emerging acceleration structures for neural fields, in combination with an efficient empty-space skipping strategy for dynamic scenes. We also contribute an efficient implementation that we will make available for research purposes. Compared to existing methods, InstantAvatar converges 130x faster and can be trained in minutes instead of hours. It achieves comparable or even better reconstruction quality and novel pose synthesis results. When given the same time budget, our method significantly outperforms SoTA methods. InstantAvatar can yield acceptable visual quality in as little as 10 seconds training time. For code and more demo results, please refer to https://ait.ethz.ch/InstantAvatar. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_InstantAvatar_Learning_Avatars_From_Monocular_Video_in_60_Seconds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_InstantAvatar_Learning_Avatars_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.10550 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_InstantAvatar_Learning_Avatars_From_Monocular_Video_in_60_Seconds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_InstantAvatar_Learning_Avatars_From_Monocular_Video_in_60_Seconds_CVPR_2023_paper.html | CVPR 2023 | null |
You Only Segment Once: Towards Real-Time Panoptic Segmentation | Jie Hu, Linyan Huang, Tianhe Ren, Shengchuan Zhang, Rongrong Ji, Liujuan Cao | In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO predicts masks via dynamic convolutions between panoptic kernels and image feature maps, in which you only need to segment once for both instance and semantic segmentation tasks. To reduce the computational overhead, we design a feature pyramid aggregator for the feature map extraction, and a separable dynamic decoder for the panoptic kernel generation. The aggregator re-parameterizes interpolation-first modules in a convolution-first way, which significantly speeds up the pipeline without any additional costs. The decoder performs multi-head cross-attention via separable dynamic convolution for better efficiency and accuracy. To the best of our knowledge, YOSO is the first real-time panoptic segmentation framework that delivers competitive performance compared to state-of-the-art models. Specifically, YOSO achieves 46.4 PQ, 45.6 FPS on COCO; 52.5 PQ, 22.6 FPS on Cityscapes; 38.0 PQ, 35.4 FPS on ADE20K; and 34.1 PQ, 7.1 FPS on Mapillary Vistas. Code is available at https://github.com/hujiecpp/YOSO. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_You_Only_Segment_Once_Towards_Real-Time_Panoptic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_You_Only_Segment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14651 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_You_Only_Segment_Once_Towards_Real-Time_Panoptic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_You_Only_Segment_Once_Towards_Real-Time_Panoptic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Single Image Reflection Removal Against Adversarial Attacks | Zhenbo Song, Zhenyuan Zhang, Kaihao Zhang, Wenhan Luo, Zhaoxin Fan, Wenqi Ren, Jianfeng Lu | This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR^2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Robust_Single_Image_Reflection_Removal_Against_Adversarial_Attacks_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Robust_Single_Image_Reflection_Removal_Against_Adversarial_Attacks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Robust_Single_Image_Reflection_Removal_Against_Adversarial_Attacks_CVPR_2023_paper.html | CVPR 2023 | null |
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation | Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, Dahua Lin, Ziwei Liu | Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale real-scanned 3D databases. To facilitate the development of 3D perception, reconstruction, and generation in the real world, we propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties: 1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations. 2) Rich Annotations: Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 3) Realistic Scans: The professional scanners support high-quality object scans with precise shapes and realistic appearances. With the vast exploration space offered by OmniObject3D, we carefully set up four evaluation tracks: a) robust 3D perception, b) novel-view synthesis, c) neural surface reconstruction, and d) 3D object generation. Extensive studies are performed on these four benchmarks, revealing new observations, challenges, and opportunities for future research in realistic 3D vision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_OmniObject3D_Large-Vocabulary_3D_Object_Dataset_for_Realistic_Perception_Reconstruction_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_OmniObject3D_Large-Vocabulary_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07525 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_OmniObject3D_Large-Vocabulary_3D_Object_Dataset_for_Realistic_Perception_Reconstruction_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_OmniObject3D_Large-Vocabulary_3D_Object_Dataset_for_Realistic_Perception_Reconstruction_and_CVPR_2023_paper.html | CVPR 2023 | null |
PartMix: Regularization Strategy To Learn Part Discovery for Visible-Infrared Person Re-Identification | Minsu Kim, Seungryong Kim, Jungin Park, Seongheon Park, Kwanghoon Sohn | Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data augmentation technique tailored for the part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains unexplored. In this paper, we present a novel data augmentation technique, dubbed PartMix, that synthesizes the augmented samples by mixing the part descriptors across the modalities to improve the performance of part-based VI-ReID models. Especially, we synthesize the positive and negative samples within the same and across different identities and regularize the backbone model through contrastive learning. In addition, we also present an entropy-based mining strategy to weaken the adverse impact of unreliable positive and negative samples. When incorporated into existing part-based VI-ReID model, PartMix consistently boosts the performance. We conduct experiments to demonstrate the effectiveness of our PartMix over the existing VI-ReID methods and provide ablation studies. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_PartMix_Regularization_Strategy_To_Learn_Part_Discovery_for_Visible-Infrared_Person_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_PartMix_Regularization_Strategy_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_PartMix_Regularization_Strategy_To_Learn_Part_Discovery_for_Visible-Infrared_Person_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_PartMix_Regularization_Strategy_To_Learn_Part_Discovery_for_Visible-Infrared_Person_CVPR_2023_paper.html | CVPR 2023 | null |
Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models | Qiucheng Wu, Yujian Liu, Handong Zhao, Ajinkya Kale, Trung Bui, Tong Yu, Zhe Lin, Yang Zhang, Shiyu Chang | Generative models have been widely studied in computer vision. Recently, diffusion models have drawn substantial attention due to the high quality of their generated images. A key desired property of image generative models is the ability to disentangle different attributes, which should enable modification towards a style without changing the semantic content, and the modification parameters should generalize to different images. Previous studies have found that generative adversarial networks (GANs) are inherently endowed with such disentanglement capability, so they can perform disentangled image editing without re-training or fine-tuning the network. In this work, we explore whether diffusion models are also inherently equipped with such a capability. Our finding is that for stable diffusion models, by partially changing the input text embedding from a neutral description (e.g., "a photo of person") to one with style (e.g., "a photo of person with smile") while fixing all the Gaussian random noises introduced during the denoising process, the generated images can be modified towards the target style without changing the semantic content. Based on this finding, we further propose a simple, light-weight image editing algorithm where the mixing weights of the two text embeddings are optimized for style matching and content preservation. This entire process only involves optimizing over around 50 parameters and does not fine-tune the diffusion model itself. Experiments show that the proposed method can modify a wide range of attributes, with the performance outperforming diffusion-model-based image-editing algorithms that require fine-tuning. The optimized weights generalize well to different images. Our code is publicly available at https://github.com/UCSB-NLP-Chang/DiffusionDisentanglement. