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Indiscernible Object Counting in Underwater Scenes | Guolei Sun, Zhaochong An, Yun Liu, Ce Liu, Christos Sakaridis, Deng-Ping Fan, Luc Van Gool | Recently, indiscernible scene understanding has attracted a lot of attention in the vision community. We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings. Due to a lack of appropriate IOC datasets, we present a large-scale dataset IOCfish5K which contains a total of 5,637 high-resolution images and 659,024 annotated center points. Our dataset consists of a large number of indiscernible objects (mainly fish) in underwater scenes, making the annotation process all the more challenging. IOCfish5K is superior to existing datasets with indiscernible scenes because of its larger scale, higher image resolutions, more annotations, and denser scenes. All these aspects make it the most challenging dataset for IOC so far, supporting progress in this area. For benchmarking purposes, we select 14 mainstream methods for object counting and carefully evaluate them on IOCfish5K. Furthermore, we propose IOCFormer, a new strong baseline that combines density and regression branches in a unified framework and can effectively tackle object counting under concealed scenes. Experiments show that IOCFormer achieves state-of-the-art scores on IOCfish5K. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Indiscernible_Object_Counting_in_Underwater_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Indiscernible_Object_Counting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11677 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Indiscernible_Object_Counting_in_Underwater_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Indiscernible_Object_Counting_in_Underwater_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification | Jiawei Feng, Ancong Wu, Wei-Shi Zheng | Due to the modality gap between visible and infrared images with high visual ambiguity, learning diverse modality-shared semantic concepts for visible-infrared person re-identification (VI-ReID) remains a challenging problem. Body shape is one of the significant modality-shared cues for VI-ReID. To dig more diverse modality-shared cues, we expect that erasing body-shape-related semantic concepts in the learned features can force the ReID model to extract more and other modality-shared features for identification. To this end, we propose shape-erased feature learning paradigm that decorrelates modality-shared features in two orthogonal subspaces. Jointly learning shape-related feature in one subspace and shape-erased features in the orthogonal complement achieves a conditional mutual information maximization between shape-erased feature and identity discarding body shape information, thus enhancing the diversity of the learned representation explicitly. Extensive experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Shape-Erased_Feature_Learning_for_Visible-Infrared_Person_Re-Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Shape-Erased_Feature_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04205 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Shape-Erased_Feature_Learning_for_Visible-Infrared_Person_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Shape-Erased_Feature_Learning_for_Visible-Infrared_Person_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
Relational Context Learning for Human-Object Interaction Detection | Sanghyun Kim, Deunsol Jung, Minsu Cho | Recent state-of-the-art methods for HOI detection typically build on transformer architectures with two decoder branches, one for human-object pair detection and the other for interaction classification. Such disentangled transformers, however, may suffer from insufficient context exchange between the branches and lead to a lack of context information for relational reasoning, which is critical in discovering HOI instances. In this work, we propose the multiplex relation network (MUREN) that performs rich context exchange between three decoder branches using unary, pairwise, and ternary relations of human, object, and interaction tokens. The proposed method learns comprehensive relational contexts for discovering HOI instances, achieving state-of-the-art performance on two standard benchmarks for HOI detection, HICO-DET and V-COCO. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Relational_Context_Learning_for_Human-Object_Interaction_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Relational_Context_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04997 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Relational_Context_Learning_for_Human-Object_Interaction_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Relational_Context_Learning_for_Human-Object_Interaction_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Low-Light Image Enhancement via Structure Modeling and Guidance | Xiaogang Xu, Ruixing Wang, Jiangbo Lu | This paper proposes a new framework for low-light image enhancement by simultaneously conducting the appearance as well as structure modeling. It employs the structural feature to guide the appearance enhancement, leading to sharp and realistic results. The structure modeling in our framework is implemented as the edge detection in low-light images. It is achieved with a modified generative model via designing a structure-aware feature extractor and generator. The detected edge maps can accurately emphasize the essential structural information, and the edge prediction is robust towards the noises in dark areas. Moreover, to improve the appearance modeling, which is implemented with a simple U-Net, a novel structure-guided enhancement module is proposed with structure-guided feature synthesis layers. The appearance modeling, edge detector, and enhancement module can be trained end-to-end. The experiments are conducted on representative datasets (sRGB and RAW domains), showing that our model consistently achieves SOTA performance on all datasets with the same architecture. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.05839 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.html | CVPR 2023 | null |
On Calibrating Semantic Segmentation Models: Analyses and an Algorithm | Dongdong Wang, Boqing Gong, Liqiang Wang | We study the problem of semantic segmentation calibration. Lots of solutions have been proposed to approach model miscalibration of confidence in image classification. However, to date, confidence calibration research on semantic segmentation is still limited. We provide a systematic study on the calibration of semantic segmentation models and propose a simple yet effective approach. First, we find that model capacity, crop size, multi-scale testing, and prediction correctness have impact on calibration. Among them, prediction correctness, especially misprediction, is more important to miscalibration due to over-confidence. Next, we propose a simple, unifying, and effective approach, namely selective scaling, by separating correct/incorrect prediction for scaling and more focusing on misprediction logit smoothing. Then, we study popular existing calibration methods and compare them with selective scaling on semantic segmentation calibration. We conduct extensive experiments with a variety of benchmarks on both in-domain and domain-shift calibration and show that selective scaling consistently outperforms other methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_On_Calibrating_Semantic_Segmentation_Models_Analyses_and_an_Algorithm_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_On_Calibrating_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.12053 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_On_Calibrating_Semantic_Segmentation_Models_Analyses_and_an_Algorithm_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_On_Calibrating_Semantic_Segmentation_Models_Analyses_and_an_Algorithm_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Atoms: Pre-Training Vision Transformers With Sinusoidal Waves | Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota | Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViT-Base, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases. | https://openaccess.thecvf.com/content/CVPR2023/papers/Takashima_Visual_Atoms_Pre-Training_Vision_Transformers_With_Sinusoidal_Waves_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Takashima_Visual_Atoms_Pre-Training_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.01112 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Takashima_Visual_Atoms_Pre-Training_Vision_Transformers_With_Sinusoidal_Waves_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Takashima_Visual_Atoms_Pre-Training_Vision_Transformers_With_Sinusoidal_Waves_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Label Compound Expression Recognition: C-EXPR Database & Network | Dimitrios Kollias | Research in automatic analysis of facial expressions mainly focuses on recognising the seven basic ones. However, compound expressions are more diverse and represent the complexity and subtlety of our daily affective displays more accurately. Limited research has been conducted for compound expression recognition (CER), because only a few databases exist, which are small, lab controlled, imbalanced and static. In this paper we present an in-the-wild A/V database, C-EXPR-DB, consisting of 400 videos of 200K frames, annotated in terms of 13 compound expressions, valence-arousal emotion descriptors, action units, speech, facial landmarks and attributes. We also propose C-EXPR-NET, a multi-task learning (MTL) method for CER and AU detection (AU-D); the latter task is introduced to enhance CER performance. For AU-D we incorporate AU semantic description along with visual information. For CER we use a multi-label formulation and the KL-divergence loss. We also propose a distribution matching loss for coupling CER and AU-D tasks to boost their performance and alleviate negative transfer (i.e., when MT model's performance is worse than that of at least one single-task model). An extensive experimental study has been conducted illustrating the excellent performance of C-EXPR-NET, validating the theoretical claims. Finally, C-EXPR-NET is shown to effectively generalize its knowledge in new emotion recognition contexts, in a zero-shot manner. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kollias_Multi-Label_Compound_Expression_Recognition_C-EXPR_Database__Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kollias_Multi-Label_Compound_Expression_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kollias_Multi-Label_Compound_Expression_Recognition_C-EXPR_Database__Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kollias_Multi-Label_Compound_Expression_Recognition_C-EXPR_Database__Network_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Autoencoding Does Not Help Natural Language Supervision at Scale | Floris Weers, Vaishaal Shankar, Angelos Katharopoulos, Yinfei Yang, Tom Gunter | Self supervision and natural language supervision have emerged as two exciting ways to train general purpose image encoders which excel at a variety of downstream tasks. Recent works such as M3AE (Geng et al 2022) and SLIP (Mu et al 2022) have suggested that these approaches can be effectively combined, but most notably their results use small (<20M examples) pre-training datasets and don't effectively reflect the large-scale regime (>100M samples) that is commonly used for these approaches. Here we investigate whether a similar approach can be effective when trained with a much larger amount of data. We find that a combination of two state of the art approaches: masked auto-encoders, MAE (He et al 2021) and contrastive language image pre-training, CLIP (Radford et al 2021) provides a benefit over CLIP when trained on a corpus of 11.3M image-text pairs, but little to no benefit (as evaluated on a suite of common vision tasks) over CLIP when trained on a large corpus of 1.4B images. Our work provides some much needed clarity into the effectiveness (or lack thereof) of self supervision for large-scale image-text training. | https://openaccess.thecvf.com/content/CVPR2023/papers/Weers_Masked_Autoencoding_Does_Not_Help_Natural_Language_Supervision_at_Scale_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Weers_Masked_Autoencoding_Does_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07836 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Weers_Masked_Autoencoding_Does_Not_Help_Natural_Language_Supervision_at_Scale_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Weers_Masked_Autoencoding_Does_Not_Help_Natural_Language_Supervision_at_Scale_CVPR_2023_paper.html | CVPR 2023 | null |
CORA: Adapting CLIP for Open-Vocabulary Detection With Region Prompting and Anchor Pre-Matching | Xiaoshi Wu, Feng Zhu, Rui Zhao, Hongsheng Li | Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incorporating these models into detector training: (1) the distribution mismatch that happens when applying a VL-model trained on whole images to region recognition tasks; (2) the difficulty of localizing objects of unseen classes. To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching. Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier. Anchor pre-matching helps learning generalizable object localization by a class-aware matching mechanism. We evaluate CORA on the COCO OVD benchmark, where we achieve 41.7 AP50 on novel classes, which outperforms the previous SOTA by 2.4 AP50 even without resorting to extra training data. When extra training data is available, we train CORA+ on both ground-truth base-category annotations and additional pseudo bounding box labels computed by CORA. CORA+ achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark. The code is available at https://github.com/tgxs002/CORA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13076 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_paper.html | CVPR 2023 | null |
3DAvatarGAN: Bridging Domains for Personalized Editable Avatars | Rameen Abdal, Hsin-Ying Lee, Peihao Zhu, Menglei Chai, Aliaksandr Siarohin, Peter Wonka, Sergey Tulyakov | Modern 3D-GANs synthesize geometry and texture by training on large-scale datasets with a consistent structure. Training such models on stylized, artistic data, with often unknown, highly variable geometry, and camera information has not yet been shown possible. Can we train a 3D GAN on such artistic data, while maintaining multi-view consistency and texture quality? To this end, we propose an adaptation framework, where the source domain is a pre-trained 3D-GAN, while the target domain is a 2D-GAN trained on artistic datasets. We, then, distill the knowledge from a 2D generator to the source 3D generator. To do that, we first propose an optimization-based method to align the distributions of camera parameters across domains. Second, we propose regularizations necessary to learn high-quality texture, while avoiding degenerate geometric solutions, such as flat shapes. Third, we show a deformation-based technique for modeling exaggerated geometry of artistic domains, enabling---as a byproduct---personalized geometric editing. Finally, we propose a novel inversion method for 3D-GANs linking the latent spaces of the source and the target domains. Our contributions---for the first time---allow for the generation, editing, and animation of personalized artistic 3D avatars on artistic datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Abdal_3DAvatarGAN_Bridging_Domains_for_Personalized_Editable_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Abdal_3DAvatarGAN_Bridging_Domains_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02700 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Abdal_3DAvatarGAN_Bridging_Domains_for_Personalized_Editable_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Abdal_3DAvatarGAN_Bridging_Domains_for_Personalized_Editable_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
Physics-Driven Diffusion Models for Impact Sound Synthesis From Videos | Kun Su, Kaizhi Qian, Eli Shlizerman, Antonio Torralba, Chuang Gan | Modeling sounds emitted from physical object interactions is critical for immersive perceptual experiences in real and virtual worlds. Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound. However, they require fine details of both the object geometries and impact locations, which are rarely available in the real world and can not be applied to synthesize impact sounds from common videos. On the other hand, existing video-driven deep learning-based approaches could only capture the weak correspondence between visual content and impact sounds since they lack of physics knowledge. In this work, we propose a physics-driven diffusion model that can synthesize high-fidelity impact sound for a silent video clip. In addition to the video content, we propose to use additional physics priors to guide the impact sound synthesis procedure. The physics priors include both physics parameters that are directly estimated from noisy real-world impact sound examples without sophisticated setup and learned residual parameters that interpret the sound environment via neural networks. We further implement a novel diffusion model with specific training and inference strategies to combine physics priors and visual information for impact sound synthesis. Experimental results show that our model outperforms several existing systems in generating realistic impact sounds. More importantly, the physics-based representations are fully interpretable and transparent, thus enabling us to perform sound editing flexibly. We encourage the readers to visit our project page to watch demo videos with audio turned on to experience the results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Su_Physics-Driven_Diffusion_Models_for_Impact_Sound_Synthesis_From_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Su_Physics-Driven_Diffusion_Models_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.16897 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Su_Physics-Driven_Diffusion_Models_for_Impact_Sound_Synthesis_From_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Su_Physics-Driven_Diffusion_Models_for_Impact_Sound_Synthesis_From_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Transductive Few-Shot Learning With Prototype-Based Label Propagation by Iterative Graph Refinement | Hao Zhu, Piotr Koniusz | Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared with inductive few-shot learning, transductive models typically perform better as they leverage all samples of the query set. The two existing classes of methods, prototype-based and graph-based, have the disadvantages of inaccurate prototype estimation and sub-optimal graph construction with kernel functions, respectively. %, which hurt the performance. In this paper, we propose a novel prototype-based label propagation to solve these issues. Specifically, our graph construction is based on the relation between prototypes and samples rather than between samples. As prototypes are being updated, the graph changes.We also estimate the label of each prototype instead of considering a prototype be the class centre. On mini-ImageNet, tiered-ImageNet, CIFAR-FS and CUB datasets, we show the proposed method outperforms other state-of-the-art methods in transductive FSL and semi-supervised FSL when some unlabeled data accompanies the novel few-shot task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Transductive_Few-Shot_Learning_With_Prototype-Based_Label_Propagation_by_Iterative_Graph_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Transductive_Few-Shot_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11598 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Transductive_Few-Shot_Learning_With_Prototype-Based_Label_Propagation_by_Iterative_Graph_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Transductive_Few-Shot_Learning_With_Prototype-Based_Label_Propagation_by_Iterative_Graph_CVPR_2023_paper.html | CVPR 2023 | null |
