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Objaverse: A Universe of Annotated 3D Objects | Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, Ali Farhadi | Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Deitke_Objaverse_A_Universe_of_Annotated_3D_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Deitke_Objaverse_A_Universe_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deitke_Objaverse_A_Universe_of_Annotated_3D_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deitke_Objaverse_A_Universe_of_Annotated_3D_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
MonoATT: Online Monocular 3D Object Detection With Adaptive Token Transformer | Yunsong Zhou, Hongzi Zhu, Quan Liu, Shan Chang, Minyi Guo | Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt grid-based vision tokens, which is suboptimal when using coarse tokens due to the limited available computational power. In this paper, we propose an online Mono3D framework, called MonoATT, which leverages a novel vision transformer with heterogeneous tokens of varying shapes and sizes to facilitate mobile Mono3D. The core idea of MonoATT is to adaptively assign finer tokens to areas of more significance before utilizing a transformer to enhance Mono3D. To this end, we first use prior knowledge to design a scoring network for selecting the most important areas of the image, and then propose a token clustering and merging network with an attention mechanism to gradually merge tokens around the selected areas in multiple stages. Finally, a pixel-level feature map is reconstructed from heterogeneous tokens before employing a SOTA Mono3D detector as the underlying detection core. Experiment results on the real-world KITTI dataset demonstrate that MonoATT can effectively improve the Mono3D accuracy for both near and far objects and guarantee low latency. MonoATT yields the best performance compared with the state-of-the-art methods by a large margin and is ranked number one on the KITTI 3D benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_MonoATT_Online_Monocular_3D_Object_Detection_With_Adaptive_Token_Transformer_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.13018 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_MonoATT_Online_Monocular_3D_Object_Detection_With_Adaptive_Token_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_MonoATT_Online_Monocular_3D_Object_Detection_With_Adaptive_Token_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Image Quality-Aware Diagnosis via Meta-Knowledge Co-Embedding | Haoxuan Che, Siyu Chen, Hao Chen | Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge of degradations, models can better resist their adverse effects and avoid misdiagnosis. In this paper, we raise the problem of image quality-aware diagnosis, which aims to take advantage of low-quality images and image quality labels to achieve a more accurate and robust diagnosis. However, the diversity of degradations and superficially unrelated targets between image quality assessment and disease diagnosis makes it still quite challenging to effectively leverage quality labels to assist diagnosis. Thus, to tackle these issues, we propose a novel meta-knowledge co-embedding network, consisting of two subnets: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features, while Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking. Superior performance on five datasets with four widely-used medical imaging modalities demonstrates the effectiveness and generalizability of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Che_Image_Quality-Aware_Diagnosis_via_Meta-Knowledge_Co-Embedding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Che_Image_Quality-Aware_Diagnosis_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15038 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Che_Image_Quality-Aware_Diagnosis_via_Meta-Knowledge_Co-Embedding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Che_Image_Quality-Aware_Diagnosis_via_Meta-Knowledge_Co-Embedding_CVPR_2023_paper.html | CVPR 2023 | null |
A-Cap: Anticipation Captioning With Commonsense Knowledge | Duc Minh Vo, Quoc-An Luong, Akihiro Sugimoto, Hideki Nakayama | Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To tackle this new task, we propose a model called A-CAP, which incorporates commonsense knowledge into a pre-trained vision-language model, allowing it to anticipate the caption. Through both qualitative and quantitative evaluations on a customized visual storytelling dataset, A-CAP outperforms other image captioning methods and establishes a strong baseline for anticipation captioning. We also address the challenges inherent in this task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Vo_A-Cap_Anticipation_Captioning_With_Commonsense_Knowledge_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vo_A-Cap_Anticipation_Captioning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vo_A-Cap_Anticipation_Captioning_With_Commonsense_Knowledge_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vo_A-Cap_Anticipation_Captioning_With_Commonsense_Knowledge_CVPR_2023_paper.html | CVPR 2023 | null |
Learning 3D Representations From 2D Pre-Trained Models via Image-to-Point Masked Autoencoders | Renrui Zhang, Liuhui Wang, Yu Qiao, Peng Gao, Hongsheng Li | Pre-training by numerous image data has become de-facto for robust 2D representations. In contrast, due to the expensive data processing, a paucity of 3D datasets severely hinders the learning for high-quality 3D features. In this paper, we propose an alternative to obtain superior 3D representations from 2D pre-trained models via Image-to-Point Masked Autoencoders, named as I2P-MAE. By self-supervised pre-training, we leverage the well learned 2D knowledge to guide 3D masked autoencoding, which reconstructs the masked point tokens with an encoder-decoder architecture. Specifically, we first utilize off-the-shelf 2D models to extract the multi-view visual features of the input point cloud, and then conduct two types of image-to-point learning schemes. For one, we introduce a 2D-guided masking strategy that maintains semantically important point tokens to be visible. Compared to random masking, the network can better concentrate on significant 3D structures with key spatial cues. For another, we enforce these visible tokens to reconstruct multi-view 2D features after the decoder. This enables the network to effectively inherit high-level 2D semantics for discriminative 3D modeling. Aided by our image-to-point pre-training, the frozen I2P-MAE, without any fine-tuning, achieves 93.4% accuracy for linear SVM on ModelNet40, competitive to existing fully trained methods. By further fine-tuning on on ScanObjectNN's hardest split, I2P-MAE attains the state-of-the-art 90.11% accuracy, +3.68% to the second-best, demonstrating superior transferable capacity. Code is available at https://github.com/ZrrSkywalker/I2P-MAE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Learning_3D_Representations_From_2D_Pre-Trained_Models_via_Image-to-Point_Masked_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Learning_3D_Representations_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_3D_Representations_From_2D_Pre-Trained_Models_via_Image-to-Point_Masked_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_3D_Representations_From_2D_Pre-Trained_Models_via_Image-to-Point_Masked_CVPR_2023_paper.html | CVPR 2023 | null |
BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision | Chenyu Yang, Yuntao Chen, Hao Tian, Chenxin Tao, Xizhou Zhu, Zhaoxiang Zhang, Gao Huang, Hongyang Li, Yu Qiao, Lewei Lu, Jie Zhou, Jifeng Dai | We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detectors are often tied to certain depth pre-trained backbones like VoVNet, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective space supervision. To this end, we propose a two-stage BEV detector, where proposals from the perspective head are fed into the bird's-eye-view head for final predictions. To evaluate the effectiveness of our model, we conduct extensive ablation studies focusing on the form of supervision and the generality of the proposed detector. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset. The code shall be released soon. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_BEVFormer_v2_Adapting_Modern_Image_Backbones_to_Birds-Eye-View_Recognition_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_BEVFormer_v2_Adapting_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BEVFormer_v2_Adapting_Modern_Image_Backbones_to_Birds-Eye-View_Recognition_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BEVFormer_v2_Adapting_Modern_Image_Backbones_to_Birds-Eye-View_Recognition_via_CVPR_2023_paper.html | CVPR 2023 | null |
Object Discovery From Motion-Guided Tokens | Zhipeng Bao, Pavel Tokmakov, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert | Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision. Previous methods struggle to go beyond clustering of low-level cues, whether handcrafted (e.g., color, texture) or learned (e.g., from auto-encoders). In this work, we augment the auto-encoder representation learning framework with two key components: motion-guidance and mid-level feature tokenization. Although both have been separately investigated, we introduce a new transformer decoder showing that their benefits can compound thanks to motion-guided vector quantization. We show that our architecture effectively leverages the synergy between motion and tokenization, improving upon the state of the art on both synthetic and real datasets. Our approach enables the emergence of interpretable object-specific mid-level features, demonstrating the benefits of motion-guidance (no labeling) and quantization (interpretability, memory efficiency). | https://openaccess.thecvf.com/content/CVPR2023/papers/Bao_Object_Discovery_From_Motion-Guided_Tokens_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bao_Object_Discovery_From_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.15555 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_Object_Discovery_From_Motion-Guided_Tokens_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_Object_Discovery_From_Motion-Guided_Tokens_CVPR_2023_paper.html | CVPR 2023 | null |
Domain Generalized Stereo Matching via Hierarchical Visual Transformation | Tianyu Chang, Xun Yang, Tianzhu Zhang, Meng Wang | Recently, deep Stereo Matching (SM) networks have shown impressive performance and attracted increasing attention in computer vision. However, existing deep SM networks are prone to learn dataset-dependent shortcuts, which fail to generalize well on unseen realistic datasets. This paper takes a step towards training robust models for the domain generalized SM task, which mainly focuses on learning shortcut-invariant representation from synthetic data to alleviate the domain shifts. Specifically, we propose a Hierarchical Visual Transformation (HVT) network to 1) first transform the training sample hierarchically into new domains with diverse distributions from three levels: Global, Local, and Pixel, 2) then maximize the visual discrepancy between the source domain and new domains, and minimize the cross-domain feature inconsistency to capture domain-invariant features. In this way, we can prevent the model from exploiting the artifacts of synthetic stereo images as shortcut features, thereby estimating the disparity maps more effectively based on the learned robust and shortcut-invariant representation. We integrate our proposed HVT network with SOTA SM networks and evaluate its effectiveness on several public SM benchmark datasets. Extensive experiments clearly show that the HVT network can substantially enhance the performance of existing SM networks in synthetic-to-realistic domain generalization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chang_Domain_Generalized_Stereo_Matching_via_Hierarchical_Visual_Transformation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chang_Domain_Generalized_Stereo_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Domain_Generalized_Stereo_Matching_via_Hierarchical_Visual_Transformation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chang_Domain_Generalized_Stereo_Matching_via_Hierarchical_Visual_Transformation_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Semi-Supervised Metric Learning With Mixed Label Propagation | Furen Zhuang, Pierre Moulin | Metric learning requires the identification of far-apart similar pairs and close dissimilar pairs during training, and this is difficult to achieve with unlabeled data because pairs are typically assumed to be similar if they are close. We present a novel metric learning method which circumvents this issue by identifying hard negative pairs as those which obtain dissimilar labels via label propagation (LP), when the edge linking the pair of data is removed in the affinity matrix. In so doing, the negative pairs can be identified despite their proximity, and we are able to utilize this information to significantly improve LP's ability to identify far-apart positive pairs and close negative pairs. This results in a considerable improvement in semi-supervised metric learning performance as evidenced by recall, precision and Normalized Mutual Information (NMI) performance metrics on Content-based Information Retrieval (CBIR) applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhuang_Deep_Semi-Supervised_Metric_Learning_With_Mixed_Label_Propagation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhuang_Deep_Semi-Supervised_Metric_Learning_With_Mixed_Label_Propagation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhuang_Deep_Semi-Supervised_Metric_Learning_With_Mixed_Label_Propagation_CVPR_2023_paper.html | CVPR 2023 | null |
Adapting Shortcut With Normalizing Flow: An Efficient Tuning Framework for Visual Recognition | Yaoming Wang, Bowen Shi, Xiaopeng Zhang, Jin Li, Yuchen Liu, Wenrui Dai, Chenglin Li, Hongkai Xiong, Qi Tian | Pretraining followed by fine-tuning has proven to be effective in visual recognition tasks. However, fine-tuning all parameters can be computationally expensive, particularly for large-scale models. To mitigate the computational and storage demands, recent research has explored Parameter-Efficient Fine-Tuning (PEFT), which focuses on tuning a minimal number of parameters for efficient adaptation. Existing methods, however, fail to analyze the impact of the additional parameters on the model, resulting in an unclear and suboptimal tuning process. In this paper, we introduce a novel and effective PEFT paradigm, named SNF (Shortcut adaptation via Normalization Flow), which utilizes normalizing flows to adjust the shortcut layers. We highlight that layers without Lipschitz constraints can lead to error propagation when adapting to downstream datasets. Since modifying the over-parameterized residual connections in these layers is expensive, we focus on adjusting the cheap yet crucial shortcuts. Moreover, learning new information with few parameters in PEFT can be challenging, and information loss can result in label information degradation. To address this issue, we propose an information-preserving normalizing flow. Experimental results demonstrate the effectiveness of SNF. Specifically, with only 0.036M parameters, SNF surpasses previous approaches on both the FGVC and VTAB-1k benchmarks using ViT/B-16 as the backbone. The code is available at https://github.com/Wang-Yaoming/SNF | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Adapting_Shortcut_With_Normalizing_Flow_An_Efficient_Tuning_Framework_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Adapting_Shortcut_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Adapting_Shortcut_With_Normalizing_Flow_An_Efficient_Tuning_Framework_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Adapting_Shortcut_With_Normalizing_Flow_An_Efficient_Tuning_Framework_for_CVPR_2023_paper.html | CVPR 2023 | null |