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Uncovering_the_Disentanglement_Capability_in_Text-to-Image_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Uncovering_the_Disentanglement_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08698 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Uncovering_the_Disentanglement_Capability_in_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Uncovering_the_Disentanglement_Capability_in_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Feature Representation Learning With Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition | Zhijun Zhai, Jianhui Zhao, Chengjiang Long, Wenju Xu, Shuangjiang He, Huijuan Zhao | Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to micro-expression characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic feature targeted to the subsequent ME recognition task, and a well-designed Transformer fusion mechanism composed of the Transformer-based local fusion module, global fusion module, and full-face fusion module is applied to extract the multi-level informative feature from the output of the DGM for the final micro-expression prediction. Extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have strongly demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhai_Feature_Representation_Learning_With_Adaptive_Displacement_Generation_and_Transformer_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhai_Feature_Representation_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04420 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhai_Feature_Representation_Learning_With_Adaptive_Displacement_Generation_and_Transformer_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhai_Feature_Representation_Learning_With_Adaptive_Displacement_Generation_and_Transformer_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud Classification | Jiajing Chen, Minmin Yang, Senem Velipasalar | Although different approaches have been proposed for 3D point cloud-related tasks, few-shot learning (FSL) of 3D point clouds still remains under-explored. In FSL, unlike traditional supervised learning, the classes of training and test data do not overlap, and a model needs to recognize unseen classes from only a few samples. Existing FSL methods for 3D point clouds employ point-based models as their backbone. Yet, based on our extensive experiments and analysis, we first show that using a point-based backbone is not the most suitable FSL approach, since (i) a large number of points' features are discarded by the max pooling operation used in 3D point-based backbones, decreasing the ability of representing shape information; (ii)point-based backbones are sensitive to occlusion. To address these issues, we propose employing a projection- and 2D Convolutional Neural Network-based backbone, referred to as the ViewNet, for FSL from 3D point clouds. Our approach first projects a 3D point cloud onto six different views to alleviate the issue of missing points. Also, to generate more descriptive and distinguishing features, we propose View Pooling, which combines different projected plane combinations into five groups and performs max-pooling on each of them. The experiments performed on the ModelNet40, ScanObjectNN and ModelNet40-C datasets, with cross validation, show that our method consistently outperforms the state-of-the-art baselines. Moreover, compared to traditional image classification backbones, such as ResNet, the proposed ViewNet can extract more distinguishing features from multiple views of a point cloud. We also show that ViewNet can be used as a backbone with different FSL heads and provides improved performance compared to traditionally used backbones. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_ViewNet_A_Novel_Projection-Based_Backbone_With_View_Pooling_for_Few-Shot_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_ViewNet_A_Novel_Projection-Based_Backbone_With_View_Pooling_for_Few-Shot_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_ViewNet_A_Novel_Projection-Based_Backbone_With_View_Pooling_for_Few-Shot_CVPR_2023_paper.html | CVPR 2023 | null |
EXIF As Language: Learning Cross-Modal Associations Between Images and Camera Metadata | Chenhao Zheng, Ayush Shrivastava, Andrew Owens | We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then processing it with a transformer. The features that we learn significantly outperform other self-supervised and supervised features on downstream image forensics and calibration tasks. In particular, we successfully localize spliced image regions "zero shot" by clustering the visual embeddings for all of the patches within an image. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_EXIF_As_Language_Learning_Cross-Modal_Associations_Between_Images_and_Camera_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.04647 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_EXIF_As_Language_Learning_Cross-Modal_Associations_Between_Images_and_Camera_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_EXIF_As_Language_Learning_Cross-Modal_Associations_Between_Images_and_Camera_CVPR_2023_paper.html | CVPR 2023 | null |
ANetQA: A Large-Scale Benchmark for Fine-Grained Compositional Reasoning Over Untrimmed Videos | Zhou Yu, Lixiang Zheng, Zhou Zhao, Fei Wu, Jianping Fan, Kui Ren, Jun Yu | Building benchmarks to systemically analyze different capabilities of video question answering (VideoQA) models is challenging yet crucial. Existing benchmarks often use non-compositional simple questions and suffer from language biases, making it difficult to diagnose model weaknesses incisively. A recent benchmark AGQA poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control. However, its questions have limitations in reasoning about the fine-grained semantics in videos as such information is absent in its scene graphs. To this end, we present ANetQA, a large-scale benchmark that supports fine-grained compositional reasoning over the challenging untrimmed videos from ActivityNet. Similar to AGQA, the QA pairs in ANetQA are automatically generated from annotated video scene graphs. The fine-grained properties of ANetQA are reflected in the following: (i) untrimmed videos with fine-grained semantics; (ii) spatio-temporal scene graphs with fine-grained taxonomies; and (iii) diverse questions generated from fine-grained templates. ANetQA attains 1.4 billion unbalanced and 13.4 million balanced QA pairs, which is an order of magnitude larger than AGQA with a similar number of videos. Comprehensive experiments are performed for state-of-the-art methods. The best model achieves 44.5% accuracy while human performance tops out at 84.5%, leaving sufficient room for improvement. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_ANetQA_A_Large-Scale_Benchmark_for_Fine-Grained_Compositional_Reasoning_Over_Untrimmed_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_ANetQA_A_Large-Scale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.02519 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_ANetQA_A_Large-Scale_Benchmark_for_Fine-Grained_Compositional_Reasoning_Over_Untrimmed_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_ANetQA_A_Large-Scale_Benchmark_for_Fine-Grained_Compositional_Reasoning_Over_Untrimmed_CVPR_2023_paper.html | CVPR 2023 | null |
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation | Wenxuan Zhang, Xiaodong Cun, Xuan Wang, Yong Zhang, Xi Shen, Yu Guo, Ying Shan, Fei Wang | Generating talking head videos through a face image and a piece of speech audio still contains many challenges. i.e., unnatural head movement, distorted expression, and identity modification. We argue that these issues are mainly caused by learning from the coupled 2D motion fields. On the other hand, explicitly using 3D information also suffers problems of stiff expression and incoherent video. We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation. To learn the realistic motion coefficients, we explicitly model the connections between audio and different types of motion coefficients individually. Precisely, we present ExpNet to learn the accurate facial expression from audio by distilling both coefficients and 3D-rendered faces. As for the head pose, we design PoseVAE via a conditional VAE to synthesize head motion in different styles. Finally, the generated 3D motion coefficients are mapped to the unsupervised 3D keypoints space of the proposed face render to synthesize the final video. We conducted extensive experiments to show the superior of our method in terms of motion and video quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_SadTalker_Learning_Realistic_3D_Motion_Coefficients_for_Stylized_Audio-Driven_Single_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_SadTalker_Learning_Realistic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12194 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_SadTalker_Learning_Realistic_3D_Motion_Coefficients_for_Stylized_Audio-Driven_Single_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_SadTalker_Learning_Realistic_3D_Motion_Coefficients_for_Stylized_Audio-Driven_Single_CVPR_2023_paper.html | CVPR 2023 | null |