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection | Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan | Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignoring the explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose region-to-region correlation modules to economically model inter-image relations for pixel-wise segmentation features. Then, we use two types of predefined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token and co-saliency token-to-token correlation modules. We also design a token-guided feature refinement module to enhance the discriminability of the segmentation features under the guidance of the learned tokens. We perform iterative mutual promotion for the segmentation feature extraction and token construction. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method. The source code is available at: https://github.com/dragonlee258079/DMT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Discriminative_Co-Saliency_and_Background_Mining_Transformer_for_Co-Salient_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Discriminative_Co-Saliency_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.00514 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Discriminative_Co-Saliency_and_Background_Mining_Transformer_for_Co-Salient_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Discriminative_Co-Saliency_and_Background_Mining_Transformer_for_Co-Salient_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations | Hagay Michaeli, Tomer Michaeli, Daniel Soudry | Although CNNs are believed to be invariant to translations, recent works have shown this is not the case due to aliasing effects that stem from down-sampling layers. The existing architectural solutions to prevent the aliasing effects are partial since they do not solve those effects that originate in non-linearities. We propose an extended anti-aliasing method that tackles both down-sampling and non-linear layers, thus creating truly alias-free, shift-invariant CNNs. We show that the presented model is invariant to integer as well as fractional (i.e., sub-pixel) translations, thus outperforming other shift-invariant methods in terms of robustness to adversarial translations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Michaeli_Alias-Free_Convnets_Fractional_Shift_Invariance_via_Polynomial_Activations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Michaeli_Alias-Free_Convnets_Fractional_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08085 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Michaeli_Alias-Free_Convnets_Fractional_Shift_Invariance_via_Polynomial_Activations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Michaeli_Alias-Free_Convnets_Fractional_Shift_Invariance_via_Polynomial_Activations_CVPR_2023_paper.html | CVPR 2023 | null |
Binary Latent Diffusion | Ze Wang, Jiang Wang, Zicheng Liu, Qiang Qiu | In this paper, we show that a binary latent space can be explored for compact yet expressive image representations. We model the bi-directional mappings between an image and the corresponding latent binary representation by training an auto-encoder with a Bernoulli encoding distribution. On the one hand, the binary latent space provides a compact discrete image representation of which the distribution can be modeled more efficiently than pixels or continuous latent representations. On the other hand, we now represent each image patch as a binary vector instead of an index of a learned cookbook as in discrete image representations with vector quantization. In this way, we obtain binary latent representations that allow for better image quality and high-resolution image representations without any multi-stage hierarchy in the latent space. In this binary latent space, images can now be generated effectively using a binary latent diffusion model tailored specifically for modeling the prior over the binary image representations. We present both conditional and unconditional image generation experiments with multiple datasets, and show that the proposed method performs comparably to state-of-the-art methods while dramatically improving the sampling efficiency to as few as 16 steps without using any test-time acceleration. The proposed framework can also be seamlessly scaled to 1024 x 1024 high-resolution image generation without resorting to latent hierarchy or multi-stage refinements. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Binary_Latent_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Binary_Latent_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04820 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Binary_Latent_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Binary_Latent_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Person Image Synthesis via Denoising Diffusion Model | Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Jorma Laaksonen, Mubarak Shah, Fahad Shahbaz Khan | The pose-guided person image generation task requires synthesizing photorealistic images of humans in arbitrary poses. The existing approaches use generative adversarial networks that do not necessarily maintain realistic textures or need dense correspondences that struggle to handle complex deformations and severe occlusions. In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution. Our proposed Person Image Diffusion Model (PIDM) disintegrates the complex transfer problem into a series of simpler forward-backward denoising steps. This helps in learning plausible source-to-target transformation trajectories that result in faithful textures and undistorted appearance details. We introduce a 'texture diffusion module' based on cross-attention to accurately model the correspondences between appearance and pose information available in source and target images. Further, we propose 'disentangled classifier-free guidance' to ensure close resemblance between the conditional inputs and the synthesized output in terms of both pose and appearance information. Our extensive results on two large-scale benchmarks and a user study demonstrate the photorealism of our proposed approach under challenging scenarios. We also show how our generated images can help in downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bhunia_Person_Image_Synthesis_via_Denoising_Diffusion_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bhunia_Person_Image_Synthesis_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12500 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bhunia_Person_Image_Synthesis_via_Denoising_Diffusion_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bhunia_Person_Image_Synthesis_via_Denoising_Diffusion_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations | Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek | While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing can be overinterpreted if regarded as a primary criterion for selecting or discarding explanation methods. To address shortcomings of this test, we start by observing an experimental gap in the ranking of explanation methods between randomization-based sanity checks [1] and model output faithfulness measures (e.g. [20]). We identify limitations of model-randomization-based sanity checks for the purpose of evaluating explanations. Firstly, we show that uninformative attribution maps created with zero pixel-wise covariance easily achieve high scores in this type of checks. Secondly, we show that top-down model randomization preserves scales of forward pass activations with high probability. That is, channels with large activations have a high probility to contribute strongly to the output, even after randomization of the network on top of them. Hence, explanations after randomization can only be expected to differ to a certain extent. This explains the observed experimental gap. In summary, these results demonstrate the inadequacy of model-randomization-based sanity checks as a criterion to rank attribution methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Binder_Shortcomings_of_Top-Down_Randomization-Based_Sanity_Checks_for_Evaluations_of_Deep_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Binder_Shortcomings_of_Top-Down_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12486 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Binder_Shortcomings_of_Top-Down_Randomization-Based_Sanity_Checks_for_Evaluations_of_Deep_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Binder_Shortcomings_of_Top-Down_Randomization-Based_Sanity_Checks_for_Evaluations_of_Deep_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Part Priors: Learning To Optimize Part-Based Object Completion in RGB-D Scans | Aleksei Bokhovkin, Angela Dai | 3D scene understanding has seen significant advances in recent years, but has largely focused on object understanding in 3D scenes with independent per-object predictions. We thus propose to learn Neural Part Priors (NPPs), parametric spaces of objects and their parts, that enable optimizing to fit to a new input 3D scan geometry with global scene consistency constraints. The rich structure of our NPPs enables accurate, holistic scene reconstruction across similar objects in the scene. Both objects and their part geometries are characterized by coordinate field MLPs, facilitating optimization at test time to fit to input geometric observations as well as similar objects in the input scan. This enables more accurate reconstructions than independent per-object predictions as a single forward pass, while establishing global consistency within a scene. Experiments on the ScanNet dataset demonstrate that NPPs significantly outperforms the state-of-the-art in part decomposition and object completion in real-world scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bokhovkin_Neural_Part_Priors_Learning_To_Optimize_Part-Based_Object_Completion_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bokhovkin_Neural_Part_Priors_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bokhovkin_Neural_Part_Priors_Learning_To_Optimize_Part-Based_Object_Completion_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bokhovkin_Neural_Part_Priors_Learning_To_Optimize_Part-Based_Object_Completion_in_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Assignment for Geometry Aware Local Feature Matching | Dihe Huang, Ying Chen, Yong Liu, Jianlin Liu, Shang Xu, Wenlong Wu, Yikang Ding, Fan Tang, Chengjie Wang | The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale and viewpoint variations, due to the geometric inconsistency resulting from the application of the mutual nearest neighbour criterion (i.e., one-to-one assignment) in patch-level matching. Accordingly, we introduce AdaMatcher, which first accomplishes the feature correlation and co-visible area estimation through an elaborate feature interaction module, then performs adaptive assignment on patch-level matching while estimating the scales between images, and finally refines the co-visible matches through scale alignment and sub-pixel regression module. Extensive experiments show that AdaMatcher outperforms solid baselines and achieves state-of-the-art results on many downstream tasks. Additionally, the adaptive assignment and sub-pixel refinement module can be used as a refinement network for other matching methods, such as SuperGlue, to boost their performance further. The code will be publicly available at https://github.com/AbyssGaze/AdaMatcher. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Adaptive_Assignment_for_Geometry_Aware_Local_Feature_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Adaptive_Assignment_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.08427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Adaptive_Assignment_for_Geometry_Aware_Local_Feature_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Adaptive_Assignment_for_Geometry_Aware_Local_Feature_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Initialization Noise in Image Gradients and Saliency Maps | Ann-Christin Woerl, Jan Disselhoff, Michael Wand | In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate considerably with training randomness, such as the random initialization of the networks. We extend our study to gradients of intermediate layers, obtained via GradCAM, as well as popular network saliency estimators such as DeepLIFT, SHAP, LIME, Integrated Gradients, and SmoothGrad. While empirical noise levels vary, qualitatively different attributions to image features are still possible with all of these, which comes with implications for interpreting such attributions, in particular when seeking data-driven explanations of the phenomenon generating the data. Finally, we demonstrate that the observed artefacts can be removed by marginalization over the initialization distribution by simple stochastic integration. | https://openaccess.thecvf.com/content/CVPR2023/papers/Woerl_Initialization_Noise_in_Image_Gradients_and_Saliency_Maps_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Woerl_Initialization_Noise_in_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Woerl_Initialization_Noise_in_Image_Gradients_and_Saliency_Maps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Woerl_Initialization_Noise_in_Image_Gradients_and_Saliency_Maps_CVPR_2023_paper.html | CVPR 2023 | null |
FLAG3D: A 3D Fitness Activity Dataset With Language Instruction | Yansong Tang, Jinpeng Liu, Aoyang Liu, Bin Yang, Wenxun Dai, Yongming Rao, Jiwen Lu, Jie Zhou, Xiu Li | With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and diverse environments. In this paper, we present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Extensive experiments and in-depth analysis show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation. Our dataset and source code are publicly available at https://andytang15.github.io/FLAG3D. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_FLAG3D_A_3D_Fitness_Activity_Dataset_With_Language_Instruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_FLAG3D_A_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_FLAG3D_A_3D_Fitness_Activity_Dataset_With_Language_Instruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_FLAG3D_A_3D_Fitness_Activity_Dataset_With_Language_Instruction_CVPR_2023_paper.html | CVPR 2023 | null |
Implicit Neural Head Synthesis via Controllable Local Deformation Fields | Chuhan Chen, Matthew O’Toole, Gaurav Bharaj, Pablo Garrido | High-quality reconstruction of controllable 3D head avatars from 2D videos is highly desirable for virtual human applications in movies, games, and telepresence. Neural implicit fields provide a powerful representation to model 3D head avatars with personalized shape, expressions, and facial parts, e.g., hair and mouth interior, that go beyond the linear 3D morphable model (3DMM). However, existing methods do not model faces with fine-scale facial features, or local control of facial parts that extrapolate asymmetric expressions from monocular videos. Further, most condition only on 3DMM parameters with poor(er) locality, and resolve local features with a global neural field. We build on part-based implicit shape models that decompose a global deformation field into local ones. Our novel formulation models multiple implicit deformation fields with local semantic rig-like control via 3DMM-based parameters, and representative facial landmarks. Further, we propose a local control loss and attention mask mechanism that promote sparsity of each learned deformation field. Our formulation renders sharper locally controllable nonlinear deformations than previous implicit monocular approaches, especially mouth interior, asymmetric expressions, and facial details. Project page:https://imaging.cs.cmu.edu/local_deformation_fields/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Implicit_Neural_Head_Synthesis_via_Controllable_Local_Deformation_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Implicit_Neural_Head_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.11113 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Implicit_Neural_Head_Synthesis_via_Controllable_Local_Deformation_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Implicit_Neural_Head_Synthesis_via_Controllable_Local_Deformation_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralUDF: Learning Unsigned Distance Fields for Multi-View Reconstruction of Surfaces With Arbitrary Topologies | Xiaoxiao Long, Cheng Lin, Lingjie Liu, Yuan Liu, Peng Wang, Christian Theobalt, Taku Komura, Wenping Wang | We present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies from 2D images via volume rendering. Recent advances in neural rendering based reconstruction have achieved compelling results. However, these methods are limited to objects with closed surfaces since they adopt Signed Distance Function (SDF) as surface representation which requires the target shape to be divided into inside and outside. In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation. Specifically, a new density function that correlates the property of UDF with the volume rendering scheme is introduced for robust optimization of the UDF fields. Experiments on the DTU and DeepFashion3D datasets show that our method not only enables high-quality reconstruction of non-closed shapes with complex typologies, but also achieves comparable performance to the SDF based methods on the reconstruction of closed surfaces. Visit our project page at https://www.xxlong.site/NeuralUDF/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Long_NeuralUDF_Learning_Unsigned_Distance_Fields_for_Multi-View_Reconstruction_of_Surfaces_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Long_NeuralUDF_Learning_Unsigned_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14173 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Long_NeuralUDF_Learning_Unsigned_Distance_Fields_for_Multi-View_Reconstruction_of_Surfaces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Long_NeuralUDF_Learning_Unsigned_Distance_Fields_for_Multi-View_Reconstruction_of_Surfaces_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision | Siyuan Yan, Zhen Yu, Xuelin Zhang, Dwarikanath Mahapatra, Shekhar S. Chandra, Monika Janda, Peter Soyer, Zongyuan Ge | Deep neural networks have demonstrated promising performance on image recognition tasks. However, they may heavily rely on confounding factors, using irrelevant artifacts or bias within the dataset as the cue to improve performance. When a model performs decision-making based on these spurious correlations, it can become untrustable and lead to catastrophic outcomes when deployed in the real-world scene. In this paper, we explore and try to solve this problem in the context of skin cancer diagnosis. We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen. Specifically, our method can automatically discover confounding factors by analyzing the co-occurrence behavior of the samples. It is capable of learning confounding concepts using easily obtained concept exemplars. By mapping the blackbox model's feature representation onto an explainable concept space, human users can interpret the concept and intervene via first order-logic instruction. We systematically evaluate our method on our newly crafted, well-controlled skin lesion dataset and several public skin lesion datasets. Experiments show that our method can effectively detect and remove confounding factors from datasets without any prior knowledge about the category distribution and does not require fully annotated concept labels. We also show that our method enables the model to focus on clinicalrelated concepts, improving the model's performance and trustworthiness during model inference. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_Towards_Trustable_Skin_Cancer_Diagnosis_via_Rewriting_Models_Decision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_Towards_Trustable_Skin_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Towards_Trustable_Skin_Cancer_Diagnosis_via_Rewriting_Models_Decision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Towards_Trustable_Skin_Cancer_Diagnosis_via_Rewriting_Models_Decision_CVPR_2023_paper.html | CVPR 2023 | null |
Curricular Object Manipulation in LiDAR-Based Object Detection | Ziyue Zhu, Qiang Meng, Xiao Wang, Ke Wang, Liujiang Yan, Jian Yang | This paper explores the potential of curriculum learning in LiDAR-based 3D object detection by proposing a curricular object manipulation (COM) framework. The framework embeds the curricular training strategy into both the loss design and the augmentation process. For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages. On top of the widely-used augmentation technique called GT-Aug in LiDAR detection tasks, we propose a novel COMAug strategy which first clusters objects in ground-truth database based on well-designed heuristics. Group-level difficulties rather than individual ones are then predicted and updated during training for stable results. Model performance and generalization capabilities can be improved by sampling and augmenting progressively more difficult objects into the training points. Extensive experiments and ablation studies reveal the superior and generality of the proposed framework. The code is available at https://github.com/ZZY816/COM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Curricular_Object_Manipulation_in_LiDAR-Based_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Curricular_Object_Manipulation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04248 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Curricular_Object_Manipulation_in_LiDAR-Based_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Curricular_Object_Manipulation_in_LiDAR-Based_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding | Zihang Lin, Chaolei Tan, Jian-Fang Hu, Zhi Jin, Tiancai Ye, Wei-Shi Zheng | Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatio-temporal visual-linguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intra-frame visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall framework achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Collaborative_Static_and_Dynamic_Vision-Language_Streams_for_Spatio-Temporal_Video_Grounding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Collaborative_Static_and_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Collaborative_Static_and_Dynamic_Vision-Language_Streams_for_Spatio-Temporal_Video_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Collaborative_Static_and_Dynamic_Vision-Language_Streams_for_Spatio-Temporal_Video_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