Unpaired Image-to-Image Translation With Shortest Path Regularization | Shaoan Xie, Yanwu Xu, Mingming Gong, Kun Zhang | Unpaired image-to-image translation aims to learn proper mappings that can map images from one domain to another domain while preserving the content of the input image. However, with large enough capacities, the network can learn to map the inputs to any random permutation of images in another domain. Existing methods treat two domains as discrete and propose different assumptions to address this problem. In this paper, we start from a different perspective and consider the paths connecting the two domains. We assume that the optimal path length between the input and output image should be the shortest among all possible paths. Based on this assumption, we propose a new method to allow generating images along the path and present a simple way to encourage the network to find the shortest path without pair information. Extensive experiments on various tasks demonstrate the superiority of our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Unpaired_Image-to-Image_Translation_With_Shortest_Path_Regularization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Unpaired_Image-to-Image_Translation_With_Shortest_Path_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Unpaired_Image-to-Image_Translation_With_Shortest_Path_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
MotionDiffuser: Controllable Multi-Agent Motion Prediction Using Diffusion | Chiyu “Max” Jiang, Andre Cornman, Cheolho Park, Benjamin Sapp, Yin Zhou, Dragomir Anguelov | We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimodal distribution that captures diverse future outcomes. Second, the simple predictor design requires only a single L2 loss training objective, and does not depend on trajectory anchors. Third, our model is capable of learning the joint distribution for the motion of multiple agents in a permutation-invariant manner. Furthermore, we utilize a compressed trajectory representation via PCA, which improves model performance and allows for efficient computation of the exact sample log probability. Subsequently, we propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. This strategy enables a host of applications such as enforcing rules and physical priors, or creating tailored simulation scenarios. MotionDiffuser can be combined with existing backbone architectures to achieve top motion forecasting results. We obtain state-of-the-art results for multi-agent motion prediction on the Waymo Open Motion Dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_MotionDiffuser_Controllable_Multi-Agent_Motion_Prediction_Using_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_MotionDiffuser_Controllable_Multi-Agent_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_MotionDiffuser_Controllable_Multi-Agent_Motion_Prediction_Using_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_MotionDiffuser_Controllable_Multi-Agent_Motion_Prediction_Using_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
OVTrack: Open-Vocabulary Multiple Object Tracking | Siyuan Li, Tobias Fischer, Lei Ke, Henghui Ding, Martin Danelljan, Fisher Yu | The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional multiple object tracking (MOT) benchmarks rely only on a few object categories that hardly represent the multitude of possible objects that are encountered in the real world. This leaves contemporary MOT methods limited to a small set of pre-defined object categories. In this paper, we address this limitation by tackling a novel task, open-vocabulary MOT, that aims to evaluate tracking beyond pre-defined training categories. We further develop OVTrack, an open-vocabulary tracker that is capable of tracking arbitrary object classes. Its design is based on two key ingredients: First, leveraging vision-language models for both classification and association via knowledge distillation; second, a data hallucination strategy for robust appearance feature learning from denoising diffusion probabilistic models. The result is an extremely data-efficient open-vocabulary tracker that sets a new state-of-the-art on the large-scale, large-vocabulary TAO benchmark, while being trained solely on static images. The project page is at https://www.vis.xyz/pub/ovtrack/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_OVTrack_Open-Vocabulary_Multiple_Object_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_OVTrack_Open-Vocabulary_Multiple_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08408 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_OVTrack_Open-Vocabulary_Multiple_Object_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_OVTrack_Open-Vocabulary_Multiple_Object_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
ConvNeXt V2: Co-Designing and Scaling ConvNets With Masked Autoencoders | Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie | Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt models, have demonstrated strong performance across different application scenarios. Like many other architectures, ConvNeXt models were designed under the supervised learning setting with ImageNet labels. It is natural to expect ConvNeXt can also benefit from state-of-the-art self-supervised learning frameworks such as masked autoencoders (MAE), which was originally designed with Transformers. However, we show that simply combining the two designs yields subpar performance. In this paper, we develop an efficient and fully-convolutional masked autoencoder framework. We then upgrade the ConvNeXt architecture with a new Global Response Normalization (GRN) layer. GRN enhances inter-channel feature competition and is crucial for pre-training with masked input. The new model family, dubbed ConvNeXt V2, is a complete training recipe that synergizes both the architectural improvement and the advancement in self-supervised learning. With ConvNeXt V2, we are able to significantly advance pure ConvNets' performance across different recognition benchmarks including ImageNet classification, ADE20K segmentation and COCO detection. To accommodate different use cases, we provide pre-trained ConvNeXt V2 models of a wide range of complexity: from an efficient 3.7M-parameter Atto model that achieves 76.8% top-1 accuracy on ImageNet, to a 650M Huge model that can reach a state-of-the-art 88.9% accuracy using public training data only. | https://openaccess.thecvf.com/content/CVPR2023/papers/Woo_ConvNeXt_V2_Co-Designing_and_Scaling_ConvNets_With_Masked_Autoencoders_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Woo_ConvNeXt_V2_Co-Designing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.00808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Woo_ConvNeXt_V2_Co-Designing_and_Scaling_ConvNets_With_Masked_Autoencoders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Woo_ConvNeXt_V2_Co-Designing_and_Scaling_ConvNets_With_Masked_Autoencoders_CVPR_2023_paper.html | CVPR 2023 | null |
Hyperspherical Embedding for Point Cloud Completion | Junming Zhang, Haomeng Zhang, Ram Vasudevan, Matthew Johnson-Roberson | Most real-world 3D measurements from depth sensors are incomplete, and to address this issue the point cloud completion task aims to predict the complete shapes of objects from partial observations. Previous works often adapt an encoder-decoder architecture, where the encoder is trained to extract embeddings that are used as inputs to generate predictions from the decoder. However, the learned embeddings have sparse distribution in the feature space, which leads to worse generalization results during testing. To address these problems, this paper proposes a hyperspherical module, which transforms and normalizes embeddings from the encoder to be on a unit hypersphere. With the proposed module, the magnitude and direction of the output hyperspherical embedding are decoupled and only the directional information is optimized. We theoretically analyze the hyperspherical embedding and show that it enables more stable training with a wider range of learning rates and more compact embedding distributions. Experiment results show consistent improvement of point cloud completion in both single-task and multi-task learning, which demonstrates the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Hyperspherical_Embedding_for_Point_Cloud_Completion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Hyperspherical_Embedding_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Hyperspherical_Embedding_for_Point_Cloud_Completion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Hyperspherical_Embedding_for_Point_Cloud_Completion_CVPR_2023_paper.html | CVPR 2023 | null |
Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields | Taewoo Kim, Yujeong Chae, Hyun-Kurl Jang, Kuk-Jin Yoon | Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods.Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets.Our project pages are available at: https://github.com/intelpro/CBMNet | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Event-Based_Video_Frame_Interpolation_With_Cross-Modal_Asymmetric_Bidirectional_Motion_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Event-Based_Video_Frame_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Event-Based_Video_Frame_Interpolation_With_Cross-Modal_Asymmetric_Bidirectional_Motion_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Event-Based_Video_Frame_Interpolation_With_Cross-Modal_Asymmetric_Bidirectional_Motion_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity | Taeyong Song, Sunok Kim, Kwanghoon Sohn | Unsupervised stereo matching has received a lot of attention since it enables the learning of disparity estimation without ground-truth data. However, most of the unsupervised stereo matching algorithms assume that the left and right images have consistent visual properties, i.e., symmetric, and easily fail when the stereo images are asymmetric. In this paper, we present a novel spatially-adaptive self-similarity (SASS) for unsupervised asymmetric stereo matching. It extends the concept of self-similarity and generates deep features that are robust to the asymmetries. The sampling patterns to calculate self-similarities are adaptively generated throughout the image regions to effectively encode diverse patterns. In order to learn the effective sampling patterns, we design a contrastive similarity loss with positive and negative weights. Consequently, SASS is further encouraged to encode asymmetry-agnostic features, while maintaining the distinctiveness for stereo correspondence. We present extensive experimental results including ablation studies and comparisons with different methods, demonstrating effectiveness of the proposed method under resolution and noise asymmetries. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Unsupervised_Deep_Asymmetric_Stereo_Matching_With_Spatially-Adaptive_Self-Similarity_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_Unsupervised_Deep_Asymmetric_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Unsupervised_Deep_Asymmetric_Stereo_Matching_With_Spatially-Adaptive_Self-Similarity_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Unsupervised_Deep_Asymmetric_Stereo_Matching_With_Spatially-Adaptive_Self-Similarity_CVPR_2023_paper.html | CVPR 2023 | null |
QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity | Siyu Huang, Jie An, Donglai Wei, Jiebo Luo, Hanspeter Pfister | The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style. However, this type of approach cannot guarantee visual fidelity, i.e., the generated artworks should be indistinguishable from real ones. In this paper, we devise a new style transfer framework called QuantArt for high visual-fidelity stylization. QuantArt pushes the latent representation of the generated artwork toward the centroids of the real artwork distribution with vector quantization. By fusing the quantized and continuous latent representations, QuantArt allows flexible control over the generated artworks in terms of content preservation, style similarity, and visual fidelity. Experiments on various style transfer settings show that our QuantArt framework achieves significantly higher visual fidelity compared with the existing style transfer methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_QuantArt_Quantizing_Image_Style_Transfer_Towards_High_Visual_Fidelity_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_QuantArt_Quantizing_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.10431 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_QuantArt_Quantizing_Image_Style_Transfer_Towards_High_Visual_Fidelity_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_QuantArt_Quantizing_Image_Style_Transfer_Towards_High_Visual_Fidelity_CVPR_2023_paper.html | CVPR 2023 | null |
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization | Ziquan Liu, Yi Xu, Xiangyang Ji, Antoni B. Chan | Recent years have seen the ever-increasing importance of pre-trained models and their downstream training in deep learning research and applications. At the same time, the defense for adversarial examples has been mainly investigated in the context of training from random initialization on simple classification tasks. To better exploit the potential of pre-trained models in adversarial robustness, this paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. Existing research has shown that since the robust pre-trained model has already learned a robust feature extractor, the crucial question is how to maintain the robustness in the pre-trained model when learning the downstream task. We study the model-based and data-based approaches for this goal and find that the two common approaches cannot achieve the objective of improving both generalization and adversarial robustness. Thus, we propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework, which consists of two neural networks where one of them keeps the population means and variances of pre-training data in the batch normalization layers. Besides the robust information transfer, TWINS increases the effective learning rate without hurting the training stability since the relationship between a weight norm and its gradient norm in standard batch normalization layer is broken, resulting in a faster escape from the sub-optimal initialization and alleviating the robust overfitting. Finally, TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_TWINS_A_Fine-Tuning_Framework_for_Improved_Transferability_of_Adversarial_Robustness_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_TWINS_A_Fine-Tuning_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.11135 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_TWINS_A_Fine-Tuning_Framework_for_Improved_Transferability_of_Adversarial_Robustness_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_TWINS_A_Fine-Tuning_Framework_for_Improved_Transferability_of_Adversarial_Robustness_CVPR_2023_paper.html | CVPR 2023 | null |
VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction | Yufan Ren, Fangjinhua Wang, Tong Zhang, Marc Pollefeys, Sabine Süsstrunk | The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize per-scene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct the scene with fine details and little noise, VolRecon combines projection features aggregated from multi-view features, and volume features interpolated from a coarse global feature volume. Using a ray transformer, we compute SRDF values of sampled points on a ray and then render color and depth. On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction. Furthermore, our approach exhibits good generalization performance on the large-scale ETH3D benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_VolRecon_Volume_Rendering_of_Signed_Ray_Distance_Functions_for_Generalizable_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ren_VolRecon_Volume_Rendering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08067 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_VolRecon_Volume_Rendering_of_Signed_Ray_Distance_Functions_for_Generalizable_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_VolRecon_Volume_Rendering_of_Signed_Ray_Distance_Functions_for_Generalizable_CVPR_2023_paper.html | CVPR 2023 | null |
Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection | Luting Wang, Yi Liu, Penghui Du, Zihan Ding, Yue Liao, Qiaosong Qi, Biaolong Chen, Si Liu | Open-vocabulary object detection aims to provide object detectors trained on a fixed set of object categories with the generalizability to detect objects described by arbitrary text queries. Previous methods adopt knowledge distillation to extract knowledge from Pretrained Vision-and-Language Models (PVLMs) and transfer it to detectors. However, due to the non-adaptive proposal cropping and single-level feature mimicking processes, they suffer from information destruction during knowledge extraction and inefficient knowledge transfer. To remedy these limitations, we propose an Object-Aware Distillation Pyramid (OADP) framework, including an Object-Aware Knowledge Extraction (OAKE) module and a Distillation Pyramid (DP) mechanism. When extracting object knowledge from PVLMs, the former adaptively transforms object proposals and adopts object-aware mask attention to obtain precise and complete knowledge of objects. The latter introduces global and block distillation for more comprehensive knowledge transfer to compensate for the missing relation information in object distillation. Extensive experiments show that our method achieves significant improvement compared to current methods. Especially on the MS-COCO dataset, our OADP framework reaches 35.6 mAP^N_50, surpassing the current state-of-the-art method by 3.3 mAP^N_50. Code is anonymously provided in the supplementary materials. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Object-Aware_Distillation_Pyramid_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.05892 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Object-Aware_Distillation_Pyramid_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Object-Aware_Distillation_Pyramid_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Evolved Part Masking for Self-Supervised Learning | Zhanzhou Feng, Shiliang Zhang | Existing Masked Image Modeling methods apply fixed mask patterns to guide the self-supervised training. As those patterns resort to different criteria to mask local regions, sticking to a fixed pattern leads to limited vision cues modeling capability. This paper proposes an evolved part-based masking to pursue more general visual cues modeling in self-supervised learning. Our method is based on an adaptive part partition module, which leverages the vision model being trained to construct a part graph, and partitions parts with graph cut. The accuracy of partitioned parts is on par with the capability of the pre-trained model, leading to evolved mask patterns at different training stages. It generates simple patterns at the initial training stage to learn low-level visual cues, which hence evolves to eliminate accurate object parts to reinforce the learning of object semantics and contexts. Our method does not require extra pre-trained models or annotations, and effectively ensures the training efficiency by evolving the training difficulty. Experiment results show that it substantially boosts the performance on various tasks including image classification, object detection, and semantic segmentation. For example, it outperforms the recent MAE by 0.69% on imageNet-1K classification and 1.61% on ADE20K segmentation with the same training epochs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Evolved_Part_Masking_for_Self-Supervised_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Evolved_Part_Masking_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Evolved_Part_Masking_for_Self-Supervised_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Evolved_Part_Masking_for_Self-Supervised_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training | Runsen Xu, Tai Wang, Wenwei Zhang, Runjian Chen, Jinkun Cao, Jiangmiao Pang, Dahua Lin | This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object detection benchmark on the Waymo dataset. Inspired by the scene-voxel-point hierarchy in downstream 3D object detectors, we design masking and reconstruction strategies accounting for voxel distributions in the scene and local point distributions within the voxel. We employ a Reversed-Furthest-Voxel-Sampling strategy to address the uneven distribution of LiDAR points and propose MV-JAR, which combines two techniques for modeling the aforementioned distributions, resulting in superior performance. Our experiments reveal limitations in previous data-efficient experiments, which uniformly sample fine-tuning splits with varying data proportions from each LiDAR sequence, leading to similar data diversity across splits. To address this, we propose a new benchmark that samples scene sequences for diverse fine-tuning splits, ensuring adequate model convergence and providing a more accurate evaluation of pre-training methods. Experiments on our Waymo benchmark and the KITTI dataset demonstrate that MV-JAR consistently and significantly improves 3D detection performance across various data scales, achieving up to a 6.3% increase in mAPH compared to training from scratch. Codes and the benchmark are available at https://github.com/SmartBot-PJLab/MV-JAR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_MV-JAR_Masked_Voxel_Jigsaw_and_Reconstruction_for_LiDAR-Based_Self-Supervised_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_MV-JAR_Masked_Voxel_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_MV-JAR_Masked_Voxel_Jigsaw_and_Reconstruction_for_LiDAR-Based_Self-Supervised_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_MV-JAR_Masked_Voxel_Jigsaw_and_Reconstruction_for_LiDAR-Based_Self-Supervised_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples | Han Liu, Yuhao Wu, Zhiyuan Yu, Yevgeniy Vorobeychik, Ning Zhang | LiDAR-based perception is a central component of autonomous driving, playing a key role in tasks such as vehicle localization and obstacle detection. Since the safety of LiDAR-based perceptual pipelines is critical to safe autonomous driving, a number of past efforts have investigated its vulnerability under adversarial perturbations of raw point cloud inputs. However, most such efforts have focused on investigating the impact of such perturbations on predictions (integrity), and little has been done to understand the impact on latency (availability), a critical concern for real-time cyber-physical systems. We present the first systematic investigation of the availability of LiDAR detection pipelines, and SlowLiDAR, an adversarial perturbation attack that maximizes LiDAR detection runtime. The attack overcomes the technical challenges posed by the non-differentiable parts of the LiDAR detection pipelines by using differentiable proxies and uses a novel loss function that effectively captures the impact of adversarial perturbations on the execution time of the pipeline. Extensive experimental results show that SlowLiDAR can significantly increase the latency of the six most popular LiDAR detection pipelines while maintaining imperceptibility. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_SlowLiDAR_Increasing_the_Latency_of_LiDAR-Based_Detection_Using_Adversarial_Examples_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SlowLiDAR_Increasing_the_Latency_of_LiDAR-Based_Detection_Using_Adversarial_Examples_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SlowLiDAR_Increasing_the_Latency_of_LiDAR-Based_Detection_Using_Adversarial_Examples_CVPR_2023_paper.html | CVPR 2023 | null |
Learning a Sparse Transformer Network for Effective Image Deraining | Xiang Chen, Hao Li, Mingqiang Li, Jinshan Pan | Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Learning_a_Sparse_Transformer_Network_for_Effective_Image_Deraining_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Learning_a_Sparse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_a_Sparse_Transformer_Network_for_Effective_Image_Deraining_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_a_Sparse_Transformer_Network_for_Effective_Image_Deraining_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Set Semantic Segmentation for Point Clouds via Adversarial Prototype Framework | Jianan Li, Qiulei Dong | Recently, point cloud semantic segmentation has attracted much attention in computer vision. Most of the existing works in literature assume that the training and testing point clouds have the same object classes, but they are generally invalid in many real-world scenarios for identifying the 3D objects whose classes are not seen in the training set. To address this problem, we propose an Adversarial Prototype Framework (APF) for handling the open-set 3D semantic segmentation task, which aims to identify 3D unseen-class points while maintaining the segmentation performance on seen-class points. The proposed APF consists of a feature extraction module for extracting point features, a prototypical constraint module, and a feature adversarial module. The prototypical constraint module is designed to learn prototypes for each seen class from point features. The feature adversarial module utilizes generative adversarial networks to estimate the distribution of unseen-class features implicitly, and the synthetic unseen-class features are utilized to prompt the model to learn more effective point features and prototypes for discriminating unseen-class samples from the seen-class ones. Experimental results on two public datasets demonstrate that the proposed APF outperforms the comparative methods by a large margin in most cases. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Open-Set_Semantic_Segmentation_for_Point_Clouds_via_Adversarial_Prototype_Framework_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Open-Set_Semantic_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Open-Set_Semantic_Segmentation_for_Point_Clouds_via_Adversarial_Prototype_Framework_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Open-Set_Semantic_Segmentation_for_Point_Clouds_via_Adversarial_Prototype_Framework_CVPR_2023_paper.html | CVPR 2023 | null |
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending | Zeyu Xiao, Yutong Liu, Ruisheng Gao, Zhiwei Xiong | Data augmentation (DA) is an efficient strategy for improving the performance of deep neural networks. Recent DA strategies have demonstrated utility in single image super-resolution (SR). Little research has, however, focused on the DA strategy for light field SR, in which multi-view information utilization is required. For the first time in light field SR, we propose a potent DA strategy called CutMIB to improve the performance of existing light field SR networks while keeping their structures unchanged. Specifically, CutMIB first cuts low-resolution (LR) patches from each view at the same location. Then CutMIB blends all LR patches to generate the blended patch and finally pastes the blended patch to the corresponding regions of high-resolution light field views, and vice versa. By doing so, CutMIB enables light field SR networks to learn from implicit geometric information during the training stage. Experimental results demonstrate that CutMIB can improve the reconstruction performance and the angular consistency of existing light field SR networks. We further verify the effectiveness of CutMIB on real-world light field SR and light field denoising. The implementation code is available at https://github.com/zeyuxiao1997/CutMIB. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_CutMIB_Boosting_Light_Field_Super-Resolution_via_Multi-View_Image_Blending_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiao_CutMIB_Boosting_Light_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_CutMIB_Boosting_Light_Field_Super-Resolution_via_Multi-View_Image_Blending_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_CutMIB_Boosting_Light_Field_Super-Resolution_via_Multi-View_Image_Blending_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Attention As Disentangler for Compositional Zero-Shot Learning | Shaozhe Hao, Kai Han, Kwan-Yee K. Wong | Compositional zero-shot learning (CZSL) aims at learning visual concepts (i.e., attributes and objects) from seen compositions and combining concept knowledge into unseen compositions. The key to CZSL is learning the disentanglement of the attribute-object composition. To this end, we propose to exploit cross-attentions as compositional disentanglers to learn disentangled concept embeddings. For example, if we want to recognize an unseen composition "yellow flower", we can learn the attribute concept "yellow" and object concept "flower" from different yellow objects and different flowers respectively. To further constrain the disentanglers to learn the concept of interest, we employ a regularization at the attention level. Specifically, we adapt the earth mover's distance (EMD) as a feature similarity metric in the cross-attention module. Moreover, benefiting from concept disentanglement, we improve the inference process and tune the prediction score by combining multiple concept probabilities. Comprehensive experiments on three CZSL benchmark datasets demonstrate that our method significantly outperforms previous works in both closed- and open-world settings, establishing a new state-of-the-art. Project page: https://haoosz.github.io/ade-czsl/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Hao_Learning_Attention_As_Disentangler_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hao_Learning_Attention_As_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15111 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hao_Learning_Attention_As_Disentangler_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hao_Learning_Attention_As_Disentangler_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
DA-DETR: Domain Adaptive Detection Transformer With Information Fusion | Jingyi Zhang, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Xiaoqin Zhang, Shijian Lu | The recent detection transformer (DETR) simplifies the object detection pipeline by removing hand-crafted designs and hyperparameters as employed in conventional two-stage object detectors. However, how to leverage the simple yet effective DETR architecture in domain adaptive object detection is largely neglected. Inspired by the unique DETR attention mechanisms, we design DA-DETR, a domain adaptive object detection transformer that introduces information fusion for effective transfer from a labeled source domain to an unlabeled target domain. DA-DETR introduces a novel CNN-Transformer Blender (CTBlender) that fuses the CNN features and Transformer features ingeniously for effective feature alignment and knowledge transfer across domains. Specifically, CTBlender employs the Transformer features to modulate the CNN features across multiple scales where the high-level semantic information and the low-level spatial information are fused for accurate object identification and localization. Extensive experiments show that DA-DETR achieves superior detection performance consistently across multiple widely adopted domain adaptation benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_DA-DETR_Domain_Adaptive_Detection_Transformer_With_Information_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_DA-DETR_Domain_Adaptive_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DA-DETR_Domain_Adaptive_Detection_Transformer_With_Information_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DA-DETR_Domain_Adaptive_Detection_Transformer_With_Information_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