HAAV: Hierarchical Aggregation of Augmented Views for Image Captioning | Chia-Wen Kuo, Zsolt Kira | A great deal of progress has been made in image captioning, driven by research into how to encode the image using pre-trained models. This includes visual encodings (e.g. image grid features or detected objects) and more recently textual encodings (e.g. image tags or text descriptions of image regions). As more advanced encodings are available and incorporated, it is natural to ask: how to efficiently and effectively leverage the heterogeneous set of encodings? In this paper, we propose to regard the encodings as augmented views of the input image. The image captioning model encodes each view independently with a shared encoder efficiently, and a contrastive loss is incorporated across the encoded views in a novel way to improve their representation quality and the model's data efficiency. Our proposed hierarchical decoder then adaptively weighs the encoded views according to their effectiveness for caption generation by first aggregating within each view at the token level, and then across views at the view level. We demonstrate significant performance improvements of +5.6% CIDEr on MS-COCO and +12.9% CIDEr on Flickr30k compared to state of the arts, | https://openaccess.thecvf.com/content/CVPR2023/papers/Kuo_HAAV_Hierarchical_Aggregation_of_Augmented_Views_for_Image_Captioning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kuo_HAAV_Hierarchical_Aggregation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kuo_HAAV_Hierarchical_Aggregation_of_Augmented_Views_for_Image_Captioning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kuo_HAAV_Hierarchical_Aggregation_of_Augmented_Views_for_Image_Captioning_CVPR_2023_paper.html | CVPR 2023 | null |
CLAMP: Prompt-Based Contrastive Learning for Connecting Language and Animal Pose | Xu Zhang, Wen Wang, Zhe Chen, Yufei Xu, Jing Zhang, Dacheng Tao | Animal pose estimation is challenging for existing image-based methods because of limited training data and large intra- and inter-species variances. Motivated by the progress of visual-language research, we propose that pre-trained language models (eg, CLIP) can facilitate animal pose estimation by providing rich prior knowledge for describing animal keypoints in text. However, we found that building effective connections between pre-trained language models and visual animal keypoints is non-trivial since the gap between text-based descriptions and keypoint-based visual features about animal pose can be significant. To address this issue, we introduce a novel prompt-based Contrastive learning scheme for connecting Language and AniMal Pose (CLAMP) effectively. The CLAMP attempts to bridge the gap by adapting the text prompts to the animal keypoints during network training. The adaptation is decomposed into spatial-aware and feature-aware processes, and two novel contrastive losses are devised correspondingly. In practice, the CLAMP enables the first cross-modal animal pose estimation paradigm. Experimental results show that our method achieves state-of-the-art performance under the supervised, few-shot, and zero-shot settings, outperforming image-based methods by a large margin. The code is available at https://github.com/xuzhang1199/CLAMP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_CLAMP_Prompt-Based_Contrastive_Learning_for_Connecting_Language_and_Animal_Pose_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_CLAMP_Prompt-Based_Contrastive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.11752 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CLAMP_Prompt-Based_Contrastive_Learning_for_Connecting_Language_and_Animal_Pose_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CLAMP_Prompt-Based_Contrastive_Learning_for_Connecting_Language_and_Animal_Pose_CVPR_2023_paper.html | CVPR 2023 | null |
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking | Ziqi Pang, Jie Li, Pavel Tokmakov, Dian Chen, Sergey Zagoruyko, Yu-Xiong Wang | This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects. Thus, we name it "Past-and-Future reasoning for Tracking" (PF-Track). Specifically, our method adapts the "tracking by attention" framework and represents tracked instances coherently over time with object queries. To explicitly use historical cues, our "Past Reasoning" module learns to refine the tracks and enhance the object features by cross-attending to queries from previous frames and other objects. The "Future Reasoning" module digests historical information and predicts robust future trajectories. In the case of long-term occlusions, our method maintains the object positions and enables re-association by integrating motion predictions. On the nuScenes dataset, our method improves AMOTA by a large margin and remarkably reduces ID-Switches by 90% compared to prior approaches, which is an order of magnitude less. The code and models are made available at https://github.com/TRI-ML/PF-Track. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pang_Standing_Between_Past_and_Future_Spatio-Temporal_Modeling_for_Multi-Camera_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pang_Standing_Between_Past_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.03802 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Standing_Between_Past_and_Future_Spatio-Temporal_Modeling_for_Multi-Camera_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Standing_Between_Past_and_Future_Spatio-Temporal_Modeling_for_Multi-Camera_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Sample Relationship for Exposure Correction | Jie Huang, Feng Zhao, Man Zhou, Jie Xiao, Naishan Zheng, Kaiwen Zheng, Zhiwei Xiong | Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is opposite. Despite the great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency. In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a mini-batch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Learning_Sample_Relationship_for_Exposure_Correction_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_Sample_Relationship_for_Exposure_Correction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_Sample_Relationship_for_Exposure_Correction_CVPR_2023_paper.html | CVPR 2023 | null |
TRACE: 5D Temporal Regression of Avatars With Dynamic Cameras in 3D Environments | Yu Sun, Qian Bao, Wu Liu, Tao Mei, Michael J. Black | Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current methods still cannot reliably estimate moving humans in global coordinates, which is critical for many applications. This is particularly challenging when the camera is also moving, entangling human and camera motion. To address these issues, we adopt a novel 5D representation (space, time, and identity) that enables end-to-end reasoning about people in scenes. Our method, called TRACE, introduces several novel architectural components. Most importantly, it uses two new "maps" to reason about the 3D trajectory of people over time in camera, and world, coordinates. An additional memory unit enables persistent tracking of people even during long occlusions. TRACE is the first one-stage method to jointly recover and track 3D humans in global coordinates from dynamic cameras. By training it end-to-end, and using full image information, TRACE achieves state-of-the-art performance on tracking and HPS benchmarks. The code and dataset are released for research purposes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_TRACE_5D_Temporal_Regression_of_Avatars_With_Dynamic_Cameras_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_TRACE_5D_Temporal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_TRACE_5D_Temporal_Regression_of_Avatars_With_Dynamic_Cameras_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_TRACE_5D_Temporal_Regression_of_Avatars_With_Dynamic_Cameras_in_CVPR_2023_paper.html | CVPR 2023 | null |
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation | Taeyeop Lee, Jonathan Tremblay, Valts Blukis, Bowen Wen, Byeong-Uk Lee, Inkyu Shin, Stan Birchfield, In So Kweon, Kuk-Jin Yoon | Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requiring labels on the target data. In this paper, we propose a method of test-time adaptation for category-level object pose estimation called TTA-COPE. We design a pose ensemble approach with a self-training loss using pose-aware confidence. Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime. Extensive experimental results demonstrate that the proposed pose ensemble and the self-training loss improve category-level object pose performance during test time under both semi-supervised and unsupervised settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_TTA-COPE_Test-Time_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_TTA-COPE_Test-Time_Adaptation_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_TTA-COPE_Test-Time_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_TTA-COPE_Test-Time_Adaptation_for_Category-Level_Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