Shape-Constraint Recurrent Flow for 6D Object Pose Estimation | Yang Hai, Rui Song, Jiaojiao Li, Yinlin Hu | Most recent 6D object pose estimation methods rely on 2D optical flow networks to refine their results. However, these optical flow methods typically do not consider any 3D shape information of the targets during matching, making them suffer in 6D object pose estimation. In this work, we propose a shape-constraint recurrent flow network for 6D object pose estimation, which embeds the 3D shape information of the targets into the matching procedure. We first introduce a flow-to-pose component to learn an intermediate pose from the current flow estimation, then impose a shape constraint from the current pose on the lookup space of the 4D correlation volume for flow estimation, which reduces the matching space significantly and is much easier to learn. Finally, we optimize the flow and pose simultaneously in a recurrent manner until convergence. We evaluate our method on three challenging 6D object pose datasets and show that it outperforms the state of the art in both accuracy and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hai_Shape-Constraint_Recurrent_Flow_for_6D_Object_Pose_Estimation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hai_Shape-Constraint_Recurrent_Flow_for_6D_Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hai_Shape-Constraint_Recurrent_Flow_for_6D_Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER | Ce Zheng, Matias Mendieta, Taojiannan Yang, Guo-Jun Qi, Chen Chen | Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose estimation (3D HPE), and human mesh reconstruction (HMR) tasks. In these tasks, feature map representations of the human structural information are often extracted first from the image by a CNN (such as HRNet), and then further processed by transformer to predict the heatmaps (encodes each joint's location into a feature map with a Gaussian distribution) for HPE or HMR. However, existing transformer architectures are not able to process these feature map inputs directly, forcing an unnatural flattening of the location-sensitive human structural information. Furthermore, much of the performance benefit in recent HPE and HMR methods has come at the cost of ever-increasing computation and memory needs. Therefore, to simultaneously address these problems, we propose FeatER, a novel transformer design which preserves the inherent structure of feature map representations when modeling attention while reducing the memory and computational costs. Taking advantage of FeatER, we build an efficient network for a set of human reconstruction tasks including 2D HPE, 3D HPE, and HMR. A feature map reconstruction module is applied to improve the performance of the estimated human pose and mesh. Extensive experiments demonstrate the effectiveness of FeatER on various human pose and mesh datasets. For instance, FeatER outperforms the SOTA method MeshGraphormer by requiring 5% of Params (total parameters) and 16% of MACs (the Multiply-Accumulate Operations) on Human3.6M and 3DPW datasets. Code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_FeatER_An_Efficient_Network_for_Human_Reconstruction_via_Feature_Map-Based_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_FeatER_An_Efficient_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.15448 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_FeatER_An_Efficient_Network_for_Human_Reconstruction_via_Feature_Map-Based_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_FeatER_An_Efficient_Network_for_Human_Reconstruction_via_Feature_Map-Based_CVPR_2023_paper.html | CVPR 2023 | null |
Micron-BERT: BERT-Based Facial Micro-Expression Recognition | Xuan-Bac Nguyen, Chi Nhan Duong, Xin Li, Susan Gauch, Han-Seok Seo, Khoa Luu | Micro-expression recognition is one of the most challenging topics in affective computing. It aims to recognize tiny facial movements difficult for humans to perceive in a brief period, i.e., 0.25 to 0.5 seconds. Recent advances in pre-training deep Bidirectional Transformers (BERT) have significantly improved self-supervised learning tasks in computer vision. However, the standard BERT in vision problems is designed to learn only from full images or videos, and the architecture cannot accurately detect details of facial micro-expressions. This paper presents Micron-BERT (u-BERT), a novel approach to facial micro-expression recognition. The proposed method can automatically capture these movements in an unsupervised manner based on two key ideas. First, we employ Diagonal Micro-Attention (DMA) to detect tiny differences between two frames. Second, we introduce a new Patch of Interest (PoI) module to localize and highlight micro-expression interest regions and simultaneously reduce noisy backgrounds and distractions. By incorporating these components into an end-to-end deep network, the proposed u-BERT significantly outperforms all previous work in various micro-expression tasks. u-BERT can be trained on a large-scale unlabeled dataset, i.e., up to 8 million images, and achieves high accuracy on new unseen facial micro-expression datasets. Empirical experiments show u-BERT consistently outperforms state-of-the-art performance on four micro-expression benchmarks, including SAMM, CASME II, SMIC, and CASME3, by significant margins. Code will be available at https://github.com/uark-cviu/Micron-BERT | https://openaccess.thecvf.com/content/CVPR2023/papers/Nguyen_Micron-BERT_BERT-Based_Facial_Micro-Expression_Recognition_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Micron-BERT_BERT-Based_Facial_Micro-Expression_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Micron-BERT_BERT-Based_Facial_Micro-Expression_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Residual Degradation Learning Unfolding Framework With Mixing Priors Across Spectral and Spatial for Compressive Spectral Imaging | Yubo Dong, Dahua Gao, Tian Qiu, Yuyan Li, Minxi Yang, Guangming Shi | To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfolding methods achieve good performance. However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion; in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors. In this paper, we propose a Residual Degradation Learning Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix and the degradation process. Moreover, a MixS2 Transformer is designed via mixing priors across spectral and spatial to strengthen the spectral-spatial representation capability. Finally, plugging the MixS2 Transformer into the RDLUF leads to an end-to-end trainable and interpretable neural network RDLUF-MixS2. Experimental results establish the superior performance of the proposed method over existing ones. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Residual_Degradation_Learning_Unfolding_Framework_With_Mixing_Priors_Across_Spectral_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Residual_Degradation_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.06891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Residual_Degradation_Learning_Unfolding_Framework_With_Mixing_Priors_Across_Spectral_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Residual_Degradation_Learning_Unfolding_Framework_With_Mixing_Priors_Across_Spectral_CVPR_2023_paper.html | CVPR 2023 | null |
Visibility Constrained Wide-Band Illumination Spectrum Design for Seeing-in-the-Dark | Muyao Niu, Zhuoxiao Li, Zhihang Zhong, Yinqiang Zheng | Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://github.com/MyNiuuu/VCSD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Niu_Visibility_Constrained_Wide-Band_Illumination_Spectrum_Design_for_Seeing-in-the-Dark_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.11642 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Niu_Visibility_Constrained_Wide-Band_Illumination_Spectrum_Design_for_Seeing-in-the-Dark_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Niu_Visibility_Constrained_Wide-Band_Illumination_Spectrum_Design_for_Seeing-in-the-Dark_CVPR_2023_paper.html | CVPR 2023 | null |
PanelNet: Understanding 360 Indoor Environment via Panel Representation | Haozheng Yu, Lu He, Bing Jian, Weiwei Feng, Shan Liu | Indoor 360 panoramas have two essential properties. (1) The panoramas are continuous and seamless in the horizontal direction. (2) Gravity plays an important role in indoor environment design. By leveraging these properties, we present PanelNet, a framework that understands indoor environments using a novel panel representation of 360 images. We represent an equirectangular projection (ERP) as consecutive vertical panels with corresponding 3D panel geometry. To reduce the negative impact of panoramic distortion, we incorporate a panel geometry embedding network that encodes both the local and global geometric features of a panel. To capture the geometric context in room design, we introduce Local2Global Transformer, which aggregates local information within a panel and panel-wise global context. It greatly improves the model performance with low training overhead. Our method outperforms existing methods on indoor 360 depth estimation and shows competitive results against state-of-the-art approaches on the task of indoor layout estimation and semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_PanelNet_Understanding_360_Indoor_Environment_via_Panel_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_PanelNet_Understanding_360_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_PanelNet_Understanding_360_Indoor_Environment_via_Panel_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_PanelNet_Understanding_360_Indoor_Environment_via_Panel_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking | Yuanpeng Tu, Boshen Zhang, Yuxi Li, Liang Liu, Jian Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Cai Rong Zhao | Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of deep models. The proposed method is simple and flexible, it is tested on both synthetic and real-world noisy datasets, where significant improvements are achieved over previous state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tu_Learning_With_Noisy_Labels_via_Self-Supervised_Adversarial_Noisy_Masking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tu_Learning_With_Noisy_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.06805 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Learning_With_Noisy_Labels_via_Self-Supervised_Adversarial_Noisy_Masking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Learning_With_Noisy_Labels_via_Self-Supervised_Adversarial_Noisy_Masking_CVPR_2023_paper.html | CVPR 2023 | null |
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation | Qihao Liu, Adam Kortylewski, Alan L. Yuille | Human pose and shape (HPS) estimation methods achieve remarkable results. However, current HPS benchmarks are mostly designed to test models in scenarios that are similar to the training data. This can lead to critical situations in real-world applications when the observed data differs significantly from the training data and hence is out-of-distribution (OOD). It is therefore important to test and improve the OOD robustness of HPS methods. To address this fundamental problem, we develop a simulator that can be controlled in a fine-grained manner using interpretable parameters to explore the manifold of images of human pose, e.g. by varying poses, shapes, and clothes. We introduce a learning-based testing method, termed PoseExaminer, that automatically diagnoses HPS algorithms by searching over the parameter space of human pose images to find the failure modes. Our strategy for exploring this high-dimensional parameter space is a multi-agent reinforcement learning system, in which the agents collaborate to explore different parts of the parameter space. We show that our PoseExaminer discovers a variety of limitations in current state-of-the-art models that are relevant in real-world scenarios but are missed by current benchmarks. For example, it finds large regions of realistic human poses that are not predicted correctly, as well as reduced performance for humans with skinny and corpulent body shapes. In addition, we show that fine-tuning HPS methods by exploiting the failure modes found by PoseExaminer improve their robustness and even their performance on standard benchmarks by a significant margin. The code are available for research purposes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_PoseExaminer_Automated_Testing_of_Out-of-Distribution_Robustness_in_Human_Pose_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_PoseExaminer_Automated_Testing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07337 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PoseExaminer_Automated_Testing_of_Out-of-Distribution_Robustness_in_Human_Pose_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PoseExaminer_Automated_Testing_of_Out-of-Distribution_Robustness_in_Human_Pose_and_CVPR_2023_paper.html | CVPR 2023 | null |
GamutMLP: A Lightweight MLP for Color Loss Recovery | Hoang M. Le, Brian Price, Scott Cohen, Michael S. Brown | Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image---the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Le_GamutMLP_A_Lightweight_MLP_for_Color_Loss_Recovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Le_GamutMLP_A_Lightweight_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11743 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Le_GamutMLP_A_Lightweight_MLP_for_Color_Loss_Recovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Le_GamutMLP_A_Lightweight_MLP_for_Color_Loss_Recovery_CVPR_2023_paper.html | CVPR 2023 | null |
Instance-Aware Domain Generalization for Face Anti-Spoofing | Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma | Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot reflect real domain distributions accurately. Besides, such domain-aware methods focus on domain-level alignment, which is not fine-grained enough to ensure that learned representations are insensitive to domain styles. To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels. Specifically, Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles. Concretely, we propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the style-sensitive feature correlation, boosting the generalization. Moreover, Dynamic Kernel Generator and Categorical Style Assembly are proposed to first extract the instance-specific features and then generate the style-diversified features with large style shifts, respectively, further facilitating the learning of style-insensitive features. Extensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code will be publicly available at this link: https://github.com/qianyuzqy/IADG. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Instance-Aware_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.05640 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Instance-Aware_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Instance-Aware_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2023_paper.html | CVPR 2023 | null |
GANHead: Towards Generative Animatable Neural Head Avatars | Sijing Wu, Yichao Yan, Yunhao Li, Yuhao Cheng, Wenhan Zhu, Ke Gao, Xiaobo Li, Guangtao Zhai | To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_GANHead_Towards_Generative_Animatable_Neural_Head_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_GANHead_Towards_Generative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_GANHead_Towards_Generative_Animatable_Neural_Head_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_GANHead_Towards_Generative_Animatable_Neural_Head_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Domain Generalization for Multi-View 3D Object Detection in Bird-Eye-View | Shuo Wang, Xinhai Zhao, Hai-Ming Xu, Zehui Chen, Dameng Yu, Jiahao Chang, Zhen Yang, Feng Zhao | Multi-view 3D object detection (MV3D-Det) in Bird-Eye-View (BEV) has drawn extensive attention due to its low cost and high efficiency. Although new algorithms for camera-only 3D object detection have been continuously proposed, most of them may risk drastic performance degradation when the domain of input images differs from that of training. In this paper, we first analyze the causes of the domain gap for the MV3D-Det task. Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation. To acquire a robust depth prediction, we propose to decouple the depth estimation from the intrinsic parameters of the camera (i.e. the focal length) through converting the prediction of metric depth to that of scale-invariant depth and perform dynamic perspective augmentation to increase the diversity of the extrinsic parameters (i.e. the camera poses) by utilizing homography. Moreover, we modify the focal length values to create multiple pseudo-domains and construct an adversarial training loss to encourage the feature representation to be more domain-agnostic. Without bells and whistles, our approach, namely DG-BEV, successfully alleviates the performance drop on the unseen target domain without impairing the accuracy of the source domain. Extensive experiments on Waymo, nuScenes, and Lyft, demonstrate the generalization and effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Towards_Domain_Generalization_for_Multi-View_3D_Object_Detection_in_Bird-Eye-View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Towards_Domain_Generalization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01686 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Domain_Generalization_for_Multi-View_3D_Object_Detection_in_Bird-Eye-View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Domain_Generalization_for_Multi-View_3D_Object_Detection_in_Bird-Eye-View_CVPR_2023_paper.html | CVPR 2023 | null |