Energy-Efficient Adaptive 3D Sensing | Brevin Tilmon, Zhanghao Sun, Sanjeev J. Koppal, Yicheng Wu, Georgios Evangelidis, Ramzi Zahreddine, Gurunandan Krishnan, Sizhuo Ma, Jian Wang | Active depth sensing achieves robust depth estimation but is usually limited by the sensing range. Naively increasing the optical power can improve sensing range but induces eye-safety concerns for many applications, including autonomous robots and augmented reality. In this paper, we propose an adaptive active depth sensor that jointly optimizes range, power consumption, and eye-safety. The main observation is that we need not project light patterns to the entire scene but only to small regions of interest where depth is necessary for the application and passive stereo depth estimation fails. We theoretically compare this adaptive sensing scheme with other sensing strategies, such as full-frame projection, line scanning, and point scanning. We show that, to achieve the same maximum sensing distance, the proposed method consumes the least power while having the shortest (best) eye-safety distance. We implement this adaptive sensing scheme with two hardware prototypes, one with a phase-only spatial light modulator (SLM) and the other with a micro-electro-mechanical (MEMS) mirror and diffractive optical elements (DOE). Experimental results validate the advantage of our method and demonstrate its capability of acquiring higher quality geometry adaptively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tilmon_Energy-Efficient_Adaptive_3D_Sensing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tilmon_Energy-Efficient_Adaptive_3D_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tilmon_Energy-Efficient_Adaptive_3D_Sensing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tilmon_Energy-Efficient_Adaptive_3D_Sensing_CVPR_2023_paper.html | CVPR 2023 | null |
CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability | Fadi Boutros, Meiling Fang, Marcel Klemt, Biying Fu, Naser Damer | Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Boutros_CR-FIQA_Face_Image_Quality_Assessment_by_Learning_Sample_Relative_Classifiability_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Boutros_CR-FIQA_Face_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Boutros_CR-FIQA_Face_Image_Quality_Assessment_by_Learning_Sample_Relative_Classifiability_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Boutros_CR-FIQA_Face_Image_Quality_Assessment_by_Learning_Sample_Relative_Classifiability_CVPR_2023_paper.html | CVPR 2023 | null |
Endpoints Weight Fusion for Class Incremental Semantic Segmentation | Jia-Wen Xiao, Chang-Bin Zhang, Jiekang Feng, Xialei Liu, Joost van de Weijer, Ming-Ming Cheng | Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploit regularization (e.g., knowledge distillation) to maintain previous knowledge in the current model. However, distillation alone often yields limited gain to the model since only the representations of old and new models are restricted to be consistent. In this paper, we propose a simple yet effective method to obtain a model with strong memory of old knowledge, named Endpoints Weight Fusion (EWF). In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions. In addition, we analyze the relation between our fusion strategy and a popular moving average technique EMA, which reveals why our method is more suitable for class-incremental learning. To facilitate parameter fusion with closer distance in the parameter space, we use distillation to enhance the optimization process. Furthermore, we conduct experiments on two widely used datasets, achieving the state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiao_Endpoints_Weight_Fusion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
GeneCIS: A Benchmark for General Conditional Image Similarity | Sagar Vaze, Nicolas Carion, Ishan Misra | We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning methods, supervised or self-supervised, which learn a fixed embedding function and hence implicitly assume a single notion of similarity. For instance, models trained on ImageNet are biased towards object categories, while a user might prefer the model to focus on colors, textures or specific elements in the scene. In this paper, we propose the GeneCIS ('genesis') benchmark, which measures models' ability to adapt to a range of similarity conditions. Extending prior work, our benchmark is designed for zero-shot evaluation only, and hence considers an open-set of similarity conditions. We find that baselines from powerful CLIP models struggle on GeneCIS and that performance on the benchmark is only weakly correlated with ImageNet accuracy, suggesting that simply scaling existing methods is not fruitful. We further propose a simple, scalable solution based on automatically mining information from existing image-caption datasets. We find our method offers a substantial boost over the baselines on GeneCIS, and further improves zero-shot performance on related image retrieval benchmarks. In fact, though evaluated zero-shot, our model surpasses state-of-the-art supervised models on MIT-States. | https://openaccess.thecvf.com/content/CVPR2023/papers/Vaze_GeneCIS_A_Benchmark_for_General_Conditional_Image_Similarity_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vaze_GeneCIS_A_Benchmark_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vaze_GeneCIS_A_Benchmark_for_General_Conditional_Image_Similarity_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vaze_GeneCIS_A_Benchmark_for_General_Conditional_Image_Similarity_CVPR_2023_paper.html | CVPR 2023 | null |
MetaViewer: Towards a Unified Multi-View Representation | Ren Wang, Haoliang Sun, Yuling Ma, Xiaoming Xi, Yilong Yin | Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specified fusion functions and aligning criteria could potentially degrade the quality of the derived representation. To overcome them, we propose a novel uniform-to-specific multi-view learning framework from a meta-learning perspective, where the unified representation no longer involves manual manipulation but is automatically derived from a meta-learner named MetaViewer. Specifically, we formulated the extraction and fusion of view-specific latent features as a nested optimization problem and solved it by using a bi-level optimization scheme. In this way, MetaViewer automatically fuses view-specific features into a unified one and learns the optimal fusion scheme by observing reconstruction processes from the unified to the specific over all views. Extensive experimental results in downstream classification and clustering tasks demonstrate the efficiency and effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MetaViewer_Towards_a_Unified_Multi-View_Representation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.06329 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MetaViewer_Towards_a_Unified_Multi-View_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MetaViewer_Towards_a_Unified_Multi-View_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos | Zicheng Zhang, Wei Wu, Wei Sun, Danyang Tu, Wei Lu, Xiongkuo Min, Ying Chen, Guangtao Zhai | User-generated content (UGC) live videos are often bothered by various distortions during capture procedures and thus exhibit diverse visual qualities. Such source videos are further compressed and transcoded by media server providers before being distributed to end-users. Because of the flourishing of UGC live videos, effective video quality assessment (VQA) tools are needed to monitor and perceptually optimize live streaming videos in the distributing process. Unfortunately, existing compressed UGC VQA databases are either small in scale or employ high-quality UGC videos as source videos, so VQA models developed on these databases have limited abilities to evaluate UGC live videos. In this paper, we address UGC Live VQA problems by constructing a first-of-a-kind subjective UGC Live VQA database and developing an effective evaluation tool. Concretely, 418 source UGC videos are collected in real live streaming scenarios and 3,762 compressed ones at different bit rates are generated for the subsequent subjective VQA experiments. Based on the built database, we develop a Multi-Dimensional VQA (MD-VQA) evaluator to measure the visual quality of UGC live videos from semantic, distortion, and motion aspects respectively. Extensive experimental results show that MD-VQA achieves state-of-the-art performance on both our UGC Live VQA database and existing compressed UGC VQA databases. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_MD-VQA_Multi-Dimensional_Quality_Assessment_for_UGC_Live_Videos_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MD-VQA_Multi-Dimensional_Quality_Assessment_for_UGC_Live_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MD-VQA_Multi-Dimensional_Quality_Assessment_for_UGC_Live_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Vision Transformers Are Good Mask Auto-Labelers | Shiyi Lan, Xitong Yang, Zhiding Yu, Zuxuan Wu, Jose M. Alvarez, Anima Anandkumar | We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels.We show that Vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lan_Vision_Transformers_Are_Good_Mask_Auto-Labelers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lan_Vision_Transformers_Are_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.03992 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lan_Vision_Transformers_Are_Good_Mask_Auto-Labelers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lan_Vision_Transformers_Are_Good_Mask_Auto-Labelers_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Transformation Fields for Arbitrary-Styled Font Generation | Bin Fu, Junjun He, Jianjun Wang, Yu Qiao | Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-content disentanglement paradigm, which transfers the target font styles to characters by combining the content representations of source characters and the style codes of reference samples. Most existing methods attempt to increase font generation ability via exploring powerful style representations, which may be a sub-optimal solution for the FFG task due to the lack of modeling spatial transformation in transferring font styles. In this paper, we model font generation as a continuous transformation process from the source character image to the target font image via the creation and dissipation of font pixels, and embed the corresponding transformations into a neural transformation field. With the estimated transformation path, the neural transformation field generates a set of intermediate transformation results via the sampling process, and a font rendering formula is developed to accumulate them into the target font image. Extensive experiments show that our method achieves state-of-the-art performance on few-shot font generation task, which demonstrates the effectiveness of our proposed model. Our implementation is available at: https://github.com/fubinfb/NTF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Neural_Transformation_Fields_for_Arbitrary-Styled_Font_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fu_Neural_Transformation_Fields_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Neural_Transformation_Fields_for_Arbitrary-Styled_Font_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Neural_Transformation_Fields_for_Arbitrary-Styled_Font_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo | Lukas Mehl, Jenny Schmalfuss, Azin Jahedi, Yaroslava Nalivayko, Andrés Bruhn | While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation methodology. Hence, we introduce Spring -- a large, high-resolution, high-detail, computer-generated benchmark for scene flow, optical flow, and stereo. Based on rendered scenes from the open-source Blender movie "Spring", it provides photo-realistic HD datasets with state-of-the-art visual effects and ground truth training data. Furthermore, we provide a website to upload, analyze and compare results. Using a novel evaluation methodology based on a super-resolved UHD ground truth, our Spring benchmark can assess the quality of fine structures and provides further detailed performance statistics on different image regions. Regarding the number of ground truth frames, Spring is 60x larger than the only scene flow benchmark, KITTI 2015, and 15x larger than the well-established MPI Sintel optical flow benchmark. Initial results for recent methods on our benchmark show that estimating fine details is indeed challenging, as their accuracy leaves significant room for improvement. The Spring benchmark and the corresponding datasets are available at http://spring-benchmark.org. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mehl_Spring_A_High-Resolution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01943 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html | CVPR 2023 | null |
EDICT: Exact Diffusion Inversion via Coupled Transformations | Bram Wallace, Akash Gokul, Nikhil Naik | Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs), with applications for real image editing. The standard approach for real image editing with inversion uses denoising diffusion implicit models (DDIMs) to deterministically noise the image to the intermediate state along the path that the denoising would follow given the original conditioning. However, DDIM inversion for real images is unstable as it relies on local linearization assumptions, which result in the propagation of errors, leading to incorrect image reconstruction and loss of content. To alleviate these problems, we propose Exact Diffusion Inversion via Coupled Transformations (EDICT), an inversion method that draws inspiration from affine coupling layers. EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors which are used to invert each other in an alternating fashion. Using Stable Diffusion [25], a state-of-the-art latent diffusion model, we demonstrate that EDICT successfully reconstructs real images with high fidelity. On complex image datasets like MS-COCO, EDICT reconstruction significantly outperforms DDIM, improving the mean square error of reconstruction by a factor of two. Using noise vectors inverted from real images, EDICT enables a wide range of image edits--from local and global semantic edits to image stylization--while maintaining fidelity to the original image structure. EDICT requires no model training/finetuning, prompt tuning, or extra data and can be combined with any pretrained DDM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wallace_EDICT_Exact_Diffusion_Inversion_via_Coupled_Transformations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wallace_EDICT_Exact_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12446 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wallace_EDICT_Exact_Diffusion_Inversion_via_Coupled_Transformations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wallace_EDICT_Exact_Diffusion_Inversion_via_Coupled_Transformations_CVPR_2023_paper.html | CVPR 2023 | null |