TrojDiff: Trojan Attacks on Diffusion Models With Diverse Targets | Weixin Chen, Dawn Song, Bo Li | Diffusion models have achieved great success in a range of tasks, such as image synthesis and molecule design. As such successes hinge on large-scale training data collected from diverse sources, the trustworthiness of these collected data is hard to control or audit. In this work, we aim to explore the vulnerabilities of diffusion models under potential training data manipulations and try to answer: How hard is it to perform Trojan attacks on well-trained diffusion models? What are the adversarial targets that such Trojan attacks can achieve? To answer these questions, we propose an effective Trojan attack against diffusion models, TrojDiff, which optimizes the Trojan diffusion and generative processes during training. In particular, we design novel transitions during the Trojan diffusion process to diffuse adversarial targets into a biased Gaussian distribution and propose a new parameterization of the Trojan generative process that leads to an effective training objective for the attack. In addition, we consider three types of adversarial targets: the Trojaned diffusion models will always output instances belonging to a certain class from the in-domain distribution (In-D2D attack), out-of-domain distribution (Out-D2D-attack), and one specific instance (D2I attack). We evaluate TrojDiff on CIFAR-10 and CelebA datasets against both DDPM and DDIM diffusion models. We show that TrojDiff always achieves high attack performance under different adversarial targets using different types of triggers, while the performance in benign environments is preserved. The code is available at https://github.com/chenweixin107/TrojDiff. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_TrojDiff_Trojan_Attacks_on_Diffusion_Models_With_Diverse_Targets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_TrojDiff_Trojan_Attacks_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05762 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_TrojDiff_Trojan_Attacks_on_Diffusion_Models_With_Diverse_Targets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_TrojDiff_Trojan_Attacks_on_Diffusion_Models_With_Diverse_Targets_CVPR_2023_paper.html | CVPR 2023 | null |
End-to-End 3D Dense Captioning With Vote2Cap-DETR | Sijin Chen, Hongyuan Zhu, Xin Chen, Yinjie Lei, Gang Yu, Tao Chen | 3D dense captioning aims to generate multiple captions localized with their associated object regions. Existing methods follow a sophisticated "detect-then-describe" pipeline equipped with numerous hand-crafted components. However, these hand-crafted components would yield suboptimal performance given cluttered object spatial and class distributions among different scenes. In this paper, we propose a simple-yet-effective transformer framework Vote2Cap-DETR based on recent popular DEtection TRansformer (DETR). Compared with prior arts, our framework has several appealing advantages: 1) Without resorting to numerous hand-crafted components, our method is based on a full transformer encoder-decoder architecture with a learnable vote query driven object decoder, and a caption decoder that produces the dense captions in a set-prediction manner. 2) In contrast to the two-stage scheme, our method can perform detection and captioning in one-stage. 3) Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate that our Vote2Cap-DETR surpasses current state-of-the-arts by 11.13% and 7.11% in [email protected], respectively. Codes will be released soon. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_End-to-End_3D_Dense_Captioning_With_Vote2Cap-DETR_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_End-to-End_3D_Dense_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02508 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_End-to-End_3D_Dense_Captioning_With_Vote2Cap-DETR_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_End-to-End_3D_Dense_Captioning_With_Vote2Cap-DETR_CVPR_2023_paper.html | CVPR 2023 | null |
Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing With Non-Learnable Primitives | Chuntao Ding, Zhichao Lu, Shangguang Wang, Ran Cheng, Vishnu Naresh Boddeti | Multi-task learning (MTL) seeks to learn a single model to accomplish multiple tasks by leveraging shared information among the tasks. Existing MTL models, however, have been known to suffer from negative interference among tasks. Efforts to mitigate task interference have focused on either loss/gradient balancing or implicit parameter partitioning with partial overlaps among the tasks. In this paper, we propose ETR-NLP to mitigate task interference through a synergistic combination of non-learnable primitives (NLPs) and explicit task routing (ETR). Our key idea is to employ non-learnable primitives to extract a diverse set of task-agnostic features and recombine them into a shared branch common to all tasks and explicit task-specific branches reserved for each task. The non-learnable primitives and the explicit decoupling of learnable parameters into shared and task-specific ones afford the flexibility needed for minimizing task interference. We evaluate the efficacy of ETR-NLP networks for both image-level classification and pixel-level dense prediction MTL problems. Experimental results indicate that ETR-NLP significantly outperforms state-of-the-art baselines with fewer learnable parameters and similar FLOPs across all datasets. Code is available at this URL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_Mitigating_Task_Interference_in_Multi-Task_Learning_via_Explicit_Task_Routing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_Mitigating_Task_Interference_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Mitigating_Task_Interference_in_Multi-Task_Learning_via_Explicit_Task_Routing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Mitigating_Task_Interference_in_Multi-Task_Learning_via_Explicit_Task_Routing_CVPR_2023_paper.html | CVPR 2023 | null |
Learned Two-Plane Perspective Prior Based Image Resampling for Efficient Object Detection | Anurag Ghosh, N. Dinesh Reddy, Christoph Mertz, Srinivasa G. Narasimhan | Real-time efficient perception is critical for autonomous navigation and city scale sensing. Orthogonal to architectural improvements, streaming perception approaches have exploited adaptive sampling improving real-time detection performance. In this work, we propose a learnable geometry-guided prior that incorporates rough geometry of the 3D scene (a ground plane and a plane above) to resample images for efficient object detection. This significantly improves small and far-away object detection performance while also being more efficient both in terms of latency and memory. For autonomous navigation, using the same detector and scale, our approach improves detection rate by +4.1 AP_S or +39% and in real-time performance by +5.3 sAP_S or +63% for small objects over state-of-the-art (SOTA). For fixed traffic cameras, our approach detects small objects at image scales other methods cannot. At the same scale, our approach improves detection of small objects by 195% (+12.5 AP_S) over naive-downsampling and 63% (+4.2 AP_S) over SOTA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ghosh_Learned_Two-Plane_Perspective_Prior_Based_Image_Resampling_for_Efficient_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ghosh_Learned_Two-Plane_Perspective_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14311 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ghosh_Learned_Two-Plane_Perspective_Prior_Based_Image_Resampling_for_Efficient_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ghosh_Learned_Two-Plane_Perspective_Prior_Based_Image_Resampling_for_Efficient_Object_CVPR_2023_paper.html | CVPR 2023 | null |
Tell Me What Happened: Unifying Text-Guided Video Completion via Multimodal Masked Video Generation | Tsu-Jui Fu, Licheng Yu, Ning Zhang, Cheng-Yang Fu, Jong-Chyi Su, William Yang Wang, Sean Bell | Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction. We then propose Multimodal Masked Video Generation (MMVG) to address this TVC task. During training, MMVG discretizes the video frames into visual tokens and masks most of them to perform video completion from any time point. At inference time, a single MMVG model can address all 3 cases of TVC, including video prediction, rewind, and infilling, by applying corresponding masking conditions. We evaluate MMVG in various video scenarios, including egocentric, animation, and gaming. Extensive experimental results indicate that MMVG is effective in generating high-quality visual appearances with text guidance for TVC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fu_Tell_Me_What_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12824 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023_paper.html | CVPR 2023 | null |
Tracking Through Containers and Occluders in the Wild | Basile Van Hoorick, Pavel Tokmakov, Simon Stent, Jie Li, Carl Vondrick | Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce TCOW, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequence, segment both the projected extent of the target object, as well as the surrounding container or occluder whenever one exists. To study this task, we create a mixture of synthetic and annotated real datasets to support both supervised learning and structured evaluation of model performance under various forms of task variation, such as moving or nested containment. We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence. | https://openaccess.thecvf.com/content/CVPR2023/papers/Van_Hoorick_Tracking_Through_Containers_and_Occluders_in_the_Wild_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Van_Hoorick_Tracking_Through_Containers_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.03052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Van_Hoorick_Tracking_Through_Containers_and_Occluders_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Van_Hoorick_Tracking_Through_Containers_and_Occluders_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation | Yuwei Yang, Munawar Hayat, Zhao Jin, Chao Ren, Yinjie Lei | Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered. Substantial memory storage and expensive re-training is required to update the model to sequentially arriving data for new concepts. In this paper, to continually learn new categories using previous knowledge, we introduce class-incremental semantic