Robust and Scalable Gaussian Process Regression and Its Applications | Yifan Lu, Jiayi Ma, Leyuan Fang, Xin Tian, Junjun Jiang | This paper introduces a robust and scalable Gaussian process regression (GPR) model via variational learning. This enables the application of Gaussian processes to a wide range of real data, which are often large-scale and contaminated by outliers. Towards this end, we employ a mixture likelihood model where outliers are assumed to be sampled from a uniform distribution. We next derive a variational formulation that jointly infers the mode of data, i.e., inlier or outlier, as well as hyperparameters by maximizing a lower bound of the true log marginal likelihood. Compared to previous robust GPR, our formulation approximates the exact posterior distribution. The inducing variable approximation and stochastic variational inference are further introduced to our variational framework, extending our model to large-scale data. We apply our model to two challenging real-world applications, namely feature matching and dense gene expression imputation. Extensive experiments demonstrate the superiority of our model in terms of robustness and speed. Notably, when matching 4k feature points, its inference is completed in milliseconds with almost no false matches. The code is at https://github.com/YifanLu2000/Robust-Scalable-GPR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Robust_and_Scalable_Gaussian_Process_Regression_and_Its_Applications_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_Robust_and_Scalable_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Robust_and_Scalable_Gaussian_Process_Regression_and_Its_Applications_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Robust_and_Scalable_Gaussian_Process_Regression_and_Its_Applications_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU Supervision | Qi Ming, Lingjuan Miao, Zhe Ma, Lin Zhao, Zhiqiang Zhou, Xuhui Huang, Yuanpei Chen, Yufei Guo | Intersection-over-Union (IoU) is the most popular metric to evaluate regression performance in 3D object detection. Recently, there are also some methods applying IoU to the optimization of 3D bounding box regression. However, we demonstrate through experiments and mathematical proof that the 3D IoU loss suffers from abnormal gradient w.r.t. angular error and object scale, which further leads to slow convergence and suboptimal regression process, respectively. In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression. Specifically, a gradient correction strategy is designed to endow 3D IoU loss with a reasonable gradient. It ensures that the model converges quickly in the early stage of training, and helps to achieve fine-grained refinement of bounding boxes in the later stage. To solve suboptimal regression of 3D IoU loss for objects at different scales, we introduce a gradient rescaling strategy to adaptively optimize the step size. Finally, we integrate GCIoU Loss into multiple models to achieve stable performance gains and faster model convergence. Experiments on KITTI dataset demonstrate superiority of the proposed method. The code is available at https://github.com/ming71/GCIoU-loss. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ming_Deep_Dive_Into_Gradients_Better_Optimization_for_3D_Object_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ming_Deep_Dive_Into_Gradients_Better_Optimization_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ming_Deep_Dive_Into_Gradients_Better_Optimization_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Doubly Right Object Recognition: A Why Prompt for Visual Rationales | Chengzhi Mao, Revant Teotia, Amrutha Sundar, Sachit Menon, Junfeng Yang, Xin Wang, Carl Vondrick | Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a "doubly right" object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a "why prompt," which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mao_Doubly_Right_Object_Recognition_A_Why_Prompt_for_Visual_Rationales_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.06202 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mao_Doubly_Right_Object_Recognition_A_Why_Prompt_for_Visual_Rationales_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mao_Doubly_Right_Object_Recognition_A_Why_Prompt_for_Visual_Rationales_CVPR_2023_paper.html | CVPR 2023 | null |
Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning | Jinwoo Kim, Janghyuk Choi, Ho-Jin Choi, Seon Joo Kim | Object-centric learning (OCL) aspires general and com- positional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view images carry less information about how to disentangle a given scene than videos or multi-view im- ages do. Hence, owing to the difficulty of applying induc- tive biases, OCL for single-view images still remains chal- lenging, resulting in inconsistent learning of object-centric representation. To this end, we introduce a novel OCL framework for single-view images, SLot Attention via SHep- herding (SLASH), which consists of two simple-yet-effective modules on top of Slot Attention. The new modules, At- tention Refining Kernel (ARK) and Intermediate Point Pre- dictor and Encoder (IPPE), respectively, prevent slots from being distracted by the background noise and indicate lo- cations for slots to focus on to facilitate learning of object- centric representation. We also propose a weak- and semi- supervision approach for OCL, whilst our proposed frame- work can be used without any assistant annotation during the inference. Experiments show that our proposed method enables consistent learning of object-centric representa- tion and achieves strong performance across four datasets. Code is available at https://github.com/object- understanding/SLASH. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Shepherding_Slots_to_Objects_Towards_Stable_and_Robust_Object-Centric_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Shepherding_Slots_to_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17842 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Shepherding_Slots_to_Objects_Towards_Stable_and_Robust_Object-Centric_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Shepherding_Slots_to_Objects_Towards_Stable_and_Robust_Object-Centric_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity Event-Radiance Recovery via Transient Event Frequency | Jin Han, Yuta Asano, Boxin Shi, Yinqiang Zheng, Imari Sato | High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_High-Fidelity_Event-Radiance_Recovery_via_Transient_Event_Frequency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_High-Fidelity_Event-Radiance_Recovery_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_High-Fidelity_Event-Radiance_Recovery_via_Transient_Event_Frequency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_High-Fidelity_Event-Radiance_Recovery_via_Transient_Event_Frequency_CVPR_2023_paper.html | CVPR 2023 | null |
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action | Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, João Pedro Araújo, Jeffrey Gu, Karen Liu, Serena Yeung | The task of reconstructing 3D human motion has wide-ranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware, and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view MoCap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motion-driven animation accessible to the general public. However, the performance of existing HMR methods degrades when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action, and show that NeMo achieves better 3D reconstruction compared to various baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_NeMo_Learning_3D_Neural_Motion_Fields_From_Multiple_Video_Instances_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_NeMo_Learning_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_NeMo_Learning_3D_Neural_Motion_Fields_From_Multiple_Video_Instances_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_NeMo_Learning_3D_Neural_Motion_Fields_From_Multiple_Video_Instances_CVPR_2023_paper.html | CVPR 2023 | null |
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts | Han Liu, Yuhao Wu, Shixuan Zhai, Bo Yuan, Ning Zhang | The field of text-to-image generation has made remarkable strides in creating high-fidelity and photorealistic images. As this technology gains popularity, there is a growing concern about its potential security risks. However, there has been limited exploration into the robustness of these models from an adversarial perspective. Existing research has primarily focused on untargeted settings, and lacks holistic consideration for reliability (attack success rate) and stealthiness (imperceptibility). In this paper, we propose RIATIG, a reliable and imperceptible adversarial attack against text-to-image models via inconspicuous examples. By formulating the example crafting as an optimization process and solving it using a genetic-based method, our proposed attack can generate imperceptible prompts for text-to-image generation models in a reliable way. Evaluation of six popular text-to-image generation models demonstrates the efficiency and stealthiness of our attack in both white-box and black-box settings. To allow the community to build on top of our findings, we've made the artifacts available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_RIATIG_Reliable_and_Imperceptible_Adversarial_Text-to-Image_Generation_With_Natural_Prompts_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_RIATIG_Reliable_and_Imperceptible_Adversarial_Text-to-Image_Generation_With_Natural_Prompts_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_RIATIG_Reliable_and_Imperceptible_Adversarial_Text-to-Image_Generation_With_Natural_Prompts_CVPR_2023_paper.html | CVPR 2023 | null |
Distilling Neural Fields for Real-Time Articulated Shape Reconstruction | Jeff Tan, Gengshan Yang, Deva Ramanan | We present a method for reconstructing articulated 3D models from videos in real-time, without test-time optimization or manual 3D supervision at training time. Prior work often relies on pre-built deformable models (e.g. SMAL/SMPL), or slow per-scene optimization through differentiable rendering (e.g. dynamic NeRFs). Such methods fail to support arbitrary object categories, or are unsuitable for real-time applications. To address the challenge of collecting large-scale 3D training data for arbitrary deformable object categories, our key insight is to use off-the-shelf video-based dynamic NeRFs as 3D supervision to train a fast feed-forward network, turning 3D shape and motion prediction into a supervised distillation task. Our temporal-aware network uses articulated bones and blend skinning to represent arbitrary deformations, and is self-supervised on video datasets without requiring 3D shapes or viewpoints as input. Through distillation, our network learns to 3D-reconstruct unseen articulated objects at interactive frame rates. Our method yields higher-fidelity 3D reconstructions than prior real-time methods for animals, with the ability to render realistic images at novel viewpoints and poses. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Distilling_Neural_Fields_for_Real-Time_Articulated_Shape_Reconstruction_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Distilling_Neural_Fields_for_Real-Time_Articulated_Shape_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Distilling_Neural_Fields_for_Real-Time_Articulated_Shape_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
GLIGEN: Open-Set Grounded Text-to-Image Generation | Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, Yong Jae Lee | Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN: Open-Set Grounded Text-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN's zero-shot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_GLIGEN_Open-Set_Grounded_Text-to-Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_GLIGEN_Open-Set_Grounded_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07093 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_GLIGEN_Open-Set_Grounded_Text-to-Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_GLIGEN_Open-Set_Grounded_Text-to-Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Q: How To Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images! | Zaid Khan, Vijay Kumar BG, Samuel Schulter, Xiang Yu, Yun Fu, Manmohan Chandraker | Finetuning a large vision language model (VLM) on a target dataset after large scale pretraining is a dominant paradigm in visual question answering (VQA). Datasets for specialized tasks such as knowledge-based VQA or VQA in non natural-image domains are orders of magnitude smaller than those for general-purpose VQA. While collecting additional labels for specialized tasks or domains can be challenging, unlabeled images are often available. We introduce SelTDA (Self-Taught Data Augmentation), a strategy for finetuning large VLMs on small-scale VQA datasets. SelTDA uses the VLM and target dataset to build a teacher model that can generate question-answer pseudolabels directly conditioned on an image alone, allowing us to pseudolabel unlabeled images. SelTDA then finetunes the initial VLM on the original dataset augmented with freshly pseudolabeled images. We describe a series of experiments showing that our self-taught data augmentation increases robustness to adversarially searched questions, counterfactual examples, and rephrasings, it improves domain generalization, and results in greater retention of numerical reasoning skills. The proposed strategy requires no additional annotations or architectural modifications, and is compatible with any modern encoder-decoder multimodal transformer. Code available at https://github.com/codezakh/SelTDA | https://openaccess.thecvf.com/content/CVPR2023/papers/Khan_Q_How_To_Specialize_Large_Vision-Language_Models_to_Data-Scarce_VQA_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Khan_Q_How_To_Specialize_Large_Vision-Language_Models_to_Data-Scarce_VQA_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Khan_Q_How_To_Specialize_Large_Vision-Language_Models_to_Data-Scarce_VQA_CVPR_2023_paper.html | CVPR 2023 | null |
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction | Dekai Zhu, Guangyao Zhai, Yan Di, Fabian Manhardt, Hendrik Berkemeyer, Tuan Tran, Nassir Navab, Federico Tombari, Benjamin Busam | Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneously processing multiple agents lies in modeling complex social interactions caused by various driving intentions and road conditions. Previous methods typically leverage graph-based message propagation or attention mechanism to encapsulate such interactions in the format of marginal probabilistic distributions. However, it is inherently sub-optimal. In this paper, we propose IPCC-TP, a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. IPCC-TP learns pairwise joint Gaussian Distributions through the tightly-coupled estimation of the means and covariances according to interactive incremental movements. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders. Extensive experiments on nuScenes and Argoverse 2 datasets demonstrate that IPCC-TP improves the performance of baselines by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_IPCC-TP_Utilizing_Incremental_Pearson_Correlation_Coefficient_for_Joint_Multi-Agent_Trajectory_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_IPCC-TP_Utilizing_Incremental_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_IPCC-TP_Utilizing_Incremental_Pearson_Correlation_Coefficient_for_Joint_Multi-Agent_Trajectory_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_IPCC-TP_Utilizing_Incremental_Pearson_Correlation_Coefficient_for_Joint_Multi-Agent_Trajectory_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization | Zifan Wang, Nan Ding, Tomer Levinboim, Xi Chen, Radu Soricut | Recent research in robust optimization has shown an overfitting-like phenomenon in which models trained against adversarial attacks exhibit higher robustness on the training set compared to the test set. Although previous work provided theoretical explanations for this phenomenon using a robust PAC-Bayesian bound over the adversarial test error, related algorithmic derivations are at best only loosely connected to this bound, which implies that there is still a gap between their empirical success and our understanding of adversarial robustness theory. To close this gap, in this paper we consider a different form of the robust PAC-Bayesian bound and directly minimize it with respect to the model posterior. The derivation of the optimal solution connects PAC-Bayesian learning to the geometry of the robust loss surface through a Trace of Hessian (TrH) regularizer that measures the surface flatness. In practice, we restrict the TrH regularizer to the top layer only, which results in an analytical solution to the bound whose computational cost does not depend on the network depth. Finally, we evaluate our TrH regularization approach over CIFAR-10/100 and ImageNet using Vision Transformers (ViT) and compare against baseline adversarial robustness algorithms. Experimental results show that TrH regularization leads to improved ViT robustness that either matches or surpasses previous state-of-the-art approaches while at the same time requires less memory and computational cost. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Improving_Robust_Generalization_by_Direct_PAC-Bayesian_Bound_Minimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Improving_Robust_Generalization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12624 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Improving_Robust_Generalization_by_Direct_PAC-Bayesian_Bound_Minimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Improving_Robust_Generalization_by_Direct_PAC-Bayesian_Bound_Minimization_CVPR_2023_paper.html | CVPR 2023 | null |
MobileOne: An Improved One Millisecond Mobile Backbone | Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan | Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks -- image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device. | https://openaccess.thecvf.com/content/CVPR2023/papers/Vasu_MobileOne_An_Improved_One_Millisecond_Mobile_Backbone_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vasu_MobileOne_An_Improved_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.04040 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vasu_MobileOne_An_Improved_One_Millisecond_Mobile_Backbone_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vasu_MobileOne_An_Improved_One_Millisecond_Mobile_Backbone_CVPR_2023_paper.html | CVPR 2023 | null |
A Data-Based Perspective on Transfer Learning | Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Mądry | It is commonly believed that more pre-training data leads to better transfer learning performance. However, recent evidence suggests that removing data from the source dataset can actually help too. In this work, we present a framework for probing the impact of the source dataset's composition on transfer learning performance. Our framework facilitates new capabilities such as identifying transfer learning brittleness and detecting pathologies such as data-leakage and the presence of misleading examples in the source dataset. In particular, we demonstrate that removing detrimental datapoints identified by our framework improves transfer performance from ImageNet on a variety of transfer tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_A_Data-Based_Perspective_on_Transfer_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_A_Data-Based_Perspective_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2207.05739 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_A_Data-Based_Perspective_on_Transfer_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_A_Data-Based_Perspective_on_Transfer_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation | Takehiko Ohkawa, Kun He, Fadime Sener, Tomas Hodan, Luan Tran, Cem Keskin | We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object interactions. The dataset includes synchronized egocentric and exocentric images sampled from the recent Assembly101 dataset, in which participants assemble and disassemble take-apart toys. To obtain high-quality 3D hand pose annotations for the egocentric images, we develop an efficient pipeline, where we use an initial set of manual annotations to train a model to automatically annotate a much larger dataset. Our annotation model uses multi-view feature fusion and an iterative refinement scheme, and achieves an average keypoint error of 4.20 mm, which is 85 % lower than the error of the original annotations in Assembly101. AssemblyHands provides 3.0M annotated images, including 490K egocentric images, making it the largest existing benchmark dataset for egocentric 3D hand pose estimation. Using this data, we develop a strong single-view baseline of 3D hand pose estimation from egocentric images. Furthermore, we design a novel action classification task to evaluate predicted 3D hand poses. Our study shows that having higher-quality hand poses directly improves the ability to recognize actions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ohkawa_AssemblyHands_Towards_Egocentric_Activity_Understanding_via_3D_Hand_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ohkawa_AssemblyHands_Towards_Egocentric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12301 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ohkawa_AssemblyHands_Towards_Egocentric_Activity_Understanding_via_3D_Hand_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ohkawa_AssemblyHands_Towards_Egocentric_Activity_Understanding_via_3D_Hand_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Scene-Aware Egocentric 3D Human Pose Estimation | Jian Wang, Diogo Luvizon, Weipeng Xu, Lingjie Liu, Kripasindhu Sarkar, Christian Theobalt | Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Existing methods still struggle in challenging poses where the human body is highly occluded or is closely interacting with the scene. To address this issue, we propose a scene-aware egocentric pose estimation method that guides the prediction of the egocentric pose with scene constraints. To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network. Next, we propose a scene-aware pose estimation network that projects the 2D image features and estimated depth map of the scene into a voxel space and regresses the 3D pose with a V2V network. The voxel-based feature representation provides the direct geometric connection between 2D image features and scene geometry, and further facilitates the V2V network to constrain the predicted pose based on the estimated scene geometry. To enable the training of the aforementioned networks, we also generated a synthetic dataset, called EgoGTA, and an in-the-wild dataset based on EgoPW, called EgoPW-Scene. The experimental results of our new evaluation sequences show that the predicted 3D egocentric poses are accurate and physically plausible in terms of human-scene interaction, demonstrating that our method outperforms the state-of-the-art methods both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Scene-Aware_Egocentric_3D_Human_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Scene-Aware_Egocentric_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.11684 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Scene-Aware_Egocentric_3D_Human_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Scene-Aware_Egocentric_3D_Human_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Geometry-Aware Representations by Sketching | Hyundo Lee, Inwoo Hwang, Hyunsung Go, Won-Seok Choi, Kibeom Kim, Byoung-Tak Zhang | Understanding geometric concepts, such as distance and shape, is essential for understanding the real world and also for many vision tasks. To incorporate such information into a visual representation of a scene, we propose learning to represent the scene by sketching, inspired by human behavior. Our method, coined Learning by Sketching (LBS), learns to convert an image into a set of colored strokes that explicitly incorporate the geometric information of the scene in a single inference step without requiring a sketch dataset. A sketch is then generated from the strokes where CLIP-based perceptual loss maintains a semantic similarity between the sketch and the image. We show theoretically that sketching is equivariant with respect to arbitrary affine transformations and thus provably preserves geometric information. Experimental results show that LBS substantially improves the performance of object attribute classification on the unlabeled CLEVR dataset, domain transfer between CLEVR and STL-10 datasets, and for diverse downstream tasks, confirming that LBS provides rich geometric information. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Learning_Geometry-Aware_Representations_by_Sketching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Learning_Geometry-Aware_Representations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Learning_Geometry-Aware_Representations_by_Sketching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Learning_Geometry-Aware_Representations_by_Sketching_CVPR_2023_paper.html | CVPR 2023 | null |