Natural Language-Assisted Sign Language Recognition | Ronglai Zuo, Fangyun Wei, Brian Mak | Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth. Due to the inherent restriction of combinations of these visual ingredients, there exist a significant number of visually indistinguishable signs (VISigns) in sign languages, which limits the recognition capacity of vision neural networks. To mitigate the problem, we propose the Natural Language-Assisted Sign Language Recognition (NLA-SLR) framework, which exploits semantic information contained in glosses (sign labels). First, for VISigns with similar semantic meanings, we propose language-aware label smoothing by generating soft labels for each training sign whose smoothing weights are computed from the normalized semantic similarities among the glosses to ease training. Second, for VISigns with distinct semantic meanings, we present an inter-modality mixup technique which blends vision and gloss features to further maximize the separability of different signs under the supervision of blended labels. Besides, we also introduce a novel backbone, video-keypoint network, which not only models both RGB videos and human body keypoints but also derives knowledge from sign videos of different temporal receptive fields. Empirically, our method achieves state-of-the-art performance on three widely-adopted benchmarks: MSASL, WLASL, and NMFs-CSL. Codes are available at https://github.com/FangyunWei/SLRT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zuo_Natural_Language-Assisted_Sign_Language_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zuo_Natural_Language-Assisted_Sign_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zuo_Natural_Language-Assisted_Sign_Language_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zuo_Natural_Language-Assisted_Sign_Language_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
MAESTER: Masked Autoencoder Guided Segmentation at Pixel Resolution for Accurate, Self-Supervised Subcellular Structure Recognition | Ronald Xie, Kuan Pang, Gary D. Bader, Bo Wang | Accurate segmentation of cellular images remains an elusive task due to the intrinsic variability in morphology of biological structures. Complete manual segmentation is unfeasible for large datasets, and while supervised methods have been proposed to automate segmentation, they often rely on manually generated ground truths which are especially challenging and time consuming to generate in biology due to the requirement of domain expertise. Furthermore, these methods have limited generalization capacity, requiring additional manual labels to be generated for each dataset and use case. We introduce MAESTER (Masked AutoEncoder guided SegmenTation at pixEl Resolution), a self-supervised method for accurate, subcellular structure segmentation at pixel resolution. MAESTER treats segmentation as a representation learning and clustering problem. Specifically, MAESTER learns semantically meaningful token representations of multi-pixel image patches while simultaneously maintaining a sufficiently large field of view for contextual learning. We also develop a cover-and-stride inference strategy to achieve pixel-level subcellular structure segmentation. We evaluated MAESTER on a publicly available volumetric electron microscopy (VEM) dataset of primary mouse pancreatic islets beta cells and achieved upwards of 29.1% improvement over state-of-the-art under the same evaluation criteria. Furthermore, our results are competitive against supervised methods trained on the same tasks, closing the gap between self-supervised and supervised approaches. MAESTER shows promise for alleviating the critical bottleneck of ground truth generation for imaging related data analysis and thereby greatly increasing the rate of biological discovery. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_MAESTER_Masked_Autoencoder_Guided_Segmentation_at_Pixel_Resolution_for_Accurate_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_MAESTER_Masked_Autoencoder_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_MAESTER_Masked_Autoencoder_Guided_Segmentation_at_Pixel_Resolution_for_Accurate_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_MAESTER_Masked_Autoencoder_Guided_Segmentation_at_Pixel_Resolution_for_Accurate_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Semantic Relationship Among Instances for Image-Text Matching | Zheren Fu, Zhendong Mao, Yan Song, Yongdong Zhang | Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities. However, previous studies only focus on capturing fragment-level relation within a sample from a particular modality, e.g., salient regions in an image or text words in a sentence, where they usually pay less attention to capturing instance-level interactions among samples and modalities, e.g., multiple images and texts. In this paper, we argue that sample relations could help learn subtle differences for hard negative instances, and thus transfer shared knowledge for infrequent samples should be promising in obtaining better holistic embeddings. Therefore, we propose a novel hierarchical relation modeling framework (HREM), which explicitly capture both fragment- and instance-level relations to learn discriminative and robust cross-modal embeddings. Extensive experiments on Flickr30K and MS-COCO show our proposed method outperforms the state-of-the-art ones by 4%-10% in terms of rSum. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Learning_Semantic_Relationship_Among_Instances_for_Image-Text_Matching_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Learning_Semantic_Relationship_Among_Instances_for_Image-Text_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Learning_Semantic_Relationship_Among_Instances_for_Image-Text_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
AeDet: Azimuth-Invariant Multi-View 3D Object Detection | Chengjian Feng, Zequn Jie, Yujie Zhong, Xiangxiang Chu, Lin Ma | Recent LSS-based multi-view 3D object detection has made tremendous progress, by processing the features in Brid-Eye-View (BEV) via the convolutional detector. However, the typical convolution ignores the radial symmetry of the BEV features and increases the difficulty of the detector optimization. To preserve the inherent property of the BEV features and ease the optimization, we propose an azimuth-equivariant convolution (AeConv) and an azimuth-equivariant anchor. The sampling grid of AeConv is always in the radial direction, thus it can learn azimuth-invariant BEV features. The proposed anchor enables the detection head to learn predicting azimuth-irrelevant targets. In addition, we introduce a camera-decoupled virtual depth to unify the depth prediction for the images with different camera intrinsic parameters. The resultant detector is dubbed Azimuth-equivariant Detector (AeDet). Extensive experiments are conducted on nuScenes, and AeDet achieves a 62.0% NDS, surpassing the recent multi-view 3D object detectors such as PETRv2 and BEVDepth by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_AeDet_Azimuth-Invariant_Multi-View_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_AeDet_Azimuth-Invariant_Multi-View_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12501 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_AeDet_Azimuth-Invariant_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_AeDet_Azimuth-Invariant_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology | Jeongun Ryu, Aaron Valero Puche, JaeWoong Shin, Seonwook Park, Biagio Brattoli, Jinhee Lee, Wonkyung Jung, Soo Ick Cho, Kyunghyun Paeng, Chan-Young Ock, Donggeun Yoo, Sérgio Pereira | Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicly release OCELOT, a dataset purposely dedicated to the study of cell-tissue relationships for cell detection in histopathology. OCELOT provides overlapping cell and tissue annotations on images acquired from multiple organs. Within this setting, we also propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. When compared against a model trained only for the cell detection task, our proposed approaches improve cell detection performance on 3 datasets: proposed OCELOT, public TIGER, and internal CARP datasets. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score. We believe the contributions of this paper, including the release of the OCELOT dataset at https://lunit-io.github.io/research/publications/ocelot are a crucial starting point toward the important research direction of incorporating cell-tissue relationships in computation pathology. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ryu_OCELOT_Overlapped_Cell_on_Tissue_Dataset_for_Histopathology_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ryu_OCELOT_Overlapped_Cell_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13110 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ryu_OCELOT_Overlapped_Cell_on_Tissue_Dataset_for_Histopathology_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ryu_OCELOT_Overlapped_Cell_on_Tissue_Dataset_for_Histopathology_CVPR_2023_paper.html | CVPR 2023 | null |
Global-to-Local Modeling for Video-Based 3D Human Pose and Shape Estimation | Xiaolong Shen, Zongxin Yang, Xiaohan Wang, Jianxin Ma, Chang Zhou, Yi Yang | Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. Although these two metrics are responsible for different ranges of temporal consistency, existing state-of-the-art methods treat them as a unified problem and use monotonous modeling structures (e.g., RNN or attention-based block) to design their networks. However, using a single kind of modeling structure is difficult to balance the learning of short-term and long-term temporal correlations, and may bias the network to one of them, leading to undesirable predictions like global location shift, temporal inconsistency, and insufficient local details. To solve these problems, we propose to structurally decouple the modeling of long-term and short-term correlations in an end-to-end framework, Global-to-Local Transformer (GLoT). First, a global transformer is introduced with a Masked Pose and Shape Estimation strategy for long-term modeling. The strategy stimulates the global transformer to learn more inter-frame correlations by randomly masking the features of several frames. Second, a local transformer is responsible for exploiting local details on the human mesh and interacting with the global transformer by leveraging cross-attention. Moreover, a Hierarchical Spatial Correlation Regressor is further introduced to refine intra-frame estimations by decoupled global-local representation and implicit kinematic constraints. Our GLoT surpasses previous state-of-the-art methods with the lowest model parameters on popular benchmarks, i.e., 3DPW, MPI-INF-3DHP, and Human3.6M. Codes are available at https://github.com/sxl142/GLoT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_Global-to-Local_Modeling_for_Video-Based_3D_Human_Pose_and_Shape_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_Global-to-Local_Modeling_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14747 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Global-to-Local_Modeling_for_Video-Based_3D_Human_Pose_and_Shape_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Global-to-Local_Modeling_for_Video-Based_3D_Human_Pose_and_Shape_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion | Michael J. Black, Priyanka Patel, Joachim Tesch, Jinlong Yang | We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-trivial and we show how to do this for full bodies in motion. Specifically, our BEDLAM dataset contains monocular RGB videos with ground-truth 3D bodies in SMPL-X format. It includes a diversity of body shapes, motions, skin tones, hair, and clothing. The clothing is realistically simulated on the moving bodies using commercial clothing physics simulation. We render varying numbers of people in realistic scenes with varied lighting and camera motions. We then train various HPS regressors using BEDLAM and achieve state-of-the-art accuracy on real-image benchmarks despite training with synthetic data. We use BEDLAM to gain insights into what model design choices are important for accuracy. With good synthetic training data, we find that a basic method like HMR approaches the accuracy of the current SOTA method (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes. Additionally, we provide detailed information about our synthetic data generation pipeline, enabling others to generate their own datasets. See the project page: https://bedlam.is.tue.mpg.de/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Black_BEDLAM_A_Synthetic_Dataset_of_Bodies_Exhibiting_Detailed_Lifelike_Animated_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Black_BEDLAM_A_Synthetic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Black_BEDLAM_A_Synthetic_Dataset_of_Bodies_Exhibiting_Detailed_Lifelike_Animated_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Black_BEDLAM_A_Synthetic_Dataset_of_Bodies_Exhibiting_Detailed_Lifelike_Animated_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss | Anas Mahmoud, Jordan S. K. Hu, Tianshu Kuai, Ali Harakeh, Liam Paull, Steven L. Waslander | An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to-point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes. We propose to alleviate the self-similarity problem through a novel semantically tolerant image-to-point contrastive loss that takes into consideration the semantic distance between positive and negative image regions to minimize contrasting semantically similar point and image regions. Additionally, we address class imbalance by designing a class-agnostic balanced loss that approximates the degree of class imbalance through an aggregate sample-to-samples semantic similarity measure. We demonstrate that our semantically-tolerant contrastive loss with class balancing improves state-of-the-art 2D-to-3D representation learning in all evaluation settings on 3D semantic segmentation. Our method consistently outperforms state-of-the-art 2D-to-3D representation learning frameworks across a wide range of 2D self-supervised pretrained models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mahmoud_Self-Supervised_Image-to-Point_Distillation_via_Semantically_Tolerant_Contrastive_Loss_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mahmoud_Self-Supervised_Image-to-Point_Distillation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05709 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mahmoud_Self-Supervised_Image-to-Point_Distillation_via_Semantically_Tolerant_Contrastive_Loss_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mahmoud_Self-Supervised_Image-to-Point_Distillation_via_Semantically_Tolerant_Contrastive_Loss_CVPR_2023_paper.html | CVPR 2023 | null |
ProtoCon: Pseudo-Label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-Supervised Learning | Islam Nassar, Munawar Hayat, Ehsan Abbasnejad, Hamid Rezatofighi, Gholamreza Haffari | Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as additional targets to train the model. We propose ProtoCon, a novel SSL method aimed at the less-explored label-scarce SSL where such methods usually underperform. ProtoCon refines the pseudo-labels by leveraging their nearest neighbours' information. The neighbours are identified as the training proceeds using an online clustering approach operating in an embedding space trained via a prototypical loss to encourage well-formed clusters. The online nature of ProtoCon allows it to utilise the label history of the entire dataset in one training cycle to refine labels in the following cycle without the need to store image embeddings. Hence, it can seamlessly scale to larger datasets at a low cost. Finally, ProtoCon addresses the poor training signal in the initial phase of training (due to fewer confident predictions) by introducing an auxiliary self-supervised loss. It delivers significant gains and faster convergence over state-of-the-art across 5 datasets, including CIFARs, ImageNet and DomainNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nassar_ProtoCon_Pseudo-Label_Refinement_via_Online_Clustering_and_Prototypical_Consistency_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nassar_ProtoCon_Pseudo-Label_Refinement_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nassar_ProtoCon_Pseudo-Label_Refinement_via_Online_Clustering_and_Prototypical_Consistency_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nassar_ProtoCon_Pseudo-Label_Refinement_via_Online_Clustering_and_Prototypical_Consistency_for_CVPR_2023_paper.html | CVPR 2023 | null |