segmentation of 3D point cloud. Unlike 2D images, 3D point clouds are disordered and unstructured, making it difficult to store and transfer knowledge especially when the previous data is not available. We further face the challenge of semantic shift, where previous/future classes are indiscriminately collapsed and treated as the background in the current step, causing a dramatic performance drop on past classes. We exploit the structure of point cloud and propose two strategies to address these challenges. First, we design a geometry-aware distillation module that transfers point-wise feature associations in terms of their geometric characteristics. To counter forgetting caused by the semantic shift, we further develop an uncertainty-aware pseudo-labelling scheme that eliminates noise in uncertain pseudo-labels by label propagation within a local neighborhood. Our extensive experiments on S3DIS and ScanNet in a class-incremental setting show impressive results comparable to the joint training strategy (upper bound). Code is available at: https://github.com/leolyj/3DPC-CISS | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Geometry_and_Uncertainty-Aware_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Kernel Surface Reconstruction | Jiahui Huang, Zan Gojcic, Matan Atzmon, Or Litany, Sanja Fidler, Francis Williams | We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural Kernel Fields (NKF) representation. It enjoys similar generalization capabilities to NKF, while simultaneously addressing its main limitations: (a) We can scale to large scenes through compactly supported kernel functions, which enable the use of memory-efficient sparse linear solvers. (b) We are robust to noise, through a gradient fitting solve. (c) We minimize training requirements, enabling us to learn from any dataset of dense oriented points, and even mix training data consisting of objects and scenes at different scales. Our method is capable of reconstructing millions of points in a few seconds, and handling very large scenes in an out-of-core fashion. We achieve state-of-the-art results on reconstruction benchmarks consisting of single objects (ShapeNet, ABC), indoor scenes (ScanNet, Matterport3D), and outdoor scenes (CARLA, Waymo). | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Neural_Kernel_Surface_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Neural_Kernel_Surface_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Neural_Kernel_Surface_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Neural_Kernel_Surface_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets | Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang | Despite incredible advances, deep learning has been shown to be susceptible to adversarial attacks. Numerous approaches were proposed to train robust networks both empirically and certifiably. However, most of them defend against only a single type of attack, while recent work steps forward at defending against multiple attacks. In this paper, to understand multi-target robustness, we view this problem as a bargaining game in which different players (adversaries) negotiate to reach an agreement on a joint direction of parameter updating. We identify a phenomenon named player domination in the bargaining game, and show that with this phenomenon, some of the existing max-based approaches such as MAX and MSD do not converge. Based on our theoretical results, we design a novel framework that adjusts the budgets of different adversaries to avoid player domination. Experiments on two benchmarks show that employing the proposed framework to the existing approaches significantly advances multi-target robustness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Cooperation_or_Competition_Avoiding_Player_Domination_for_Multi-Target_Robustness_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Cooperation_or_Competition_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Cooperation_or_Competition_Avoiding_Player_Domination_for_Multi-Target_Robustness_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Cooperation_or_Competition_Avoiding_Player_Domination_for_Multi-Target_Robustness_via_CVPR_2023_paper.html | CVPR 2023 | null |
Decompose, Adjust, Compose: Effective Normalization by Playing With Frequency for Domain Generalization | Sangrok Lee, Jongseong Bae, Ha Young Kim | Domain generalization (DG) is a principal task to evaluate the robustness of computer vision models. Many previous studies have used normalization for DG. In normalization, statistics and normalized features are regarded as style and content, respectively. However, it has a content variation problem when removing style because the boundary between content and style is unclear. This study addresses this problem from the frequency domain perspective, where amplitude and phase are considered as style and content, respectively. First, we verify the quantitative phase variation of normalization through the mathematical derivation of the Fourier transform formula. Then, based on this, we propose a novel normalization method, PCNorm, which eliminates style only as the preserving content through spectral decomposition. Furthermore, we propose advanced PCNorm variants, CCNorm and SCNorm, which adjust the degrees of variations in content and style, respectively. Thus, they can learn domain-agnostic representations for DG. With the normalization methods, we propose ResNet-variant models, DAC-P and DAC-SC, which are robust to the domain gap. The proposed models outperform other recent DG methods. The DAC-SC achieves an average state-of-the-art performance of 65.6% on five datasets: PACS, VLCS, Office-Home, DomainNet, and TerraIncognita. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Decompose_Adjust_Compose_Effective_Normalization_by_Playing_With_Frequency_for_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.02328 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Decompose_Adjust_Compose_Effective_Normalization_by_Playing_With_Frequency_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Decompose_Adjust_Compose_Effective_Normalization_by_Playing_With_Frequency_for_CVPR_2023_paper.html | CVPR 2023 | null |
Multilateral Semantic Relations Modeling for Image Text Retrieval | Zheng Wang, Zhenwei Gao, Kangshuai Guo, Yang Yang, Xiaoming Wang, Heng Tao Shen | Image-text retrieval is a fundamental task to bridge vision and language by exploiting various strategies to fine-grained alignment between regions and words. This is still tough mainly because of one-to-many correspondence, where a set of matches from another modality can be accessed by a random query. While existing solutions to this problem including multi-point mapping, probabilistic distribution, and geometric embedding have made promising progress, one-to-many correspondence is still under-explored. In this work, we develop a Multilateral Semantic Relations Modeling (termed MSRM) for image-text retrieval to capture the one-to-many correspondence between multiple samples and a given query via hypergraph modeling. Specifically, a given query is first mapped as a probabilistic embedding to learn its true semantic distribution based on Mahalanobis distance. Then each candidate instance in a mini-batch is regarded as a hypergraph node with its mean semantics while a Gaussian query is modeled as a hyperedge to capture the semantic correlations beyond the pair between candidate points and the query. Comprehensive experimental results on two widely used datasets demonstrate that our MSRM method can outperform state-of-the-art methods in the settlement of multiple matches while still maintaining the comparable performance of instance-level matching. Our codes and checkpoints will be released soon. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Multilateral_Semantic_Relations_Modeling_for_Image_Text_Retrieval_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multilateral_Semantic_Relations_Modeling_for_Image_Text_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multilateral_Semantic_Relations_Modeling_for_Image_Text_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing | Jiechong Song, Chong Mou, Shiqi Wang, Siwei Ma, Jian Zhang | By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based Unfolding Framework (OCTUF) for image CS. Specifically, we design a novel Dual Cross Attention (Dual-CA) sub-module, which consists of an Inertia-Supplied Cross Attention (ISCA) block and a Projection-Guided Cross Attention (PGCA) block. ISCA block introduces multi-channel inertia forces and increases the memory effect by a cross attention mechanism between adjacent iterations. And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block. Extensive CS experiments manifest that our OCTUF achieves superior performance compared to state-of-the-art methods while training lower complexity. Codes are available at https://github.com/songjiechong/OCTUF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Optimization-Inspired_Cross-Attention_Transformer_for_Compressive_Sensing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_Optimization-Inspired_Cross-Attention_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.13986 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Optimization-Inspired_Cross-Attention_Transformer_for_Compressive_Sensing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Optimization-Inspired_Cross-Attention_Transformer_for_Compressive_Sensing_CVPR_2023_paper.html | CVPR 2023 | null |