SVFormer: Semi-Supervised Video Transformer for Action Recognition | Zhen Xing, Qi Dai, Han Hu, Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang | Semi-supervised action recognition is a challenging but critical task due to the high cost of video annotations. Existing approaches mainly use convolutional neural networks, yet current revolutionary vision transformer models have been less explored. In this paper, we investigate the use of transformer models under the SSL setting for action recognition. To this end, we introduce SVFormer, which adopts a steady pseudo-labeling framework (ie, EMA-Teacher) to cope with unlabeled video samples. While a wide range of data augmentations have been shown effective for semi-supervised image classification, they generally produce limited results for video recognition. We therefore introduce a novel augmentation strategy, Tube TokenMix, tailored for video data where video clips are mixed via a mask with consistent masked tokens over the temporal axis. In addition, we propose a temporal warping augmentation to cover the complex temporal variation in videos, which stretches selected frames to various temporal durations in the clip. Extensive experiments on three datasets Kinetics-400, UCF-101, and HMDB-51 verify the advantage of SVFormer. In particular, SVFormer outperforms the state-of-the-art by 31.5% with fewer training epochs under the 1% labeling rate of Kinetics-400. Our method can hopefully serve as a strong benchmark and encourage future search on semi-supervised action recognition with Transformer networks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xing_SVFormer_Semi-Supervised_Video_Transformer_for_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xing_SVFormer_Semi-Supervised_Video_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13222 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xing_SVFormer_Semi-Supervised_Video_Transformer_for_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xing_SVFormer_Semi-Supervised_Video_Transformer_for_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
X-Avatar: Expressive Human Avatars | Kaiyue Shen, Chen Guo, Manuel Kaufmann, Juan Jose Zarate, Julien Valentin, Jie Song, Otmar Hilliges | We present X-Avatar, a novel avatar model that captures the full expressiveness of digital humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data. To achieve this, we propose a part-aware learned forward skinning module that can be driven by the parameter space of SMPL-X, allowing for expressive animation of X-Avatars. To efficiently learn the neural shape and deformation fields, we propose novel part-aware sampling and initialization strategies. This leads to higher fidelity results, especially for smaller body parts while maintaining efficient training despite increased number of articulated bones. To capture the appearance of the avatar with high-frequency details, we extend the geometry and deformation fields with a texture network that is conditioned on pose, facial expression, geometry and the normals of the deformed surface. We show experimentally that our method outperforms strong baselines both quantitatively and qualitatively on the animation task. To facilitate future research on expressive avatars we contribute a new dataset, called X-Humans, containing 233 sequences of high-quality textured scans from 20 participants, totalling 35,500 data frames. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_X-Avatar_Expressive_Human_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_X-Avatar_Expressive_Human_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_X-Avatar_Expressive_Human_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_X-Avatar_Expressive_Human_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
AccelIR: Task-Aware Image Compression for Accelerating Neural Restoration | Juncheol Ye, Hyunho Yeo, Jinwoo Park, Dongsu Han | Recently, deep neural networks have been successfully applied for image restoration (IR) (e.g., super-resolution, de-noising, de-blurring). Despite their promising performance, running IR networks requires heavy computation. A large body of work has been devoted to addressing this issue by designing novel neural networks or pruning their parameters. However, the common limitation is that while images are saved in a compressed format before being enhanced by IR, prior work does not consider the impact of compression on the IR quality. In this paper, we present AccelIR, a framework that optimizes image compression considering the end-to-end pipeline of IR tasks. AccelIR encodes an image through IR-aware compression that optimizes compression levels across image blocks within an image according to the impact on the IR quality. Then, it runs a lightweight IR network on the compressed image, effectively reducing IR computation, while maintaining the same IR quality and image size. Our extensive evaluation using seven IR networks shows that AccelIR can reduce the computing overhead of super-resolution, de-nosing, and de-blurring by 49%, 29%, and 32% on average, respectively | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_AccelIR_Task-Aware_Image_Compression_for_Accelerating_Neural_Restoration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_AccelIR_Task-Aware_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_AccelIR_Task-Aware_Image_Compression_for_Accelerating_Neural_Restoration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_AccelIR_Task-Aware_Image_Compression_for_Accelerating_Neural_Restoration_CVPR_2023_paper.html | CVPR 2023 | null |
BEV-Guided Multi-Modality Fusion for Driving Perception | Yunze Man, Liang-Yan Gui, Yu-Xiong Wang | Integrating multiple sensors and addressing diverse tasks in an end-to-end algorithm are challenging yet critical topics for autonomous driving. To this end, we introduce BEVGuide, a novel Bird's Eye-View (BEV) representation learning framework, representing the first attempt to unify a wide range of sensors under direct BEV guidance in an end-to-end fashion. Our architecture accepts input from a diverse sensor pool, including but not limited to Camera, Lidar and Radar sensors, and extracts BEV feature embeddings using a versatile and general transformer backbone. We design a BEV-guided multi-sensor attention block to take queries from BEV embeddings and learn the BEV representation from sensor-specific features. BEVGuide is efficient due to its lightweight backbone design and highly flexible as it supports almost any input sensor configurations. Extensive experiments demonstrate that our framework achieves exceptional performance in BEV perception tasks with a diverse sensor set. Project page is at https://yunzeman.github.io/BEVGuide. | https://openaccess.thecvf.com/content/CVPR2023/papers/Man_BEV-Guided_Multi-Modality_Fusion_for_Driving_Perception_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Man_BEV-Guided_Multi-Modality_Fusion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Man_BEV-Guided_Multi-Modality_Fusion_for_Driving_Perception_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Man_BEV-Guided_Multi-Modality_Fusion_for_Driving_Perception_CVPR_2023_paper.html | CVPR 2023 | null |
Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding | Minyoung Hwang, Jaeyeon Jeong, Minsoo Kim, Yoonseon Oh, Songhwai Oh | The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outperforms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rate by 17.1% and SPL by 20.6% for the SOON benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hwang_Meta-Explore_Exploratory_Hierarchical_Vision-and-Language_Navigation_Using_Scene_Object_Spectrum_Grounding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hwang_Meta-Explore_Exploratory_Hierarchical_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hwang_Meta-Explore_Exploratory_Hierarchical_Vision-and-Language_Navigation_Using_Scene_Object_Spectrum_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hwang_Meta-Explore_Exploratory_Hierarchical_Vision-and-Language_Navigation_Using_Scene_Object_Spectrum_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
Proximal Splitting Adversarial Attack for Semantic Segmentation | Jérôme Rony, Jean-Christophe Pesquet, Ismail Ben Ayed | Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation. The methods proposed in these works do not accurately solve the adversarial segmentation problem and, therefore, overestimate the size of the perturbations required to fool models. Here, we propose a white-box attack for these models based on a proximal splitting to produce adversarial perturbations with much smaller l_infinity norms. Our attack can handle large numbers of constraints within a nonconvex minimization framework via an Augmented Lagrangian approach, coupled with adaptive constraint scaling and masking strategies. We demonstrate that our attack significantly outperforms previously proposed ones, as well as classification attacks that we adapted for segmentation, providing a first comprehensive benchmark for this dense task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rony_Proximal_Splitting_Adversarial_Attack_for_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rony_Proximal_Splitting_Adversarial_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.07179 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rony_Proximal_Splitting_Adversarial_Attack_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rony_Proximal_Splitting_Adversarial_Attack_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Improved Test-Time Adaptation for Domain Generalization | Liang Chen, Yong Zhang, Yibing Song, Ying Shan, Lingqiao Liu | The main challenge in domain generalization (DG) is to handle the distribution shift problem that lies between the training and test data. Recent studies suggest that test-time training (TTT), which adapts the learned model with test data, might be a promising solution to the problem. Generally, a TTT strategy hinges its performance on two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase. Both previous arts and our experiments indicate that TTT may not improve but be detrimental to the learned model if those two factors are not properly considered. This work addresses those two factors by proposing an Improved Test-Time Adaptation (ITTA) method. First, instead of heuristically defining an auxiliary objective, we propose a learnable consistency loss for the TTT task, which contains learnable parameters that can be adjusted toward better alignment between our TTT task and the main prediction task. Second, we introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase. Through extensive experiments, we show that the proposed two strategies are beneficial for the learned model (see Figure 1), and ITTA could achieve superior performance to the current state-of-the-arts on several DG benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Improved_Test-Time_Adaptation_for_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Improved_Test-Time_Adaptation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04494 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Improved_Test-Time_Adaptation_for_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Improved_Test-Time_Adaptation_for_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Recovering 3D Hand Mesh Sequence From a Single Blurry Image: A New Dataset and Temporal Unfolding | Yeonguk Oh, JoonKyu Park, Jaeha Kim, Gyeongsik Moon, Kyoung Mu Lee | Hands, one of the most dynamic parts of our body, suffer from blur due to their active movements. However, previous 3D hand mesh recovery methods have mainly focused on sharp hand images rather than considering blur due to the absence of datasets providing blurry hand images. We first present a novel dataset BlurHand, which contains blurry hand images with 3D groundtruths. The BlurHand is constructed by synthesizing motion blur from sequential sharp hand images, imitating realistic and natural motion blurs. In addition to the new dataset, we propose BlurHandNet, a baseline network for accurate 3D hand mesh recovery from a blurry hand image. Our BlurHandNet unfolds a blurry input image to a 3D hand mesh sequence to utilize temporal information in the blurry input image, while previous works output a static single hand mesh. We demonstrate the usefulness of BlurHand for the 3D hand mesh recovery from blurry images in our experiments. The proposed BlurHandNet produces much more robust results on blurry images while generalizing well to in-the-wild images. The training codes and BlurHand dataset are available at https://github.com/JaehaKim97/BlurHand_RELEASE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Oh_Recovering_3D_Hand_Mesh_Sequence_From_a_Single_Blurry_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Oh_Recovering_3D_Hand_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.15417 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Oh_Recovering_3D_Hand_Mesh_Sequence_From_a_Single_Blurry_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Oh_Recovering_3D_Hand_Mesh_Sequence_From_a_Single_Blurry_Image_CVPR_2023_paper.html | CVPR 2023 | null |
NaQ: Leveraging Narrations As Queries To Supervise Episodic Memory | Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman | Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand. However, the structured nature of the learning problem (free-form text query inputs, localized video temporal window outputs) and its needle-in-a-haystack nature makes it both technically challenging and expensive to supervise. We introduce Narrations-as-Queries (NaQ), a data augmentation strategy that transforms standard video-text narrations into training data for a video query localization model. Validating our idea on the Ego4D benchmark, we find it has tremendous impact in practice. NaQ improves multiple top models by substantial margins (even doubling their accuracy), and yields the very best results to date on the Ego4D NLQ challenge, soundly outperforming all challenge winners in the CVPR and ECCV 2022 competitions and topping the current public leaderboard. Beyond achieving the state-of-the-art for NLQ, we also demonstrate unique properties of our approach such as the ability to perform zero-shot and few-shot NLQ, and improved performance on queries about long-tail object categories. Code and models: http://vision.cs.utexas.edu/projects/naq. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ramakrishnan_NaQ_Leveraging_Narrations_As_Queries_To_Supervise_Episodic_Memory_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ramakrishnan_NaQ_Leveraging_Narrations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.00746 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ramakrishnan_NaQ_Leveraging_Narrations_As_Queries_To_Supervise_Episodic_Memory_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ramakrishnan_NaQ_Leveraging_Narrations_As_Queries_To_Supervise_Episodic_Memory_CVPR_2023_paper.html | CVPR 2023 | null |
Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution | Yixuan Sun, Dongyang Zhao, Zhangyue Yin, Yiwen Huang, Tao Gui, Wenqiang Zhang, Weifeng Ge | This paper solves the problem of learning dense visual correspondences between different object instances of the same category with only sparse annotations. We decompose this pixel-level semantic matching problem into two easier ones: (i) First, local feature descriptors of source and target images need to be mapped into shared semantic spaces to get coarse matching flows. (ii) Second, matching flows in low resolution should be refined to generate accurate point-to-point matching results. We propose asymmetric feature learning and matching flow super-resolution based on vision transformers to solve the above problems. The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts. Then matching flow in low resolutions is enhanced by a super-resolution network to get accurate correspondences. Our pipeline is built upon vision transformers and can be trained in an end-to-end manner. Extensive experimental results on several popular benchmarks, such as PF-PASCAL, PF-WILLOW, and SPair-71K, demonstrate that the proposed method can catch subtle semantic differences in pixels efficiently. Code is available on https://github.com/YXSUNMADMAX/ACTR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Correspondence_Transformers_With_Asymmetric_Feature_Learning_and_Matching_Flow_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Correspondence_Transformers_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Correspondence_Transformers_With_Asymmetric_Feature_Learning_and_Matching_Flow_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Correspondence_Transformers_With_Asymmetric_Feature_Learning_and_Matching_Flow_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Adjustment and Alignment for Unbiased Open Set Domain Adaptation | Wuyang Li, Jie Liu, Bo Han, Yixuan Yuan | Open Set Domain Adaptation (OSDA) transfers the model from a label-rich domain to a label-free one containing novel-class samples. Existing OSDA works overlook abundant novel-class semantics hidden in the source domain, leading to a biased model learning and transfer. Although the causality has been studied to remove the semantic-level bias, the non-available novel-class samples result in the failure of existing causal solutions in OSDA. To break through this barrier, we propose a novel causality-driven solution with the unexplored front-door adjustment theory, and then implement it with a theoretically grounded framework, coined AdjustmeNt aNd Alignment (ANNA), to achieve an unbiased OSDA. In a nutshell, ANNA consists of Front-Door Adjustment (FDA) to correct the biased learning in the source domain and Decoupled Causal Alignment (DCA) to transfer the model unbiasedly. On the one hand, FDA delves into fine-grained visual blocks to discover novel-class regions hidden in the base-class image. Then, it corrects the biased model optimization by implementing causal debiasing. On the other hand, DCA disentangles the base-class and novel-class regions with orthogonal masks, and then adapts the decoupled distribution for an unbiased model transfer. Extensive experiments show that ANNA achieves state-of-the-art results. The code is available at https://github.com/CityU-AIM-Group/Anna. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Adjustment_and_Alignment_for_Unbiased_Open_Set_Domain_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Adjustment_and_Alignment_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Adjustment_and_Alignment_for_Unbiased_Open_Set_Domain_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Adjustment_and_Alignment_for_Unbiased_Open_Set_Domain_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation | Jiaxu Miao, Zongxin Yang, Leilei Fan, Yi Yang | Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a global model across multiple clients with data privacy-preserving. Although many FL algorithms have been proposed for classification tasks, few works focus on more challenging semantic seg-mentation tasks, especially in the class-heterogeneous FL situation. Compared with classification, the issues from heterogeneous FL for semantic segmentation are more severe: (1) Due to the non-IID distribution, different clients may contain inconsistent foreground-background classes, resulting in divergent local updates. (2) Class-heterogeneity for complex dense prediction tasks makes the local optimum of clients farther from the global optimum. In this work, we propose FedSeg, a basic federated learning approach for class-heterogeneous semantic segmentation. We first propose a simple but strong modified cross-entropy loss to correct the local optimization and address the foreground-background inconsistency problem. Based on it, we introduce pixel-level contrastive learning to enforce local pixel embeddings belonging to the global semantic space. Extensive experiments on four semantic segmentation benchmarks (Cityscapes, CamVID, PascalVOC and ADE20k) demonstrate the effectiveness of our FedSeg. We hope this work will attract more attention from the FL community to the challenging semantic segmentation federated learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Miao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Miao_FedSeg_Class-Heterogeneous_Federated_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Miao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Miao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralField-LDM: Scene Generation With Hierarchical Latent Diffusion Models | Seung Wook Kim, Bradley Brown, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, Sanja Fidler | Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce NeuralField-LDM, a generative model capable of synthesizing complex 3D environments. We leverage Latent Diffusion Models that have been successfully utilized for efficient high-quality 2D content creation. We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene. To further compress this representation, we train a latent-autoencoder that maps the voxel grids to a set of latent representations. A hierarchical diffusion model is then fit to the latents to complete the scene generation pipeline. We achieve a substantial improvement over existing state-of-the-art scene generation models. Additionally, we show how NeuralField-LDM can be used for a variety of 3D content creation applications, including conditional scene generation, scene inpainting and scene style manipulation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_NeuralField-LDM_Scene_Generation_With_Hierarchical_Latent_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_NeuralField-LDM_Scene_Generation_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_NeuralField-LDM_Scene_Generation_With_Hierarchical_Latent_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_NeuralField-LDM_Scene_Generation_With_Hierarchical_Latent_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
DPF: Learning Dense Prediction Fields With Weak Supervision | Xiaoxue Chen, Yuhang Zheng, Yupeng Zheng, Qiang Zhou, Hao Zhao, Guyue Zhou, Ya-Qin Zhang | Nowadays, many visual scene understanding problems are addressed by dense prediction networks. But pixel-wise dense annotations are very expensive (e.g., for scene parsing) or impossible (e.g., for intrinsic image decomposition), motivating us to leverage cheap point-level weak supervision. However, existing pointly-supervised methods still use the same architecture designed for full supervision. In stark contrast to them, we propose a new paradigm that makes predictions for point coordinate queries, as inspired by the recent success of implicit representations, like distance or radiance fields. As such, the method is named as dense prediction fields (DPFs). DPFs generate expressive intermediate features for continuous sub-pixel locations, thus allowing outputs of an arbitrary resolution. DPFs are naturally compatible with point-level supervision. We showcase the effectiveness of DPFs using two substantially different tasks: high-level semantic parsing and low-level intrinsic image decomposition. In these two cases, supervision comes in the form of single-point semantic category and two-point relative reflectance, respectively. As benchmarked by three large-scale public datasets PascalContext, ADE20k and IIW, DPFs set new state-of-the-art performance on all of them with significant margins. Code can be accessed at https://github.com/cxx226/DPF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_DPF_Learning_Dense_Prediction_Fields_With_Weak_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_DPF_Learning_Dense_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16890 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DPF_Learning_Dense_Prediction_Fields_With_Weak_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DPF_Learning_Dense_Prediction_Fields_With_Weak_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Fast Monocular Scene Reconstruction With Global-Sparse Local-Dense Grids | Wei Dong, Christopher Choy, Charles Loop, Or Litany, Yuke Zhu, Anima Anandkumar | Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representations and monocular priors have led to remarkable results in scene-level surface reconstructions. The reliance on Multilayer Perceptrons (MLP), however, significantly limits speed in training and rendering. In this work, we propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without MLPs. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data such as color and semantic labels. To apply this representation to monocular scene reconstruction, we develop a scale calibration algorithm for fast geometric initialization from monocular depth priors. We apply differentiable volume rendering from this initialization to refine details with fast convergence. We also introduce efficient high-dimensional Continuous Random Fields (CRFs) to further exploit the semantic-geometry consistency between scene objects. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Fast_Monocular_Scene_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023_paper.html | CVPR 2023 | null |
Thermal Spread Functions (TSF): Physics-Guided Material Classification | Aniket Dashpute, Vishwanath Saragadam, Emma Alexander, Florian Willomitzer, Aggelos Katsaggelos, Ashok Veeraraghavan, Oliver Cossairt | Robust and non-destructive material classification is a challenging but crucial first-step in numerous vision applications. We propose a physics-guided material classification framework that relies on thermal properties of the object. Our key observation is that the rate of heating and cooling of an object depends on the unique intrinsic properties of the material, namely the emissivity and diffusivity. We leverage this observation by gently heating the objects in the scene with a low-power laser for a fixed duration and then turning it off, while a thermal camera captures measurements during the heating and cooling process. We then take this spatial and temporal "thermal spread function" (TSF) to solve an inverse heat equation using the finite-differences approach, resulting in a spatially varying estimate of diffusivity and emissivity. These tuples are then used to train a classifier that produces a fine-grained material label at each spatial pixel. Our approach is extremely simple requiring only a small light source (low power laser) and a thermal camera, and produces robust classification results with 86% accuracy over 16 classes | https://openaccess.thecvf.com/content/CVPR2023/papers/Dashpute_Thermal_Spread_Functions_TSF_Physics-Guided_Material_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dashpute_Thermal_Spread_Functions_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dashpute_Thermal_Spread_Functions_TSF_Physics-Guided_Material_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dashpute_Thermal_Spread_Functions_TSF_Physics-Guided_Material_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields | Mohammad Mahdi Johari, Camilla Carta, François Fleuret | We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system, resulting in an efficient and accurate dense visual SLAM method. Our scene representation consists of multi-scale axis-aligned perpendicular feature planes and shallow decoders that, for each point in the continuous space, decode the interpolated features into Truncated Signed Distance Field (TSDF) and RGB values. Our extensive experiments on three standard datasets, Replica, ScanNet, and TUM RGB-D show that ESLAM improves the accuracy of 3D reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to 10 times faster and does not require any pre-training. Project page: https://www.idiap.ch/paper/eslam | https://openaccess.thecvf.com/content/CVPR2023/papers/Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Johari_ESLAM_Efficient_Dense_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11704 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023_paper.html | CVPR 2023 | null |
CNVid-3.5M: Build, Filter, and Pre-Train the Large-Scale Public Chinese Video-Text Dataset | Tian Gan, Qing Wang, Xingning Dong, Xiangyuan Ren, Liqiang Nie, Qingpei Guo | Owing to well-designed large-scale video-text datasets, recent years have witnessed tremendous progress in video-text pre-training. However, existing large-scale video-text datasets are mostly English-only. Though there are certain methods studying the Chinese video-text pre-training, they pre-train their models on private datasets whose videos and text are unavailable. This lack of large-scale public datasets and benchmarks in Chinese hampers the research and downstream applications of Chinese video-text pre-training. Towards this end, we release and benchmark CNVid-3.5M, a large-scale public cross-modal dataset containing over 3.5M Chinese video-text pairs. We summarize our contributions by three verbs, i.e., "Build", "Filter", and "Pre-train": 1) To build a public Chinese video-text dataset, we collect over 4.5M videos from the Chinese websites. 2) To improve the data quality, we propose a novel method to filter out 1M weakly-paired videos, resulting in the CNVid-3.5M dataset. And 3) we benchmark CNVid-3.5M with three mainstream pixel-level pre-training architectures. At last, we propose the Hard Sample Curriculum Learning strategy to promote the pre-training performance. To the best of our knowledge, CNVid-3.5M is the largest public video-text dataset in Chinese, and we provide the first pixel-level benchmarks for Chinese video-text pre-training. The dataset, codebase, and pre-trained models are available at https://github.com/CNVid/CNVid-3.5M. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gan_CNVid-3.5M_Build_Filter_and_Pre-Train_the_Large-Scale_Public_Chinese_Video-Text_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gan_CNVid-3.5M_Build_Filter_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gan_CNVid-3.5M_Build_Filter_and_Pre-Train_the_Large-Scale_Public_Chinese_Video-Text_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gan_CNVid-3.5M_Build_Filter_and_Pre-Train_the_Large-Scale_Public_Chinese_Video-Text_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple Motions | Etienne Meunier, Patrick Bouthemy | Motion segmentation is one of the main tasks in computer vision and is relevant for many applications. The optical flow (OF) is the input generally used to segment every frame of a video sequence into regions of coherent motion. Temporal consistency is a key feature of motion segmentation, but it is often neglected. In this paper, we propose an original unsupervised spatio-temporal framework for motion segmentation from optical flow that fully investigates the temporal dimension of the problem. More specifically, we have defined a 3D network for multiple motion segmentation that takes as input a sub-volume of successive optical flows and delivers accordingly a sub-volume of coherent segmentation maps. Our network is trained in a fully unsupervised way, and the loss function combines a flow reconstruction term involving spatio-temporal parametric motion models, and a regularization term enforcing temporal consistency on the masks. We have specified an easy temporal linkage of the predicted segments. Besides, we have proposed a flexible and efficient way of coding U-nets. We report experiments on several VOS benchmarks with convincing quantitative results, while not using appearance and not training with any ground-truth data. We also highlight through visual results the distinctive contribution of the short- and long-term temporal consistency brought by our OF segmentation method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Meunier_Unsupervised_Space-Time_Network_for_Temporally-Consistent_Segmentation_of_Multiple_Motions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Meunier_Unsupervised_Space-Time_Network_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Meunier_Unsupervised_Space-Time_Network_for_Temporally-Consistent_Segmentation_of_Multiple_Motions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Meunier_Unsupervised_Space-Time_Network_for_Temporally-Consistent_Segmentation_of_Multiple_Motions_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised 3D Point Cloud Representation Learning by Triangle Constrained Contrast for Autonomous Driving | Bo Pang, Hongchi Xia, Cewu Lu | Due to the difficulty of annotating the 3D LiDAR data of autonomous driving, an efficient unsupervised 3D representation learning method is important. In this paper, we design the Triangle Constrained Contrast (TriCC) framework tailored for autonomous driving scenes which learns 3D unsupervised representations through both the multimodal information and dynamic of temporal sequences. We treat one camera image and two LiDAR point clouds with different timestamps as a triplet. And our key design is the consistent constraint that automatically finds matching relationships among the triplet through "self-cycle" and learns representations from it. With the matching relations across the temporal dimension and modalities, we can further conduct a triplet contrast to improve learning efficiency. To the best of our knowledge, TriCC is the first framework that unifies both the temporal and multimodal semantics, which means it utilizes almost all the information in autonomous driving scenes. And compared with previous contrastive methods, it can automatically dig out contrasting pairs with higher difficulty, instead of relying on handcrafted ones. Extensive experiments are conducted with Minkowski-UNet and VoxelNet on several semantic segmentation and 3D detection datasets. Results show that TriCC learns effective representations with much fewer training iterations and improves the SOTA results greatly on all the downstream tasks. Code and models can be found at https://bopang1996.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pang_Unsupervised_3D_Point_Cloud_Representation_Learning_by_Triangle_Constrained_Contrast_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pang_Unsupervised_3D_Point_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Unsupervised_3D_Point_Cloud_Representation_Learning_by_Triangle_Constrained_Contrast_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Unsupervised_3D_Point_Cloud_Representation_Learning_by_Triangle_Constrained_Contrast_CVPR_2023_paper.html | CVPR 2023 | null |
iDisc: Internal Discretization for Monocular Depth Estimation | Luigi Piccinelli, Christos Sakaridis, Fisher Yu | Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill-posed due to the lack of geometric constraints. We observe that although a scene can consist of millions of pixels, there are much fewer high-level patterns. We propose iDisc to learn those patterns with internal discretized representations. The method implicitly partitions the scene into a set of high-level concepts. In particular, our new module, Internal Discretization (ID), implements a continuous-discrete-continuous bottleneck to learn those concepts without supervision. In contrast to state-of-the-art methods, the proposed model does not enforce any explicit constraints or priors on the depth output. The whole network with the ID module can be trained in an end-to-end fashion thanks to the bottleneck module based on attention. Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark. iDisc can also achieve state-of-the-art results on surface normal estimation. Further, we explore the model generalization capability via zero-shot testing. From there, we observe the compelling need to promote diversification in the outdoor scenario and we introduce splits of two autonomous driving datasets, DDAD and Argoverse. Code is available at http://vis.xyz/pub/idisc/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Piccinelli_iDisc_Internal_Discretization_for_Monocular_Depth_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Piccinelli_iDisc_Internal_Discretization_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.06334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Piccinelli_iDisc_Internal_Discretization_for_Monocular_Depth_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Piccinelli_iDisc_Internal_Discretization_for_Monocular_Depth_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Balancing Logit Variation for Long-Tailed Semantic Segmentation | Yuchao Wang, Jingjing Fei, Haochen Wang, Wei Li, Tianpeng Bao, Liwei Wu, Rui Zhao, Yujun Shen | Semantic segmentation usually suffers from a long tail data distribution. Due to the imbalanced number of samples across categories, the features of those tail classes may get squeezed into a narrow area in the feature space. Towards a balanced feature distribution, we introduce category-wise variation into the network predictions in the training phase such that an instance is no longer projected to a feature point, but a small region instead. Such a perturbation is highly dependent on the category scale, which appears as assigning smaller variation to head classes and larger variation to tail classes. In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation. It is noteworthy that the introduced variation is discarded at the inference stage to facilitate a confident prediction. Although with an embarrassingly simple implementation, our method manifests itself in strong generalizability to various datasets and task settings. Extensive experiments suggest that our plug-in design lends itself well to a range of state-of-the-art approaches and boosts the performance on top of them. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Balancing_Logit_Variation_for_Long-Tailed_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Balancing_Logit_Variation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Balancing_Logit_Variation_for_Long-Tailed_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Balancing_Logit_Variation_for_Long-Tailed_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Prompt-Guided Zero-Shot Anomaly Action Recognition Using Pretrained Deep Skeleton Features | Fumiaki Sato, Ryo Hachiuma, Taiki Sekii | This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and simultaneously addresses three limitations in the conventional skeleton-based approaches: target domain-dependent DNN training, robustness against skeleton errors, and a lack of normal samples. We present a unified, user prompt-guided zero-shot learning framework using a target domain-independent skeleton feature extractor, which is pretrained on a large-scale action recognition dataset. Particularly, during the training phase using normal samples, the method models the distribution of skeleton features of the normal actions while freezing the weights of the DNNs and estimates the anomaly score using this distribution in the inference phase. Additionally, to increase robustness against skeleton errors, we introduce a DNN architecture inspired by a point cloud deep learning paradigm, which sparsely propagates the features between joints. Furthermore, to prevent the unobserved normal actions from being misidentified as abnormal actions, we incorporate a similarity score between the user prompt embeddings and skeleton features aligned in the common space into the anomaly score, which indirectly supplements normal actions. On two publicly available datasets, we conduct experiments to test the effectiveness of the proposed method with respect to abovementioned limitations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sato_Prompt-Guided_Zero-Shot_Anomaly_Action_Recognition_Using_Pretrained_Deep_Skeleton_Features_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sato_Prompt-Guided_Zero-Shot_Anomaly_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15167 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sato_Prompt-Guided_Zero-Shot_Anomaly_Action_Recognition_Using_Pretrained_Deep_Skeleton_Features_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sato_Prompt-Guided_Zero-Shot_Anomaly_Action_Recognition_Using_Pretrained_Deep_Skeleton_Features_CVPR_2023_paper.html | CVPR 2023 | null |