Image Super-Resolution Using T-Tetromino Pixels | Simon Grosche, Andy Regensky, Jürgen Seiler, André Kaup | For modern high-resolution imaging sensors, pixel binning is performed in low-lighting conditions and in case high frame rates are required. To recover the original spatial resolution, single-image super-resolution techniques can be applied for upscaling. To achieve a higher image quality after upscaling, we propose a novel binning concept using tetromino-shaped pixels. It is embedded into the field of compressed sensing and the coherence is calculated to motivate the sensor layouts used. Next, we investigate the reconstruction quality using tetromino pixels for the first time in literature. Instead of using different types of tetrominoes as proposed elsewhere, we show that using a small repeating cell consisting of only four T-tetrominoes is sufficient. For reconstruction, we use a locally fully connected reconstruction (LFCR) network as well as two classical reconstruction methods from the field of compressed sensing. Using the LFCR network in combination with the proposed tetromino layout, we achieve superior image quality in terms of PSNR, SSIM, and visually compared to conventional single-image super-resolution using the very deep super-resolution (VDSR) network. For PSNR, a gain of up to +1.92 dB is achieved. | https://openaccess.thecvf.com/content/CVPR2023/papers/Grosche_Image_Super-Resolution_Using_T-Tetromino_Pixels_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Grosche_Image_Super-Resolution_Using_T-Tetromino_Pixels_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Grosche_Image_Super-Resolution_Using_T-Tetromino_Pixels_CVPR_2023_paper.html | CVPR 2023 | null |
GFIE: A Dataset and Baseline for Gaze-Following From 2D to 3D in Indoor Environments | Zhengxi Hu, Yuxue Yang, Xiaolin Zhai, Dingye Yang, Bohan Zhou, Jingtai Liu | Gaze-following is a kind of research that requires locating where the person in the scene is looking automatically under the topic of gaze estimation. It is an important clue for understanding human intention, such as identifying objects or regions of interest to humans. However, a survey of datasets used for gaze-following tasks reveals defects in the way they collect gaze point labels. Manual labeling may introduce subjective bias and is labor-intensive, while automatic labeling with an eye-tracking device would alter the person's appearance. In this work, we introduce GFIE, a novel dataset recorded by a gaze data collection system we developed. The system is constructed with two devices, an Azure Kinect and a laser rangefinder, which generate the laser spot to steer the subject's attention as they perform in front of the camera. And an algorithm is developed to locate laser spots in images for annotating 2D/3D gaze targets and removing ground truth introduced by the spots. The whole procedure of collecting gaze behavior allows us to obtain unbiased labels in unconstrained environments semi-automatically. We also propose a baseline method with stereo field-of-view (FoV) perception for establishing a 2D/3D gaze-following benchmark on the GFIE dataset. Project page: https://sites.google.com/view/gfie. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_GFIE_A_Dataset_and_Baseline_for_Gaze-Following_From_2D_to_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_GFIE_A_Dataset_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_GFIE_A_Dataset_and_Baseline_for_Gaze-Following_From_2D_to_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_GFIE_A_Dataset_and_Baseline_for_Gaze-Following_From_2D_to_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition | Shun Fang, Zhengqin Xu, Shiqian Wu, Shoulie Xie | Robust principal component analysis (RPCA) is widely studied in computer vision. Recently an adaptive rank estimate based RPCA has achieved top performance in low-level vision tasks without the prior rank, but both the rank estimate and RPCA optimization algorithm involve singular value decomposition, which requires extremely huge computational resource for large-scale matrices. To address these issues, an efficient RPCA (eRPCA) algorithm is proposed based on block Krylov iteration and CUR decomposition in this paper. Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires O(ndrq + n(rq)^2) for an (nxd) input matrix, in which q is a parameter with a small value, r is the target rank. Based on the estimated rank, CUR decomposition is adopted to replace SVD in updating low-rank matrix component, whose complexity reduces from O(rnd) to O(r^2n) per iteration. Experimental results verify the efficiency and effectiveness of the proposed eRPCA over the state-of-the-art methods in various low-level vision applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fang_Efficient_Robust_Principal_Component_Analysis_via_Block_Krylov_Iteration_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fang_Efficient_Robust_Principal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Efficient_Robust_Principal_Component_Analysis_via_Block_Krylov_Iteration_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Efficient_Robust_Principal_Component_Analysis_via_Block_Krylov_Iteration_and_CVPR_2023_paper.html | CVPR 2023 | null |
VIVE3D: Viewpoint-Independent Video Editing Using 3D-Aware GANs | Anna Frühstück, Nikolaos Sarafianos, Yuanlu Xu, Peter Wonka, Tony Tung | We introduce VIVE3D, a novel approach that extends the capabilities of image-based 3D GANs to video editing and is able to represent the input video in an identity-preserving and temporally consistent way. We propose two new building blocks. First, we introduce a novel GAN inversion technique specifically tailored to 3D GANs by jointly embedding multiple frames and optimizing for the camera parameters. Second, besides traditional semantic face edits (e.g. for age and expression), we are the first to demonstrate edits that show novel views of the head enabled by the inherent properties of 3D GANs and our optical flow-guided compositing technique to combine the head with the background video. Our experiments demonstrate that VIVE3D generates high-fidelity face edits at consistent quality from a range of camera viewpoints which are composited with the original video in a temporally and spatially-consistent manner. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fruhstuck_VIVE3D_Viewpoint-Independent_Video_Editing_Using_3D-Aware_GANs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fruhstuck_VIVE3D_Viewpoint-Independent_Video_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fruhstuck_VIVE3D_Viewpoint-Independent_Video_Editing_Using_3D-Aware_GANs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fruhstuck_VIVE3D_Viewpoint-Independent_Video_Editing_Using_3D-Aware_GANs_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction | Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang | The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of the predicted distribution. To promote the sampling process of stochastic prediction, we propose a novel method, called BOsampler, to adaptively mine potential paths with Bayesian optimization in an unsupervised manner, as a sequential design strategy in which new prediction is dependent on the previously drawn samples. Specifically, we model the trajectory sampling as a Gaussian process and construct an acquisition function to measure the potential sampling value. This acquisition function applies the original distribution as prior and encourages exploring paths in the long-tail region. This sampling method can be integrated with existing stochastic predictive models without retraining. Experimental results on various baseline methods demonstrate the effectiveness of our method. The source code is released in this link. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Unsupervised_Sampling_Promoting_for_Stochastic_Human_Trajectory_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Unsupervised_Sampling_Promoting_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.04298 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Unsupervised_Sampling_Promoting_for_Stochastic_Human_Trajectory_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Unsupervised_Sampling_Promoting_for_Stochastic_Human_Trajectory_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
BKinD-3D: Self-Supervised 3D Keypoint Discovery From Multi-View Videos | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_BKinD-3D_Self-Supervised_3D_Keypoint_Discovery_From_Multi-View_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_BKinD-3D_Self-Supervised_3D_Keypoint_Discovery_From_Multi-View_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
StyleRF: Zero-Shot 3D Style Transfer of Neural Radiance Fields | Kunhao Liu, Fangneng Zhan, Yiwen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El Saddik, Shijian Lu, Eric P. Xing | 3D style transfer aims to render stylized novel views of a 3D scene with multi-view consistency. However, most existing work suffers from a three-way dilemma over accurate geometry reconstruction, high-quality stylization, and being generalizable to arbitrary new styles. We propose StyleRF (Style Radiance Fields), an innovative 3D style transfer technique that resolves the three-way dilemma by performing style transformation within the feature space of a radiance field. StyleRF employs an explicit grid of high-level features to represent 3D scenes, with which high-fidelity geometry can be reliably restored via volume rendering. In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer. StyleRF consists of two innovative designs. The first is sampling-invariant content transformation that makes the transformation invariant to the holistic statistics of the sampled 3D points and accordingly ensures multi-view consistency. The second is deferred style transformation of 2D feature maps which is equivalent to the transformation of 3D points but greatly reduces memory footprint without degrading multi-view consistency. Extensive experiments show that StyleRF achieves superior 3D stylization quality with precise geometry reconstruction and it can generalize to various new styles in a zero-shot manner. Project website: https://kunhao-liu.github.io/StyleRF/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_StyleRF_Zero-Shot_3D_Style_Transfer_of_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_StyleRF_Zero-Shot_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10598 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_StyleRF_Zero-Shot_3D_Style_Transfer_of_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_StyleRF_Zero-Shot_3D_Style_Transfer_of_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Accidental Light Probes | Hong-Xing Yu, Samir Agarwala, Charles Herrmann, Richard Szeliski, Noah Snavely, Jiajun Wu, Deqing Sun | Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes are generally unavailable in everyday images. In this work, we study recovering lighting from accidental light probes (ALPs)---common, shiny objects like Coke cans, which often accidentally appear in daily scenes. We propose a physically-based approach to model ALPs and estimate lighting from their appearances in single images. The main idea is to model the appearance of ALPs by photogrammetrically principled shading and to invert this process via differentiable rendering to recover incidental illumination. We demonstrate that we can put an ALP into a scene to allow high-fidelity lighting estimation. Our model can also recover lighting for existing images that happen to contain an ALP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Accidental_Light_Probes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Accidental_Light_Probes_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Accidental_Light_Probes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Accidental_Light_Probes_CVPR_2023_paper.html | CVPR 2023 | null |
Iterative Vision-and-Language Navigation | Jacob Krantz, Shurjo Banerjee, Wang Zhu, Jason Corso, Peter Anderson, Stefan Lee, Jesse Thomason | We present Iterative Vision-and-Language Navigation (IVLN), a paradigm for evaluating language-guided agents navigating in a persistent environment over time. Existing Vision-and-Language Navigation (VLN) benchmarks erase the agent's memory at the beginning of every episode, testing the ability to perform cold-start navigation with no prior information. However, deployed robots occupy the same environment for long periods of time. The IVLN paradigm addresses this disparity by training and evaluating VLN agents that maintain memory across tours of scenes that consist of up to 100 ordered instruction-following Room-to-Room (R2R) episodes, each defined by an individual language instruction and a target path. We present discrete and continuous Iterative Room-to-Room (IR2R) benchmarks comprising about 400 tours each in 80 indoor scenes. We find that extending the implicit memory of high-performing transformer VLN agents is not sufficient for IVLN, but agents that build maps can benefit from environment persistence, motivating a renewed focus on map-building agents in VLN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Krantz_Iterative_Vision-and-Language_Navigation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Krantz_Iterative_Vision-and-Language_Navigation_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2210.03087 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Krantz_Iterative_Vision-and-Language_Navigation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Krantz_Iterative_Vision-and-Language_Navigation_CVPR_2023_paper.html | CVPR 2023 | null |
DPE: Disentanglement of Pose and Expression for General Video Portrait Editing | Youxin Pang, Yong Zhang, Weize Quan, Yanbo Fan, Xiaodong Cun, Ying Shan, Dong-Ming Yan | One-shot video-driven talking face generation aims at producing a synthetic talking video by transferring the facial motion from a video to an arbitrary portrait image. Head pose and facial expression are always entangled in facial motion and transferred simultaneously. However, the entanglement sets up a barrier for these methods to be used in video portrait editing directly, where it may require to modify the expression only while maintaining the pose unchanged. One challenge of decoupling pose and expression is the lack of paired data, such as the same pose but different expressions. Only a few methods attempt to tackle this challenge with the feat of 3D Morphable Models (3DMMs) for explicit disentanglement. But 3DMMs are not accurate enough to capture facial details due to the limited number of Blendshapes, which has side effects on motion transfer. In this paper, we introduce a novel self-supervised disentanglement framework to decouple pose and expression without 3DMMs and paired data, which consists of a motion editing module, a pose generator, and an expression generator. The editing module projects faces into a latent space where pose motion and expression motion can be disentangled, and the pose or expression transfer can be performed in the latent space conveniently via addition. The two generators render the modified latent codes to images, respectively. Moreover, to guarantee the disentanglement, we propose a bidirectional cyclic training strategy with well-designed constraints. Evaluations demonstrate our method can control pose or expression independently and be used for general video editing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pang_DPE_Disentanglement_of_Pose_and_Expression_for_General_Video_Portrait_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pang_DPE_Disentanglement_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.06281 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_DPE_Disentanglement_of_Pose_and_Expression_for_General_Video_Portrait_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_DPE_Disentanglement_of_Pose_and_Expression_for_General_Video_Portrait_CVPR_2023_paper.html | CVPR 2023 | null |