Novel Class Discovery for 3D Point Cloud Semantic Segmentation | Luigi Riz, Cristiano Saltori, Elisa Ricci, Fabio Poiesi | Novel class discovery (NCD) for semantic segmentation is the task of learning a model that can segment unlabelled (novel) classes using only the supervision from labelled (base) classes. This problem has recently been pioneered for 2D image data, but no work exists for 3D point cloud data. In fact, the assumptions made for 2D are loosely applicable to 3D in this case. This paper is presented to advance the state of the art on point cloud data analysis in four directions. Firstly, we address the new problem of NCD for point cloud semantic segmentation. Secondly, we show that the transposition of the only existing NCD method for 2D semantic segmentation to 3D data is suboptimal. Thirdly, we present a new method for NCD based on online clustering that exploits uncertainty quantification to produce prototypes for pseudo-labelling the points of the novel classes. Lastly, we introduce a new evaluation protocol to assess the performance of NCD for point cloud semantic segmentation. We thoroughly evaluate our method on SemanticKITTI and SemanticPOSS datasets, showing that it can significantly outperform the baseline. Project page: https://github.com/LuigiRiz/NOPS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Riz_Novel_Class_Discovery_for_3D_Point_Cloud_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Riz_Novel_Class_Discovery_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Riz_Novel_Class_Discovery_for_3D_Point_Cloud_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Riz_Novel_Class_Discovery_for_3D_Point_Cloud_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection | Shuailei Ma, Yuefeng Wang, Ying Wei, Jiaqi Fan, Thomas H. Li, Hongli Liu, Fanbing Lv | Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The PLM does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Comprehensive experiments on two benchmark datasets, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art in terms of all metrics in the task of OWOD, incremental object detection (IOD) and open-set detection. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_CAT_LoCalization_and_IdentificAtion_Cascade_Detection_Transformer_for_Open-World_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_CAT_LoCalization_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01970 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_CAT_LoCalization_and_IdentificAtion_Cascade_Detection_Transformer_for_Open-World_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_CAT_LoCalization_and_IdentificAtion_Cascade_Detection_Transformer_for_Open-World_Object_CVPR_2023_paper.html | CVPR 2023 | null |
TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization | Fabrizio Guillaro, Davide Cozzolino, Avneesh Sud, Nicholas Dufour, Luisa Verdoliva | In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture that combines the RGB image and a learned noise-sensitive fingerprint. The latter learns to embed the artifacts related to the camera internal and external processing by training only on real data in a self-supervised manner. Forgeries are detected as deviations from the expected regular pattern that characterizes each pristine image. Looking for anomalies makes the approach able to robustly detect a variety of local manipulations, ensuring generalization. In addition to a pixel-level localization map and a whole-image integrity score, our approach outputs a reliability map that highlights areas where localization predictions may be error-prone. This is particularly important in forensic applications in order to reduce false alarms and allow for a large scale analysis. Extensive experiments on several datasets show that our method is able to reliably detect and localize both cheapfakes and deepfakes manipulations outperforming state-of-the-art works. Code is publicly available at https://grip-unina.github.io/TruFor/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guillaro_TruFor_Leveraging_All-Round_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.10957 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023_paper.html | CVPR 2023 | null |
LANA: A Language-Capable Navigator for Instruction Following and Generation | Xiaohan Wang, Wenguan Wang, Jiayi Shao, Yi Yang | Recently, visual-language navigation (VLN) -- entailing robot agents to follow navigation instructions -- has shown great advance. However, existing literature put most emphasis on interpreting instructions into actions, only delivering "dumb" wayfinding agents. In this article, we devise LANA, a language-capable navigation agent which is able to not only execute human-written navigation commands, but also provide route descriptions to humans. This is achieved by simultaneously learning instruction following and generation with only one single model. More specifically, two encoders, respectively for route and language encoding, are built and shared by two decoders, respectively, for action prediction and instruction generation, so as to exploit cross-task knowledge and capture task-specific characteristics. Throughout pretraining and fine-tuning, both instruction following and generation are set as optimization objectives. We empirically verify that, compared with recent advanced task-specific solutions, LANA attains better performances on both instruction following and route description, with nearly half complexity. In addition, endowed with language generation capability, LANA can explain to humans its behaviors and assist human's wayfinding. This work is expected to foster future efforts towards building more trustworthy and socially-intelligent navigation robots. Our code will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_LANA_A_Language-Capable_Navigator_for_Instruction_Following_and_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_LANA_A_Language-Capable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08409 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LANA_A_Language-Capable_Navigator_for_Instruction_Following_and_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LANA_A_Language-Capable_Navigator_for_Instruction_Following_and_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning 3D-Aware Image Synthesis With Unknown Pose Distribution | Zifan Shi, Yujun Shen, Yinghao Xu, Sida Peng, Yiyi Liao, Sheng Guo, Qifeng Chen, Dit-Yan Yeung | Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution pre-estimated on the training set. An inaccurate estimation may mislead the model into learning faulty geometry. This work proposes PoF3D that frees generative radiance fields from the requirements of 3D pose priors. We first equip the generator with an efficient pose learner, which is able to infer a pose from a latent code, to approximate the underlying true pose distribution automatically. We then assign the discriminator a task to learn pose distribution under the supervision of the generator and to differentiate real and synthesized images with the predicted pose as the condition. The pose-free generator and the pose-aware discriminator are jointly trained in an adversarial manner. Extensive results on a couple of datasets confirm that the performance of our approach, regarding both image quality and geometry quality, is on par with state of the art. To our best knowledge, PoF3D demonstrates the feasibility of learning high-quality 3D-aware image synthesis without using 3D pose priors for the first time. Project page can be found at https://vivianszf.github.io/pof3d/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shi_Learning_3D-Aware_Image_Synthesis_With_Unknown_Pose_Distribution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shi_Learning_3D-Aware_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07702 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Learning_3D-Aware_Image_Synthesis_With_Unknown_Pose_Distribution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Learning_3D-Aware_Image_Synthesis_With_Unknown_Pose_Distribution_CVPR_2023_paper.html | CVPR 2023 | null |
Normalizing Flow Based Feature Synthesis for Outlier-Aware Object Detection | Nishant Kumar, Siniša Šegvić, Abouzar Eslami, Stefan Gumhold | Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for outlier objects. Recent outlier-aware object detection approaches estimate the density of instance-wide features with class-conditional Gaussians and train on synthesized outlier features from their low-likelihood regions. However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians. We propose a novel outlier-aware object detection framework that distinguishes outliers from inlier objects by learning the joint data distribution of all inlier classes with an invertible normalizing flow. The appropriate sampling of the flow model ensures that the synthesized outliers have a lower likelihood than inliers of all object classes, thereby modeling a better decision boundary between inlier and outlier objects. Our approach significantly outperforms the state-of-the-art for outlier-aware object detection on both image and video datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kumar_Normalizing_Flow_Based_Feature_Synthesis_for_Outlier-Aware_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kumar_Normalizing_Flow_Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kumar_Normalizing_Flow_Based_Feature_Synthesis_for_Outlier-Aware_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kumar_Normalizing_Flow_Based_Feature_Synthesis_for_Outlier-Aware_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