iQuery: Instruments As Queries for Audio-Visual Sound Separation | Jiaben Chen, Renrui Zhang, Dongze Lian, Jiaqi Yang, Ziyao Zeng, Jianbo Shi | Current audio-visual separation methods share a standard architecture design where an audio encoder-decoder network is fused with visual encoding features at the encoder bottleneck. This design confounds the learning of multi-modal feature encoding with robust sound decoding for audio separation. To generalize to a new instrument, one must fine-tune the entire visual and audio network for all musical instruments. We re-formulate the visual-sound separation task and propose Instruments as Queries (iQuery) with a flexible query expansion mechanism. Our approach ensures cross-modal consistency and cross-instrument disentanglement. We utilize "visually named" queries to initiate the learning of audio queries and use cross-modal attention to remove potential sound source interference at the estimated waveforms. To generalize to a new instrument or event class, drawing inspiration from the text-prompt design, we insert additional queries as audio prompts while freezing the attention mechanism. Experimental results on three benchmarks demonstrate that our iQuery improves audio-visual sound source separation performance. Code is available at https://github.com/JiabenChen/iQuery. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_iQuery_Instruments_As_Queries_for_Audio-Visual_Sound_Separation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_iQuery_Instruments_As_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.03814 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_iQuery_Instruments_As_Queries_for_Audio-Visual_Sound_Separation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_iQuery_Instruments_As_Queries_for_Audio-Visual_Sound_Separation_CVPR_2023_paper.html | CVPR 2023 | null |
Sampling Is Matter: Point-Guided 3D Human Mesh Reconstruction | Jeonghwan Kim, Mi-Gyeong Gwon, Hyunwoo Park, Hyukmin Kwon, Gi-Mun Um, Wonjun Kim | This paper presents a simple yet powerful method for 3D human mesh reconstruction from a single RGB image. Most recently, the non-local interactions of the whole mesh vertices have been effectively estimated in the transformer while the relationship between body parts also has begun to be handled via the graph model. Even though those approaches have shown the remarkable progress in 3D human mesh reconstruction, it is still difficult to directly infer the relationship between features, which are encoded from the 2D input image, and 3D coordinates of each vertex. To resolve this problem, we propose to design a simple feature sampling scheme. The key idea is to sample features in the embedded space by following the guide of points, which are estimated as projection results of 3D mesh vertices (i.e., ground truth). This helps the model to concentrate more on vertex-relevant features in the 2D space, thus leading to the reconstruction of the natural human pose. Furthermore, we apply progressive attention masking to precisely estimate local interactions between vertices even under severe occlusions. Experimental results on benchmark datasets show that the proposed method efficiently improves the performance of 3D human mesh reconstruction. The code and model are publicly available at: https://github.com/DCVL-3D/PointHMR_release. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Sampling_Is_Matter_Point-Guided_3D_Human_Mesh_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Sampling_Is_Matter_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.09502 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Sampling_Is_Matter_Point-Guided_3D_Human_Mesh_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Sampling_Is_Matter_Point-Guided_3D_Human_Mesh_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Multimodal Fusion via Interactive Prompting | Yaowei Li, Ruijie Quan, Linchao Zhu, Yi Yang | Large-scale pre-training has brought unimodal fields such as computer vision and natural language processing to a new era. Following this trend, the size of multimodal learning models constantly increases, leading to an urgent need to reduce the massive computational cost of fine-tuning these models for downstream tasks. In this paper, we propose an efficient and flexible multimodal fusion method, namely PMF, tailored for fusing unimodally pretrained transformers. Specifically, we first present a modular multimodal fusion framework that exhibits high flexibility and facilitates mutual interactions among different modalities. In addition, we disentangle vanilla prompts into three types in order to learn different optimizing objectives for multimodal learning. It is also worth noting that we propose to add prompt vectors only on the deep layers of the unimodal transformers, thus significantly reducing the training memory usage. Experiment results show that our proposed method achieves comparable performance to several other multimodal finetuning methods with less than 3% trainable parameters and up to 66% saving of training memory usage. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Efficient_Multimodal_Fusion_via_Interactive_Prompting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Efficient_Multimodal_Fusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06306 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Efficient_Multimodal_Fusion_via_Interactive_Prompting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Efficient_Multimodal_Fusion_via_Interactive_Prompting_CVPR_2023_paper.html | CVPR 2023 | null |
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning | MyeongAh Cho, Minjung Kim, Sangwon Hwang, Chaewon Park, Kyungjae Lee, Sangyoun Lee | Weakly-supervised Video Anomaly Detection is the task of detecting frame-level anomalies using video-level labeled training data. It is difficult to explore class representative features using minimal supervision of weak labels with a single backbone branch. Furthermore, in real-world scenarios, the boundary between normal and abnormal is ambiguous and varies depending on the situation. For example, even for the same motion of running person, the abnormality varies depending on whether the surroundings are a playground or a roadway. Therefore, our aim is to extract discriminative features by widening the relative gap between classes' features from a single branch. In the proposed Class-Activate Feature Learning (CLAV), the features are extracted as per the weights that are implicitly activated depending on the class, and the gap is then enlarged through relative distance learning. Furthermore, as the relationship between context and motion is important in order to identify the anomalies in complex and diverse scenes, we propose a Context--Motion Interrelation Module (CoMo), which models the relationship between the appearance of the surroundings and motion, rather than utilizing only temporal dependencies or motion information. The proposed method shows SOTA performance on four benchmarks including large-scale real-world datasets, and we demonstrate the importance of relational information by analyzing the qualitative results and generalization ability. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_Look_Around_for_Anomalies_Weakly-Supervised_Anomaly_Detection_via_Context-Motion_Relational_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cho_Look_Around_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Look_Around_for_Anomalies_Weakly-Supervised_Anomaly_Detection_via_Context-Motion_Relational_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Look_Around_for_Anomalies_Weakly-Supervised_Anomaly_Detection_via_Context-Motion_Relational_CVPR_2023_paper.html | CVPR 2023 | null |
Depth Estimation From Indoor Panoramas With Neural Scene Representation | Wenjie Chang, Yueyi Zhang, Zhiwei Xiong | Depth estimation from indoor panoramas is challenging due to the equirectangular distortions of panoramas and inaccurate matching. In this paper, we propose a practical framework to improve the accuracy and efficiency of depth estimation from multi-view indoor panoramic images with the Neural Radiance Field technology. Specifically, we develop two networks to implicitly learn the Signed Distance Function for depth measurements and the radiance field from panoramas. We also introduce a novel spherical position embedding scheme to achieve high accuracy. For better convergence, we propose an initialization method for the network weights based on the Manhattan World Assumption. Furthermore, we devise a geometric consistency loss, leveraging the surface normal, to further refine the depth estimation. The experimental results demonstrate that our proposed method outperforms state-of-the-art works by a large margin in both quantitative and qualitative evaluations. Our source code is available at https://github.com/WJ-Chang-42/IndoorPanoDepth. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chang_Depth_Estimation_From_Indoor_Panoramas_With_Neural_Scene_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chang_Depth_Estimation_From_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Depth_Estimation_From_Indoor_Panoramas_With_Neural_Scene_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Depth_Estimation_From_Indoor_Panoramas_With_Neural_Scene_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification | Honglin Li, Chenglu Zhu, Yunlong Zhang, Yuxuan Sun, Zhongyi Shui, Wenwei Kuang, Sunyi Zheng, Lin Yang | While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and generalization problems due to high computational costs and limited supervision of Gigapixel WSIs. To deal with the computation problem, previous methods utilize a frozen model pretrained from ImageNet to obtain representations, however, it may lose key information owing to the large domain gap and hinder the generalization ability without image-level training-time augmentation. Though Self-supervised Learning (SSL) proposes viable representation learning schemes, the downstream task-specific features via partial label tuning are not explored. To alleviate this problem, we propose an efficient WSI fine-tuning framework motivated by the Information Bottleneck theory. The theory enables the framework to find the minimal sufficient statistics of WSI, thus supporting us to fine-tune the backbone into a task-specific representation only depending on WSI-level weak labels. The WSI-MIL problem is further analyzed to theoretically deduce our fine-tuning method. We evaluate the method on five pathological WSI datasets on various WSI heads. The experimental results show significant improvements in both accuracy and generalization compared with previous works. Source code will be available at https://github.com/invoker-LL/WSI-finetuning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Task-Specific_Fine-Tuning_via_Variational_Information_Bottleneck_for_Weakly-Supervised_Pathology_Whole_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Task-Specific_Fine-Tuning_via_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08446 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Task-Specific_Fine-Tuning_via_Variational_Information_Bottleneck_for_Weakly-Supervised_Pathology_Whole_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Task-Specific_Fine-Tuning_via_Variational_Information_Bottleneck_for_Weakly-Supervised_Pathology_Whole_CVPR_2023_paper.html | CVPR 2023 | null |
Detecting Everything in the Open World: Towards Universal Object Detection | Zhenyu Wang, Yali Li, Xi Chen, Ser-Nam Lim, Antonio Torralba, Hengshuang Zhao, Shengjin Wang | In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual information, and the novel categories in the open world severely restrict the universality of traditional detectors. We propose UniDetector, a universal object detector that has the ability to recognize enormous categories in the open world. The critical points for the universality of UniDetector are: 1) it leverages images of multiple sources and heterogeneous label spaces for training through the alignment of image and text spaces, which guarantees sufficient information for universal representations. 2) it generalizes to the open world easily while keeping the balance between seen and unseen classes, thanks to abundant information from both vision and language modalities. 3) it further promotes the generalization ability to novel categories through our proposed decoupling training manner and probability calibration. These contributions allow UniDetector to detect over 7k categories, the largest measurable category size so far, with only about 500 classes participating in training. Our UniDetector behaves the strong zero-shot generalization ability on large-vocabulary datasets like LVIS, ImageNetBoxes, and VisualGenome - it surpasses the traditional supervised baselines by more than 4% on average without seeing any corresponding images. On 13 public detection datasets with various scenes, UniDetector also achieves state-of-the-art performance with only a 3% amount of training data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Detecting_Everything_in_the_Open_World_Towards_Universal_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Detecting_Everything_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11749 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Detecting_Everything_in_the_Open_World_Towards_Universal_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Detecting_Everything_in_the_Open_World_Towards_Universal_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take | Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc Van Gool | Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is ill-posed, the fundamental goal is to come up with an approach that can reliably model the scene depth from a set of training examples. In the pursuit of perfect depth estimation, most existing state-of-the-art learning techniques predict a single scalar depth value per-pixel. Yet, it is well-known that the trained model has accuracy limits and can predict imprecise depth. Therefore, an SIDP approach must be mindful of the expected depth variations in the model's prediction at test time. Accordingly, we introduce an approach that performs continuous modeling of per-pixel depth, where we can predict and reason about the per-pixel depth and its distribution. To this end, we model per-pixel scene depth using a multivariate Gaussian distribution. Moreover, contrary to the existing uncertainty modeling methods---in the same spirit, where per-pixel depth is assumed to be independent, we introduce per-pixel covariance modeling that encodes its depth dependency w.r.t. all the scene points. Unfortunately, per-pixel depth covariance modeling leads to a computationally expensive continuous loss function, which we solve efficiently using the learned low-rank approximation of the overall covariance matrix. Notably, when tested on benchmark datasets such as KITTI, NYU, and SUN-RGB-D, the SIDP model obtained by optimizing our loss function shows state-of-the-art results. Our method's accuracy (named MG) is among the top on the KITTI depth-prediction benchmark leaderboard. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Single_Image_Depth_Prediction_Made_Better_A_Multivariate_Gaussian_Take_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Single_Image_Depth_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18164 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Single_Image_Depth_Prediction_Made_Better_A_Multivariate_Gaussian_Take_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Single_Image_Depth_Prediction_Made_Better_A_Multivariate_Gaussian_Take_CVPR_2023_paper.html | CVPR 2023 | null |
NUWA-LIP: Language-Guided Image Inpainting With Defect-Free VQGAN | Minheng Ni, Xiaoming Li, Wangmeng Zuo | Language-guided image inpainting aims to fill the defective regions of an image under the guidance of text while keeping the non-defective regions unchanged. However, directly encoding the defective images is prone to have an adverse effect on the non-defective regions, giving rise to distorted structures on non-defective parts. To better adapt the text guidance to the inpainting task, this paper proposes NUWA-LIP, which involves defect-free VQGAN (DF-VQGAN) and a multi-perspective sequence-to-sequence module (MP-S2S). To be specific, DF-VQGAN introduces relative estimation to carefully control the receptive spreading, as well as symmetrical connections to protect structure details unchanged. For harmoniously embedding text guidance into the locally defective regions, MP-S2S is employed by aggregating the complementary perspectives from low-level pixels, high-level tokens as well as the text description. Experiments show that our DF-VQGAN effectively aids the inpainting process while avoiding unexpected changes in non-defective regions. Results on three open-domain benchmarks demonstrate the superior performance of our method against state-of-the-arts. Our code, datasets, and model will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ni_NUWA-LIP_Language-Guided_Image_Inpainting_With_Defect-Free_VQGAN_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ni_NUWA-LIP_Language-Guided_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ni_NUWA-LIP_Language-Guided_Image_Inpainting_With_Defect-Free_VQGAN_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ni_NUWA-LIP_Language-Guided_Image_Inpainting_With_Defect-Free_VQGAN_CVPR_2023_paper.html | CVPR 2023 | null |
One-Shot Model for Mixed-Precision Quantization | Ivan Koryakovskiy, Alexandra Yakovleva, Valentin Buchnev, Temur Isaev, Gleb Odinokikh | Neural network quantization is a popular approach for model compression. Modern hardware supports quantization in mixed-precision mode, which allows for greater compression rates but adds the challenging task of searching for the optimal bit width. The majority of existing searchers find a single mixed-precision architecture. To select an architecture that is suitable in terms of performance and resource consumption, one has to restart searching multiple times. We focus on a specific class of methods that find tensor bit width using gradient-based optimization. First, we theoretically derive several methods that were empirically proposed earlier. Second, we present a novel One-Shot method that finds a diverse set of Pareto-front architectures in O(1) time. For large models, the proposed method is 5 times more efficient than existing methods. We verify the method on two classification and super-resolution models and show above 0.93 correlation score between the predicted and actual model performance. The Pareto-front architecture selection is straightforward and takes only 20 to 40 supernet evaluations, which is the new state-of-the-art result to the best of our knowledge. | https://openaccess.thecvf.com/content/CVPR2023/papers/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Koryakovskiy_One-Shot_Model_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
MARLIN: Masked Autoencoder for Facial Video Representation LearnINg | Zhixi Cai, Shreya Ghosh, Kalin Stefanov, Abhinav Dhall, Jianfei Cai, Hamid Rezatofighi, Reza Haffari, Munawar Hayat | This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our proposed framework, named MARLIN, is a facial video masked autoencoder, that learns highly robust and generic facial embeddings from abundantly available non-annotated web crawled facial videos. As a challenging auxiliary task, MARLIN reconstructs the spatio-temporal details of the face from the densely masked facial regions which mainly include eyes, nose, mouth, lips, and skin to capture local and global aspects that in turn help in encoding generic and transferable features. Through a variety of experiments on diverse downstream tasks, we demonstrate MARLIN to be an excellent facial video encoder as well as feature extractor, that performs consistently well across a variety of downstream tasks including FAR (1.13% gain over supervised benchmark), FER (2.64% gain over unsupervised benchmark), DFD (1.86% gain over unsupervised benchmark), LS (29.36% gain for Frechet Inception Distance), and even in low data regime. Our code and models are available at https://github.com/ControlNet/MARLIN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_MARLIN_Masked_Autoencoder_for_Facial_Video_Representation_LearnINg_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.06627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_MARLIN_Masked_Autoencoder_for_Facial_Video_Representation_LearnINg_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_MARLIN_Masked_Autoencoder_for_Facial_Video_Representation_LearnINg_CVPR_2023_paper.html | CVPR 2023 | null |