Adversarial Counterfactual Visual Explanations | Guillaume Jeanneret, Loïc Simon, Frédéric Jurie | Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks cannot be used directly in a counterfactual explanation perspective, as such perturbations are perceived as noise and not as actionable and understandable image modifications. Building on the robust learning literature, this paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain. The proposed approach hypothesizes that Denoising Diffusion Probabilistic Models are excellent regularizers for avoiding high-frequency and out-of-distribution perturbations when generating adversarial attacks. The paper's key idea is to build attacks through a diffusion model to polish them. This allows studying the target model regardless of its robustification level. Extensive experimentation shows the advantages of our counterfactual explanation approach over current State-of-the-Art in multiple testbeds. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jeanneret_Adversarial_Counterfactual_Visual_Explanations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jeanneret_Adversarial_Counterfactual_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09962 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jeanneret_Adversarial_Counterfactual_Visual_Explanations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jeanneret_Adversarial_Counterfactual_Visual_Explanations_CVPR_2023_paper.html | CVPR 2023 | null |
MaLP: Manipulation Localization Using a Proactive Scheme | Vishal Asnani, Xi Yin, Tal Hassner, Xiaoming Liu | Advancements in the generation quality of various Generative Models (GMs) has made it necessary to not only perform binary manipulation detection but also localize the modified pixels in an image. However, prior works termed as passive for manipulation localization exhibit poor generalization performance over unseen GMs and attribute modifications. To combat this issue, we propose a proactive scheme for manipulation localization, termed MaLP. We encrypt the real images by adding a learned template. If the image is manipulated by any GM, this added protection from the template not only aids binary detection but also helps in identifying the pixels modified by the GM. The template is learned by leveraging local and global-level features estimated by a two-branch architecture. We show that MaLP performs better than prior passive works. We also show the generalizability of MaLP by testing on 22 different GMs, providing a benchmark for future research on manipulation localization. Finally, we show that MaLP can be used as a discriminator for improving the generation quality of GMs. Our models/codes are available at www.github.com/vishal3477/pro_loc. | https://openaccess.thecvf.com/content/CVPR2023/papers/Asnani_MaLP_Manipulation_Localization_Using_a_Proactive_Scheme_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Asnani_MaLP_Manipulation_Localization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16976 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Asnani_MaLP_Manipulation_Localization_Using_a_Proactive_Scheme_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Asnani_MaLP_Manipulation_Localization_Using_a_Proactive_Scheme_CVPR_2023_paper.html | CVPR 2023 | null |
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models | Patrick Schramowski, Manuel Brack, Björn Deiseroth, Kristian Kersting | Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed - inappropriate image prompts (I2P) - containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment. | https://openaccess.thecvf.com/content/CVPR2023/papers/Schramowski_Safe_Latent_Diffusion_Mitigating_Inappropriate_Degeneration_in_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Schramowski_Safe_Latent_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.05105 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Schramowski_Safe_Latent_Diffusion_Mitigating_Inappropriate_Degeneration_in_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Schramowski_Safe_Latent_Diffusion_Mitigating_Inappropriate_Degeneration_in_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation | Ludan Ruan, Yiyang Ma, Huan Yang, Huiguo He, Bei Liu, Jianlong Fu, Nicholas Jing Yuan, Qin Jin, Baining Guo | We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ruan_MM-Diffusion_Learning_Multi-Modal_Diffusion_Models_for_Joint_Audio_and_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ruan_MM-Diffusion_Learning_Multi-Modal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ruan_MM-Diffusion_Learning_Multi-Modal_Diffusion_Models_for_Joint_Audio_and_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ruan_MM-Diffusion_Learning_Multi-Modal_Diffusion_Models_for_Joint_Audio_and_Video_CVPR_2023_paper.html | CVPR 2023 | null |
HexPlane: A Fast Representation for Dynamic Scenes | Ang Cao, Justin Johnson | Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3D scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than 100x. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlane is a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_HexPlane_A_Fast_Representation_for_Dynamic_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_HexPlane_A_Fast_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.09632 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_HexPlane_A_Fast_Representation_for_Dynamic_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_HexPlane_A_Fast_Representation_for_Dynamic_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data | Yuhao Chen, Xin Tan, Borui Zhao, Zhaowei Chen, Renjie Song, Jiajun Liang, Xuequan Lu | Semi-supervised learning (SSL) has attracted enormous attention due to its vast potential of mitigating the dependence on large labeled datasets. The latest methods (e.g., FixMatch) use a combination of consistency regularization and pseudo-labeling to achieve remarkable successes. However, these methods all suffer from the waste of complicated examples since all pseudo-labels have to be selected by a high threshold to filter out noisy ones. Hence, the examples with ambiguous predictions will not contribute to the training phase. For better leveraging all unlabeled examples, we propose two novel techniques: Entropy Meaning Loss (EML) and Adaptive Negative Learning (ANL). EML incorporates the prediction distribution of non-target classes into the optimization objective to avoid competition with target class, and thus generating more high-confidence predictions for selecting pseudo-label. ANL introduces the additional negative pseudo-label for all unlabeled data to leverage low-confidence examples. It adaptively allocates this label by dynamically evaluating the top-k performance of the model. EML and ANL do not introduce any additional parameter and hyperparameter. We integrate these techniques with FixMatch, and develop a simple yet powerful framework called FullMatch. Extensive experiments on several common SSL benchmarks (CIFAR-10/100, SVHN, STL-10 and ImageNet) demonstrate that FullMatch exceeds FixMatch by a large margin. Integrated with FlexMatch (an advanced FixMatch-based framework), we achieve state-of-the-art performance. Source code is available at https://github.com/megvii-research/FullMatch. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Boosting_Semi-Supervised_Learning_by_Exploiting_All_Unlabeled_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Boosting_Semi-Supervised_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11066 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Boosting_Semi-Supervised_Learning_by_Exploiting_All_Unlabeled_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Boosting_Semi-Supervised_Learning_by_Exploiting_All_Unlabeled_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Novel-View Acoustic Synthesis | Changan Chen, Alexander Richard, Roman Shapovalov, Vamsi Krishna Ithapu, Natalia Neverova, Kristen Grauman, Andrea Vedaldi | We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint? We propose a neural rendering approach: Visually-Guided Acoustic Synthesis (ViGAS) network that learns to synthesize the sound of an arbitrary point in space by analyzing the input audio-visual cues. To benchmark this task, we collect two first-of-their-kind large-scale multi-view audio-visual datasets, one synthetic and one real. We show that our model successfully reasons about the spatial cues and synthesizes faithful audio on both datasets. To our knowledge, this work represents the very first formulation, dataset, and approach to solve the novel-view acoustic synthesis task, which has exciting potential applications ranging from AR/VR to art and design. Unlocked by this work, we believe that the future of novel-view synthesis is in multi-modal learning from videos. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Novel-View_Acoustic_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Novel-View_Acoustic_Synthesis_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2301.08730 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Novel-View_Acoustic_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Novel-View_Acoustic_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization | Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu | Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Robust_Generalization_Against_Photon-Limited_Corruptions_via_Worst-Case_Sharpness_Minimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Robust_Generalization_Against_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13087 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Robust_Generalization_Against_Photon-Limited_Corruptions_via_Worst-Case_Sharpness_Minimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Robust_Generalization_Against_Photon-Limited_Corruptions_via_Worst-Case_Sharpness_Minimization_CVPR_2023_paper.html | CVPR 2023 | null |
Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields | Tao Hu, Xiaogang Xu, Shu Liu, Jiaya Jia | Synthesizing photo-realistic images from a point cloud is challenging because of the sparsity of point cloud representation. Recent Neural Radiance Fields and extensions are proposed to synthesize realistic images from 2D input. In this paper, we present Point2Pix as a novel point renderer to link the 3D sparse point clouds with 2D dense image pixels. Taking advantage of the point cloud 3D prior and NeRF rendering pipeline, our method can synthesize high-quality images from colored point clouds, generally for novel indoor scenes. To improve the efficiency of ray sampling, we propose point-guided sampling, which focuses on valid samples. Also, we present Point Encoding to build Multi-scale Radiance Fields that provide discriminative 3D point features. Finally, we propose Fusion Encoding to efficiently synthesize high-quality images. Extensive experiments on the ScanNet and ArkitScenes datasets demonstrate the effectiveness and generalization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Point2Pix_Photo-Realistic_Point_Cloud_Rendering_via_Neural_Radiance_Fields_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.16482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Point2Pix_Photo-Realistic_Point_Cloud_Rendering_via_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Point2Pix_Photo-Realistic_Point_Cloud_Rendering_via_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Superclass Learning With Representation Enhancement | Zeyu Gan, Suyun Zhao, Jinlong Kang, Liyuan Shang, Hong Chen, Cuiping Li | In many real scenarios, data are often divided into a handful of artificial super categories in terms of expert knowledge rather than the representations of images. Concretely, a superclass may contain massive and various raw categories, such as refuse sorting. Due to the lack of common semantic features, the existing classification techniques are intractable to recognize superclass without raw class labels, thus they suffer severe performance damage or require huge annotation costs. To narrow this gap, this paper proposes a superclass learning framework, called SuperClass Learning with Representation Enhancement(SCLRE), to recognize super categories by leveraging enhanced representation. Specifically, by exploiting the self-attention technique across the batch, SCLRE collapses the boundaries of those raw categories and enhances the representation of each superclass. On the enhanced representation space, a superclass-aware decision boundary is then reconstructed. Theoretically, we prove that by leveraging attention techniques the generalization error of SCLRE can be bounded under superclass scenarios. Experimentally, extensive results demonstrate that SCLRE outperforms the baseline and other contrastive-based methods on CIFAR-100 datasets and four high-resolution datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Superclass_Learning_With_Representation_Enhancement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Superclass_Learning_With_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Superclass_Learning_With_Representation_Enhancement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Superclass_Learning_With_Representation_Enhancement_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Prompt Tuning for Generative Transfer Learning | Kihyuk Sohn, Huiwen Chang, José Lezama, Luisa Polania, Han Zhang, Yuan Hao, Irfan Essa, Lu Jiang | Learning generative image models from various domains efficiently needs transferring knowledge from an image synthesis model trained on a large dataset. We present a recipe for learning vision transformers by generative knowledge transfer. We base our framework on generative vision transformers representing an image as a sequence of visual tokens with the autoregressive or non-autoregressive transformers. To adapt to a new domain, we employ prompt tuning, which prepends learnable tokens called prompts to the image token sequence and introduces a new prompt design for our task. We study on a variety of visual domains with varying amounts of training images. We show the effectiveness of knowledge transfer and a significantly better image generation quality. Code is available at https://github.com/google-research/generative_transfer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sohn_Visual_Prompt_Tuning_for_Generative_Transfer_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sohn_Visual_Prompt_Tuning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.00990 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sohn_Visual_Prompt_Tuning_for_Generative_Transfer_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sohn_Visual_Prompt_Tuning_for_Generative_Transfer_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