DivClust: Controlling Diversity in Deep Clustering | Ioannis Maniadis Metaxas, Georgios Tzimiropoulos, Ioannis Patras | Clustering has been a major research topic in the field of machine learning, one to which Deep Learning has recently been applied with significant success. However, an aspect of clustering that is not addressed by existing deep clustering methods, is that of efficiently producing multiple, diverse partitionings for a given dataset. This is particularly important, as a diverse set of base clusterings are necessary for consensus clustering, which has been found to produce better and more robust results than relying on a single clustering. To address this gap, we propose DivClust, a diversity controlling loss that can be incorporated into existing deep clustering frameworks to produce multiple clusterings with the desired degree of diversity. We conduct experiments with multiple datasets and deep clustering frameworks and show that: a) our method effectively controls diversity across frameworks and datasets with very small additional computational cost, b) the sets of clusterings learned by DivClust include solutions that significantly outperform single-clustering baselines, and c) using an off-the-shelf consensus clustering algorithm, DivClust produces consensus clustering solutions that consistently outperform single-clustering baselines, effectively improving the performance of the base deep clustering framework. | https://openaccess.thecvf.com/content/CVPR2023/papers/Metaxas_DivClust_Controlling_Diversity_in_Deep_Clustering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Metaxas_DivClust_Controlling_Diversity_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01042 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Metaxas_DivClust_Controlling_Diversity_in_Deep_Clustering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Metaxas_DivClust_Controlling_Diversity_in_Deep_Clustering_CVPR_2023_paper.html | CVPR 2023 | null |
CAPE: Camera View Position Embedding for Multi-View 3D Object Detection | Kaixin Xiong, Shi Gong, Xiaoqing Ye, Xiao Tan, Ji Wan, Errui Ding, Jingdong Wang, Xiang Bai | In this paper, we address the problem of detecting 3D objects from multi-view images. Current query-based methods rely on global 3D position embeddings (PE) to learn the geometric correspondence between images and 3D space. We claim that directly interacting 2D image features with global 3D PE could increase the difficulty of learning view transformation due to the variation of camera extrinsics. Thus we propose a novel method based on CAmera view Position Embedding, called CAPE. We form the 3D position embeddings under the local camera-view coordinate system instead of the global coordinate system, such that 3D position embedding is free of encoding camera extrinsic parameters. Furthermore, we extend our CAPE to temporal modeling by exploiting the object queries of previous frames and encoding the ego motion for boosting 3D object detection. CAPE achieves the state-of-the-art performance (61.0% NDS and 52.5% mAP) among all LiDAR-free methods on standard nuScenes dataset. Codes and models are available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiong_CAPE_Camera_View_Position_Embedding_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.10209 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_CAPE_Camera_View_Position_Embedding_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_CAPE_Camera_View_Position_Embedding_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Train-Once-for-All Personalization | Hong-You Chen, Yandong Li, Yin Cui, Mingda Zhang, Wei-Lun Chao, Li Zhang | We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e.g., for accurately classifying different subsets of objects. One baseline approach is to train a "generic" model for classifying a wide range of objects, followed by class selection. In our experiments, we however found it suboptimal, perhaps because the model's weights are kept frozen without being personalized. To address this drawback, we propose Train-once-for-All PERsonalization (TAPER), a framework that is trained just once and can later customize a model for different end-users given their task descriptions. TAPER learns a set of "basis" models and a mixer predictor, such that given the task description, the weights (not the predictions!) of the basis models can be on the fly combined into a single "personalized" model. Via extensive experiments on multiple recognition tasks, we show that TAPER consistently outperforms the baseline methods in achieving a higher personalized accuracy. Moreover, we show that TAPER can synthesize a much smaller model to achieve comparable performance to a huge generic model, making it "deployment-friendly" to resource-limited end devices. Interestingly, even without end-users' task descriptions, TAPER can still be specialized to the deployed context based on its past predictions, making it even more "personalization-friendly". | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Train-Once-for-All_Personalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Train-Once-for-All_Personalization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Train-Once-for-All_Personalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Train-Once-for-All_Personalization_CVPR_2023_paper.html | CVPR 2023 | null |
Bi-Directional Distribution Alignment for Transductive Zero-Shot Learning | Zhicai Wang, Yanbin Hao, Tingting Mu, Ouxiang Li, Shuo Wang, Xiangnan He | It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match. Although transductive ZSL (TZSL) attempts to improve this by allowing the use of unlabelled examples from the unseen classes, there is still a high level of distribution shift. We propose a novel TZSL model (named as Bi-VAEGAN), which largely improves the shift by a strengthened distribution alignment between the visual and auxiliary spaces. The key proposal of the model design includes (1) a bi-directional distribution alignment, (2) a simple but effective L_2-norm based feature normalization approach, and (3) a more sophisticated unseen class prior estimation approach. In benchmark evaluation using four datasets, Bi-VAEGAN achieves the new state of the arts under both the standard and generalized TZSL settings. Code could be found at https://github.com/Zhicaiwww/Bi-VAEGAN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Bi-Directional_Distribution_Alignment_for_Transductive_Zero-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Bi-Directional_Distribution_Alignment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08698 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Bi-Directional_Distribution_Alignment_for_Transductive_Zero-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Bi-Directional_Distribution_Alignment_for_Transductive_Zero-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
FlexNeRF: Photorealistic Free-Viewpoint Rendering of Moving Humans From Sparse Views | Vinoj Jayasundara, Amit Agrawal, Nicolas Heron, Abhinav Shrivastava, Larry S. Davis | We present FlexNeRF, a method for photorealistic free-viewpoint rendering of humans in motion from monocular videos. Our approach works well with sparse views, which is a challenging scenario when the subject is exhibiting fast/complex motions. We propose a novel approach which jointly optimizes a canonical time and pose configuration, with a pose-dependent motion field and pose-independent temporal deformations complementing each other. Thanks to our novel temporal and cyclic consistency constraints along with additional losses on intermediate representation such as segmentation, our approach provides high quality outputs as the observed views become sparser. We empirically demonstrate that our method significantly outperforms the state-of-the-art on public benchmark datasets as well as a self-captured fashion dataset. The project page is available at: https://flex-nerf.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jayasundara_FlexNeRF_Photorealistic_Free-Viewpoint_Rendering_of_Moving_Humans_From_Sparse_Views_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jayasundara_FlexNeRF_Photorealistic_Free-Viewpoint_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jayasundara_FlexNeRF_Photorealistic_Free-Viewpoint_Rendering_of_Moving_Humans_From_Sparse_Views_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jayasundara_FlexNeRF_Photorealistic_Free-Viewpoint_Rendering_of_Moving_Humans_From_Sparse_Views_CVPR_2023_paper.html | CVPR 2023 | null |