Language Adaptive Weight Generation for Multi-Task Visual Grounding | Wei Su, Peihan Miao, Huanzhang Dou, Gaoang Wang, Liang Qiao, Zheyang Li, Xi Li | Although the impressive performance in visual grounding, the prevailing approaches usually exploit the visual backbone in a passive way, i.e., the visual backbone extracts features with fixed weights without expression-related hints. The passive perception may lead to mismatches (e.g., redundant and missing), limiting further performance improvement. Ideally, the visual backbone should actively extract visual features since the expressions already provide the blueprint of desired visual features. The active perception can take expressions as priors to extract relevant visual features, which can effectively alleviate the mismatches. Inspired by this, we propose an active perception Visual Grounding framework based on Language Adaptive Weights, called VG-LAW. The visual backbone serves as an expression-specific feature extractor through dynamic weights generated for various expressions. Benefiting from the specific and relevant visual features extracted from the language-aware visual backbone, VG-LAW does not require additional modules for cross-modal interaction. Along with a neat multi-task head, VG-LAW can be competent in referring expression comprehension and segmentation jointly. Extensive experiments on four representative datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, validate the effectiveness of the proposed framework and demonstrate state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Su_Language_Adaptive_Weight_Generation_for_Multi-Task_Visual_Grounding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Su_Language_Adaptive_Weight_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Su_Language_Adaptive_Weight_Generation_for_Multi-Task_Visual_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Su_Language_Adaptive_Weight_Generation_for_Multi-Task_Visual_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
Continuous Intermediate Token Learning With Implicit Motion Manifold for Keyframe Based Motion Interpolation | Clinton A. Mo, Kun Hu, Chengjiang Long, Zhiyong Wang | Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, existing methods are often with inputs of interpolated intermediate frame for continuity using basic interpolation methods with keyframes, which result in a trivial local minimum during training. In this paper, we propose a novel framework to formulate latent motion manifolds with keyframe-based constraints, from which the continuous nature of intermediate token representations is considered. Particularly, our proposed framework consists of two stages for identifying a latent motion subspace, i.e., a keyframe encoding stage and an intermediate token generation stage, and a subsequent motion synthesis stage to extrapolate and compose motion data from manifolds. Through our extensive experiments conducted on both the LaFAN1 and CMU Mocap datasets, our proposed method demonstrates both superior interpolation accuracy and high visual similarity to ground truth motions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mo_Continuous_Intermediate_Token_Learning_With_Implicit_Motion_Manifold_for_Keyframe_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mo_Continuous_Intermediate_Token_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14926 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mo_Continuous_Intermediate_Token_Learning_With_Implicit_Motion_Manifold_for_Keyframe_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mo_Continuous_Intermediate_Token_Learning_With_Implicit_Motion_Manifold_for_Keyframe_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Coarse-To-Fine Learning for Oriented Tiny Object Detection | Chang Xu, Jian Ding, Jinwang Wang, Wen Yang, Huai Yu, Lei Yu, Gui-Song Xia | Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the position prior, positive sample feature, and instance are mismatched, and the learning of extreme-shaped objects is biased and unbalanced due to little proper feature supervision. To tackle these issues, we propose a dynamic prior along with the coarse-to-fine assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and object representation all in a dynamic manner to alleviate the mismatch issue. For another, we leverage the coarse prior matching and finer posterior constraint to dynamically assign labels, providing appropriate and relatively balanced supervision for diverse instances. Extensive experiments on six datasets show substantial improvements to the baseline. Notably, we obtain the state-of-the-art performance for one-stage detectors on the DOTA-v1.5, DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes are available at https://github.com/Chasel-Tsui/mmrotate-dcfl. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Dynamic_Coarse-To-Fine_Learning_for_Oriented_Tiny_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Dynamic_Coarse-To-Fine_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08876 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Dynamic_Coarse-To-Fine_Learning_for_Oriented_Tiny_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Dynamic_Coarse-To-Fine_Learning_for_Oriented_Tiny_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models | Zhaoyang Lyu, Jinyi Wang, Yuwei An, Ya Zhang, Dahua Lin, Bo Dai | Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their irregular data structure and inconsistent topology of meshes in the same category. In this work, we design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. While meshes can be generated from point clouds via techniques like Shape as Points (SAP), the challenges of directly generating meshes can be effectively avoided. To boost the efficiency and controllability of our mesh generation method, we propose to further encode point clouds to a set of sparse latent points with point-wise semantic meaningful features, where two DDPMs are trained in the space of sparse latent points to respectively model the distribution of the latent point positions and features at these latent points. We find that sampling in this latent space is faster than directly sampling dense point clouds. Moreover, the sparse latent points also enable us to explicitly control both the overall structures and local details of the generated meshes. Extensive experiments are conducted on the ShapeNet dataset, where our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability when compared to existing methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lyu_Controllable_Mesh_Generation_Through_Sparse_Latent_Point_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lyu_Controllable_Mesh_Generation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07938 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lyu_Controllable_Mesh_Generation_Through_Sparse_Latent_Point_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lyu_Controllable_Mesh_Generation_Through_Sparse_Latent_Point_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Query-Centric Trajectory Prediction | Zikang Zhou, Jianping Wang, Yung-Hui Li, Yu-Kai Huang | Predicting the future trajectories of surrounding agents is essential for autonomous vehicles to operate safely. This paper presents QCNet, a modeling framework toward pushing the boundaries of trajectory prediction. First, we identify that the agent-centric modeling scheme used by existing approaches requires re-normalizing and re-encoding the input whenever the observation window slides forward, leading to redundant computations during online prediction. To overcome this limitation and achieve faster inference, we introduce a query-centric paradigm for scene encoding, which enables the reuse of past computations by learning representations independent of the global spacetime coordinate system. Sharing the invariant scene features among all target agents further allows the parallelism of multi-agent trajectory decoding. Second, even given rich encodings of the scene, existing decoding strategies struggle to capture the multimodality inherent in agents' future behavior, especially when the prediction horizon is long. To tackle this challenge, we first employ anchor-free queries to generate trajectory proposals in a recurrent fashion, which allows the model to utilize different scene contexts when decoding waypoints at different horizons. A refinement module then takes the trajectory proposals as anchors and leverages anchor-based queries to refine the trajectories further. By supplying adaptive and high-quality anchors to the refinement module, our query-based decoder can better deal with the multimodality in the output of trajectory prediction. Our approach ranks 1st on Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming all methods on all main metrics by a large margin. Meanwhile, our model can achieve streaming scene encoding and parallel multi-agent decoding thanks to the query-centric design ethos. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
The Enemy of My Enemy Is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training | Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, Xiaohua Xie | Although current deep learning techniques have yielded superior performance on various computer vision tasks, yet they are still vulnerable to adversarial examples. Adversarial training and its variants have been shown to be the most effective approaches to defend against adversarial examples. A particular class of these methods regularize the difference between output probabilities for an adversarial and its corresponding natural example. However, it may have a negative impact if a natural example is misclassified. To circumvent this issue, we propose a novel adversarial training scheme that encourages the model to produce similar output probabilities for an adversarial example and its "inverse adversarial" counterpart. Particularly, the counterpart is generated by maximizing the likelihood in the neighborhood of the natural example. Extensive experiments on various vision datasets and architectures demonstrate that our training method achieves state-of-the-art robustness as well as natural accuracy among robust models. Furthermore, using a universal version of inverse adversarial examples, we improve the performance of single-step adversarial training techniques at a low computational cost. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_The_Enemy_of_My_Enemy_Is_My_Friend_Exploring_Inverse_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_The_Enemy_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.00525 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_The_Enemy_of_My_Enemy_Is_My_Friend_Exploring_Inverse_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_The_Enemy_of_My_Enemy_Is_My_Friend_Exploring_Inverse_CVPR_2023_paper.html | CVPR 2023 | null |
Look Before You Match: Instance Understanding Matters in Video Object Segmentation | Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Chuanxin Tang, Xiyang Dai, Yucheng Zhao, Yujia Xie, Lu Yuan, Yu-Gang Jiang | Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmentation (VOS) recently. Nevertheless, due to the lack of instance understanding ability, the above approaches are oftentimes brittle to large appearance variations or viewpoint changes resulted from the movement of objects and cameras. In this paper, we argue that instance understanding matters in VOS, and integrating it with memory-based matching can enjoy the synergy, which is intuitively sensible from the definition of VOS task, i.e., identifying and segmenting object instances within the video. Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank. We employ the well-learned object queries from IS branch to inject instance-specific information into the query key, with which the instance-augmented matching is further performed. In addition, we introduce a multi-path fusion block to effectively combine the memory readout with multi-scale features from the instance segmentation decoder, which incorporates high-resolution instance-aware features to produce final segmentation results. Our method achieves state-of-the-art performance on DAVIS 2016/2017 val (92.6% and 87.1%), DAVIS 2017 test-dev (82.8%), and YouTube-VOS 2018/2019 val (86.3% and 86.3%), outperforming alternative methods by clear margins. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Look_Before_You_Match_Instance_Understanding_Matters_in_Video_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Look_Before_You_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06826 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Look_Before_You_Match_Instance_Understanding_Matters_in_Video_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Look_Before_You_Match_Instance_Understanding_Matters_in_Video_Object_CVPR_2023_paper.html | CVPR 2023 | null |
SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization | Wen Li, Shangshu Yu, Cheng Wang, Guosheng Hu, Siqi Shen, Chenglu Wen | LiDAR-based absolute pose regression estimates the global pose through a deep network in an end-to-end manner, achieving impressive results in learning-based localization. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding the scene geometry and the unsatisfactory quality of the data. In this work, we propose a novel LiDAR localization framework, SGLoc, which decouples the pose estimation to point cloud correspondence regression and pose estimation via this correspondence. This decoupling effectively encodes the scene geometry because the decoupled correspondence regression step greatly preserves the scene geometry, leading to significant performance improvement. Apart from this decoupling, we also design a tri-scale spatial feature aggregation module and inter-geometric consistency constraint loss to effectively capture scene geometry. Moreover, we empirically find that the ground truth might be noisy due to GPS/INS measuring errors, greatly reducing the pose estimation performance. Thus, we propose a pose quality evaluation and enhancement method to measure and correct the ground truth pose. Extensive experiments on the Oxford Radar RobotCar and NCLT datasets demonstrate the effectiveness of SGLoc, which outperforms state-of-the-art regression-based localization methods by 68.5% and 67.6% on position accuracy, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SGLoc_Scene_Geometry_Encoding_for_Outdoor_LiDAR_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_SGLoc_Scene_Geometry_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SGLoc_Scene_Geometry_Encoding_for_Outdoor_LiDAR_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SGLoc_Scene_Geometry_Encoding_for_Outdoor_LiDAR_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary | Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, Chen Wang | The practical needs of the "right to be forgotten" and poisoned data removal call for efficient machine unlearning techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lineage. Recent studies on machine unlearning for deep neural networks (DNNs) attempt to destroy the influence of the forgetting data by scrubbing the model parameters. However, it is prohibitively expensive due to the large dimension of the parameter space. In this paper, we refocus our attention from the parameter space to the decision space of the DNN model, and propose Boundary Unlearning, a rapid yet effective way to unlearn an entire class from a trained DNN model. The key idea is to shift the decision boundary of the original DNN model to imitate the decision behavior of the model retrained from scratch. We develop two novel boundary shift methods, namely Boundary Shrink and Boundary Expanding, both of which can rapidly achieve the utility and privacy guarantees. We extensively evaluate Boundary Unlearning on CIFAR-10 and Vggface2 datasets, and the results show that Boundary Unlearning can effectively forget the forgetting class on image classification and face recognition tasks, with an expected speed-up of 17x and 19x, respectively, compared with retraining from the scratch. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Boundary_Unlearning_Rapid_Forgetting_of_Deep_Networks_via_Shifting_the_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Boundary_Unlearning_Rapid_Forgetting_of_Deep_Networks_via_Shifting_the_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Boundary_Unlearning_Rapid_Forgetting_of_Deep_Networks_via_Shifting_the_CVPR_2023_paper.html | CVPR 2023 | null |
Bridging Search Region Interaction With Template for RGB-T Tracking | Tianrui Hui, Zizheng Xun, Fengguang Peng, Junshi Huang, Xiaoming Wei, Xiaolin Wei, Jiao Dai, Jizhong Han, Si Liu | RGB-T tracking aims to leverage the mutual enhancement and complement ability of RGB and TIR modalities for improving the tracking process in various scenarios, where cross-modal interaction is the key component. Some previous methods concatenate the RGB and TIR search region features directly to perform a coarse interaction process with redundant background noises introduced. Many other methods sample candidate boxes from search frames and conduct various fusion approaches on isolated pairs of RGB and TIR boxes, which limits the cross-modal interaction within local regions and brings about inadequate context modeling. To alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions by gathering and distributing target-relevant object and environment contexts. Original templates are also updated with enriched multimodal contexts from the template medium. Our TBSI module is inserted into a ViT backbone for joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three popular RGB-T tracking benchmarks demonstrate our method achieves new state-of-the-art performances. Code is available at https://github.com/RyanHTR/TBSI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hui_Bridging_Search_Region_Interaction_With_Template_for_RGB-T_Tracking_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hui_Bridging_Search_Region_Interaction_With_Template_for_RGB-T_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hui_Bridging_Search_Region_Interaction_With_Template_for_RGB-T_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Indescribable Multi-Modal Spatial Evaluator | Lingke Kong, X. Sharon Qi, Qijin Shen, Jiacheng Wang, Jingyi Zhang, Yanle Hu, Qichao Zhou | Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have different imaging distributions, making it difficult to focus only on the spatial aspect of the images and ignore differences in distributions. In this study, we developed a self-supervised approach, Indescribable Multi-model Spatial Evaluator (IMSE), to address multi-modal image registration. IMSE creates an accurate multi-modal spatial evaluator to measure spatial differences between two images, and then optimizes registration by minimizing the error predicted of the evaluator. To optimize IMSE performance, we also proposed a new style enhancement method called Shuffle Remap which randomizes the image distribution into multiple segments, and then randomly disorders and remaps these segments, so that the distribution of the original image is changed. Shuffle Remap can help IMSE to predict the difference in spatial location from unseen target distributions. Our results show that IMSE outperformed the existing methods for registration using T1-T2 and CT-MRI datasets. IMSE also can be easily integrated into the traditional registration process, and can provide a convenient way to evaluate and visualize registration results. IMSE also has the potential to be used as a new paradigm for image-to-image translation. Our code is available at https://github.com/Kid-Liet/IMSE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kong_Indescribable_Multi-Modal_Spatial_Evaluator_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kong_Indescribable_Multi-Modal_Spatial_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00369 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Indescribable_Multi-Modal_Spatial_Evaluator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Indescribable_Multi-Modal_Spatial_Evaluator_CVPR_2023_paper.html | CVPR 2023 | null |
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