DIFu: Depth-Guided Implicit Function for Clothed Human Reconstruction | Dae-Young Song, HeeKyung Lee, Jeongil Seo, Donghyeon Cho | Recently, implicit function (IF)-based methods for clothed human reconstruction using a single image have received a lot of attention. Most existing methods rely on a 3D embedding branch using volume such as the skinned multi-person linear (SMPL) model, to compensate for the lack of information in a single image. Beyond the SMPL, which provides skinned parametric human 3D information, in this paper, we propose a new IF-based method, DIFu, that utilizes a projected depth prior containing textured and non-parametric human 3D information. In particular, DIFu consists of a generator, an occupancy prediction network, and a texture prediction network. The generator takes an RGB image of the human front-side as input, and hallucinates the human back-side image. After that, depth maps for front/back images are estimated and projected into 3D volume space. Finally, the occupancy prediction network extracts a pixel-aligned feature and a voxel-aligned feature through a 2D encoder and a 3D encoder, respectively, and estimates occupancy using these features. Note that voxel-aligned features are obtained from the projected depth maps, thus it can contain detailed 3D information such as hair and cloths. Also, colors of each 3D point are also estimated with the texture inference branch. The effectiveness of DIFu is demonstrated by comparing to recent IF-based models quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_DIFu_Depth-Guided_Implicit_Function_for_Clothed_Human_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_DIFu_Depth-Guided_Implicit_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_DIFu_Depth-Guided_Implicit_Function_for_Clothed_Human_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_DIFu_Depth-Guided_Implicit_Function_for_Clothed_Human_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment | Baorui Ma, Junsheng Zhou, Yu-Shen Liu, Zhizhong Han | Neural signed distance functions (SDFs) have shown remarkable capability in representing geometry with details. However, without signed distance supervision, it is still a challenge to infer SDFs from point clouds or multi-view images using neural networks. In this paper, we claim that gradient consistency in the field, indicated by the parallelism of level sets, is the key factor affecting the inference accuracy. Hence, we propose a level set alignment loss to evaluate the parallelism of level sets, which can be minimized to achieve better gradient consistency. Our novelty lies in that we can align all level sets to the zero level set by constraining gradients at queries and their projections on the zero level set in an adaptive way. Our insight is to propagate the zero level set to everywhere in the field through consistent gradients to eliminate uncertainty in the field that is caused by the discreteness of 3D point clouds or the lack of observations from multi-view images. Our proposed loss is a general term which can be used upon different methods to infer SDFs from 3D point clouds and multi-view images. Our numerical and visual comparisons demonstrate that our loss can significantly improve the accuracy of SDFs inferred from point clouds or multi-view images under various benchmarks. Code and data are available at https://github.com/mabaorui/TowardsBetterGradient. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_Towards_Better_Gradient_Consistency_for_Neural_Signed_Distance_Functions_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_Towards_Better_Gradient_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Towards_Better_Gradient_Consistency_for_Neural_Signed_Distance_Functions_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Towards_Better_Gradient_Consistency_for_Neural_Signed_Distance_Functions_via_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style | Fengyin Lin, Mingkang Li, Da Li, Timothy Hospedales, Yi-Zhe Song, Yonggang Qi | This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intra-category, and cross datasets) of ZS-SBIR with just one network ("everything"), and (ii) we would really like to understand how this sketch-photo matching operates ("explainable"). Our key innovation lies with the realization that such a cross-modal matching problem could be reduced to comparisons of groups of key local patches -- akin to the seasoned "bag-of-words" paradigm. Just with this change, we are able to achieve both of the aforementioned goals, with the added benefit of no longer requiring external semantic knowledge. Technically, ours is a transformer-based cross-modal network, with three novel components (i) a self-attention module with a learnable tokenizer to produce visual tokens that correspond to the most informative local regions, (ii) a cross-attention module to compute local correspondences between the visual tokens across two modalities, and finally (iii) a kernel-based relation network to assemble local putative matches and produce an overall similarity metric for a sketch-photo pair. Experiments show ours indeed delivers superior performances across all ZS-SBIR settings. The all important explainable goal is elegantly achieved by visualizing cross-modal token correspondences, and for the first time, via sketch to photo synthesis by universal replacement of all matched photo patches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Zero-Shot_Everything_Sketch-Based_Image_Retrieval_and_in_Explainable_Style_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.14348 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Zero-Shot_Everything_Sketch-Based_Image_Retrieval_and_in_Explainable_Style_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Zero-Shot_Everything_Sketch-Based_Image_Retrieval_and_in_Explainable_Style_CVPR_2023_paper.html | CVPR 2023 | null |
Graph Representation for Order-Aware Visual Transformation | Yue Qiu, Yanjun Sun, Fumiya Matsuzawa, Kenji Iwata, Hirokatsu Kataoka | This paper proposes a new visual reasoning formulation that aims at discovering changes between image pairs and their temporal orders. Recognizing scene dynamics and their chronological orders is a fundamental aspect of human cognition. The aforementioned abilities make it possible to follow step-by-step instructions, reason about and analyze events, recognize abnormal dynamics, and restore scenes to their previous states. However, it remains unclear how well current AI systems perform in these capabilities. Although a series of studies have focused on identifying and describing changes from image pairs, they mainly consider those changes that occur synchronously, thus neglecting potential orders within those changes. To address the above issue, we first propose a visual transformation graph structure for conveying order-aware changes. Then, we benchmarked previous methods on our newly generated dataset and identified the issues of existing methods for change order recognition. Finally, we show a significant improvement in order-aware change recognition by introducing a new model that explicitly associates different changes and then identifies changes and their orders in a graph representation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qiu_Graph_Representation_for_Order-Aware_Visual_Transformation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qiu_Graph_Representation_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_Graph_Representation_for_Order-Aware_Visual_Transformation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_Graph_Representation_for_Order-Aware_Visual_Transformation_CVPR_2023_paper.html | CVPR 2023 | null |
StarCraftImage: A Dataset for Prototyping Spatial Reasoning Methods for Multi-Agent Environments | Sean Kulinski, Nicholas R. Waytowich, James Z. Hare, David I. Inouye | Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e.g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)). StarCraft II game replays encode intelligent (and adversarial) multi-agent behavior and could provide a testbed for these tasks; however, extracting simple and standardized representations for prototyping these tasks is laborious and hinders reproducibility. In contrast, MNIST and CIFAR10, despite their extreme simplicity, have enabled rapid prototyping and reproducibility of ML methods. Following the simplicity of these datasets, we construct a benchmark spatial reasoning dataset based on StarCraft II replays that exhibit complex multi-agent behaviors, while still being as easy to use as MNIST and CIFAR10. Specifically, we carefully summarize a window of 255 consecutive game states to create 3.6 million summary images from 60,000 replays, including all relevant metadata such as game outcome and player races. We develop three formats of decreasing complexity: Hyperspectral images that include one channel for every unit type (similar to multispectral geospatial images), RGB images that mimic CIFAR10, and grayscale images that mimic MNIST. We show how this dataset can be used for prototyping spatial reasoning methods. All datasets, code for extraction, and code for dataset loading can be found at https://starcraftdata.davidinouye.com/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kulinski_StarCraftImage_A_Dataset_for_Prototyping_Spatial_Reasoning_Methods_for_Multi-Agent_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kulinski_StarCraftImage_A_Dataset_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kulinski_StarCraftImage_A_Dataset_for_Prototyping_Spatial_Reasoning_Methods_for_Multi-Agent_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kulinski_StarCraftImage_A_Dataset_for_Prototyping_Spatial_Reasoning_Methods_for_Multi-Agent_CVPR_2023_paper.html | CVPR 2023 | null |
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