NICO++: Towards Better Benchmarking for Domain Generalization | Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, Peng Cui | Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named NICO++ along with more rational evaluation methods for comprehensively evaluating DG algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respectively. Two novel generalization bounds from the perspective of data construction are proposed to prove that limited concept shift and significant covariate shift favor the evaluation capability for generalization. Through extensive experiments, NICO++ shows its superior evaluation capability compared with current DG datasets and its contribution in alleviating unfairness caused by the leak of oracle knowledge in model selection. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_NICO_Towards_Better_Benchmarking_for_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_NICO_Towards_Better_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_NICO_Towards_Better_Benchmarking_for_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_NICO_Towards_Better_Benchmarking_for_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
CHMATCH: Contrastive Hierarchical Matching and Robust Adaptive Threshold Boosted Semi-Supervised Learning | Jianlong Wu, Haozhe Yang, Tian Gan, Ning Ding, Feijun Jiang, Liqiang Nie | The recently proposed FixMatch and FlexMatch have achieved remarkable results in the field of semi-supervised learning. But these two methods go to two extremes as FixMatch and FlexMatch use a pre-defined constant threshold for all classes and an adaptive threshold for each category, respectively. By only investigating consistency regularization, they also suffer from unstable results and indiscriminative feature representation, especially under the situation of few labeled samples. In this paper, we propose a novel CHMatch method, which can learn robust adaptive thresholds for instance-level prediction matching as well as discriminative features by contrastive hierarchical matching. We first present a memory-bank based robust threshold learning strategy to select highly-confident samples. In the meantime, we make full use of the structured information in the hierarchical labels to learn an accurate affinity graph for contrastive learning. CHMatch achieves very stable and superior results on several commonly-used benchmarks. For example, CHMatch achieves 8.44% and 9.02% error rate reduction over FlexMatch on CIFAR-100 under WRN-28-2 with only 4 and 25 labeled samples per class, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_CHMATCH_Contrastive_Hierarchical_Matching_and_Robust_Adaptive_Threshold_Boosted_Semi-Supervised_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_CHMATCH_Contrastive_Hierarchical_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_CHMATCH_Contrastive_Hierarchical_Matching_and_Robust_Adaptive_Threshold_Boosted_Semi-Supervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_CHMATCH_Contrastive_Hierarchical_Matching_and_Robust_Adaptive_Threshold_Boosted_Semi-Supervised_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Dependencies Emerging From Learning Massive Categories | Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael Jordan, Zheng-Jun Zha | This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call neural dependency. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is sparse, which implies one category relates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularize. Code to exactly reproduce the results in this work will be released publicly. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Neural_Dependencies_Emerging_From_Learning_Massive_Categories_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Neural_Dependencies_Emerging_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12339 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Neural_Dependencies_Emerging_From_Learning_Massive_Categories_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Neural_Dependencies_Emerging_From_Learning_Massive_Categories_CVPR_2023_paper.html | CVPR 2023 | null |
ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects | Marco Toschi, Riccardo De Matteo, Riccardo Spezialetti, Daniele De Gregorio, Luigi Di Stefano, Samuele Salti | In this paper, we focus on the problem of rendering novel views from a Neural Radiance Field (NeRF) under unobserved light conditions. To this end, we introduce a novel dataset, dubbed ReNe (Relighting NeRF), framing real world objects under one-light-at-time (OLAT) conditions, annotated with accurate ground-truth camera and light poses. Our acquisition pipeline leverages two robotic arms holding, respectively, a camera and an omni-directional point-wise light source. We release a total of 20 scenes depicting a variety of objects with complex geometry and challenging materials. Each scene includes 2000 images, acquired from 50 different points of views under 40 different OLAT conditions. By leveraging the dataset, we perform an ablation study on the relighting capability of variants of the vanilla NeRF architecture and identify a lightweight architecture that can render novel views of an object under novel light conditions, which we use to establish a non-trivial baseline for the dataset. Dataset and benchmark are available at https://eyecan-ai.github.io/rene. | https://openaccess.thecvf.com/content/CVPR2023/papers/Toschi_ReLight_My_NeRF_A_Dataset_for_Novel_View_Synthesis_and_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.10448 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Toschi_ReLight_My_NeRF_A_Dataset_for_Novel_View_Synthesis_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Toschi_ReLight_My_NeRF_A_Dataset_for_Novel_View_Synthesis_and_CVPR_2023_paper.html | CVPR 2023 | null |
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation | Zicong Fan, Omid Taheri, Dimitrios Tzionas, Muhammed Kocabas, Manuel Kaufmann, Michael J. Black, Otmar Hilliges | Humans intuitively understand that inanimate objects do not move by themselves, but that state changes are typically caused by human manipulation (e.g., the opening of a book). This is not yet the case for machines. In part this is because there exist no datasets with ground-truth 3D annotations for the study of physically consistent and synchronised motion of hands and articulated objects. To this end, we introduce ARCTIC -- a dataset of two hands that dexterously manipulate objects, containing 2.1M video frames paired with accurate 3D hand and object meshes and detailed, dynamic contact information. It contains bi-manual articulation of objects such as scissors or laptops, where hand poses and object states evolve jointly in time. We propose two novel articulated hand-object interaction tasks: (1) Consistent motion reconstruction: Given a monocular video, the goal is to reconstruct two hands and articulated objects in 3D, so that their motions are spatio-temporally consistent. (2) Interaction field estimation: Dense relative hand-object distances must be estimated from images. We introduce two baselines ArcticNet and InterField, respectively and evaluate them qualitatively and quantitatively on ARCTIC. Our code and data are available at https://arctic.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_ARCTIC_A_Dataset_for_Dexterous_Bimanual_Hand-Object_Manipulation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fan_ARCTIC_A_Dataset_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.13662 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_ARCTIC_A_Dataset_for_Dexterous_Bimanual_Hand-Object_Manipulation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_ARCTIC_A_Dataset_for_Dexterous_Bimanual_Hand-Object_Manipulation_CVPR_2023_paper.html | CVPR 2023 | null |
Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking | Xiongbiao Luo | Stochastic filtering is widely used to deal with nonlinear optimization problems such as 3-D and visual tracking in various computer vision and augmented reality applications. Many current methods suffer from an imbalance between exploration and exploitation due to their particle degeneracy and impoverishment, resulting in local optimums. To address this imbalance, this work proposes a new constrained evolutionary diffusion filter for nonlinear optimization. Specifically, this filter develops spatial state constraints and adaptive history-recall differential evolution embedded evolutionary stochastic diffusion instead of sequential resampling to resolve the degeneracy and impoverishment problem. With application to monocular endoscope 3-D tracking, the experimental results show that the proposed filtering significantly improves the balance between exploration and exploitation and certainly works better than recent 3-D tracking methods. Particularly, the surgical tracking error was reduced from 4.03 mm to 2.59 mm. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Constrained_Evolutionary_Diffusion_Filter_for_Monocular_Endoscope_Tracking_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Constrained_Evolutionary_Diffusion_Filter_for_Monocular_Endoscope_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Constrained_Evolutionary_Diffusion_Filter_for_Monocular_Endoscope_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
MAGVIT: Masked Generative Video Transformer | Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G. Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, Lu Jiang | We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at https://magvit.cs.cmu.edu. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_MAGVIT_Masked_Generative_Video_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_MAGVIT_Masked_Generative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05199 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_MAGVIT_Masked_Generative_Video_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_MAGVIT_Masked_Generative_Video_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Content-Aware Token Sharing for Efficient Semantic Segmentation With Vision Transformers | Chenyang Lu, Daan de Geus, Gijs Dubbelman | This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Transformers (ViTs). Existing works have proposed token reduction approaches to improve the efficiency of ViT-based image classification networks, but these methods are not directly applicable to semantic segmentation, which we address in this work. We observe that, for semantic segmentation, multiple image patches can share a token if they contain the same semantic class, as they contain redundant information. Our approach leverages this by employing an efficient, class-agnostic policy network that predicts if image patches contain the same semantic class, and lets them share a token if they do. With experiments, we explore the critical design choices of CTS and show its effectiveness on the ADE20K, Pascal Context and Cityscapes datasets, various ViT backbones, and different segmentation decoders. With Content-aware Token Sharing, we are able to reduce the number of processed tokens by up to 44%, without diminishing the segmentation quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Content-Aware_Token_Sharing_for_Efficient_Semantic_Segmentation_With_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_Content-Aware_Token_Sharing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Content-Aware_Token_Sharing_for_Efficient_Semantic_Segmentation_With_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Content-Aware_Token_Sharing_for_Efficient_Semantic_Segmentation_With_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Toward Accurate Post-Training Quantization for Image Super Resolution | Zhijun Tu, Jie Hu, Hanting Chen, Yunhe Wang | Model quantization is a crucial step for deploying super resolution (SR) networks on mobile devices. However, existing works focus on quantization-aware training, which requires complete dataset and expensive computational overhead. In this paper, we study post-training quantization(PTQ) for image super resolution using only a few unlabeled calibration images. As the SR model aims to maintain the texture and color information of input images, the distribution of activations are long-tailed, asymmetric and highly dynamic compared with classification models. To this end, we introduce the density-based dual clipping to cut off the outliers based on analyzing the asymmetric bounds of activations. Moreover, we present a novel pixel aware calibration method with the supervision of the full-precision model to accommodate the highly dynamic range of different samples. Extensive experiments demonstrate that the proposed method significantly outperforms existing PTQ algorithms on various models and datasets. For instance, we get a 2.091 dB increase on Urban100 benchmark when quantizing EDSRx4 to 4-bit with 100 unlabeled images. Our code is available at both https://github.com/huawei-noah/Efficient-Computing/tree/master/Quantization/PTQ4SR and https://gitee.com/mindspore/models/tree/master/research/cv/PTQ4SR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tu_Toward_Accurate_Post-Training_Quantization_for_Image_Super_Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tu_Toward_Accurate_Post-Training_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Toward_Accurate_Post-Training_Quantization_for_Image_Super_Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_Toward_Accurate_Post-Training_Quantization_for_Image_Super_Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision | Fangqiang Ding, Andras Palffy, Dariu M. Gavrila, Chris Xiaoxuan Lu | This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning. Our approach is motivated by the co-located sensing redundancy in modern autonomous vehicles. Such redundancy implicitly provides various forms of supervision cues to the radar scene flow estimation. Specifically, we introduce a multi-task model architecture for the identified cross-modal learning problem and propose loss functions to opportunistically engage scene flow estimation using multiple cross-modal constraints for effective model training. Extensive experiments show the state-of-the-art performance of our method and demonstrate the effectiveness of cross-modal supervised learning to infer more accurate 4D radar scene flow. We also show its usefulness to two subtasks - motion segmentation and ego-motion estimation. Our source code will be available on https://github.com/Toytiny/CMFlow. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_Hidden_Gems_4D_Radar_Scene_Flow_Learning_Using_Cross-Modal_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_Hidden_Gems_4D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00462 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Hidden_Gems_4D_Radar_Scene_Flow_Learning_Using_Cross-Modal_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Hidden_Gems_4D_Radar_Scene_Flow_Learning_Using_Cross-Modal_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
OmniMAE: Single Model Masked Pretraining on Images and Videos | Rohit Girdhar, Alaaeldin El-Nouby, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra | Transformer-based architectures have become competitive across a variety of visual domains, most notably images and videos. While prior work studies these modalities in isolation, having a common architecture suggests that one can train a single unified model for multiple visual modalities. Prior attempts at unified modeling typically use architectures tailored for vision tasks, or obtain worse performance compared to single modality models. In this work, we show that masked autoencoding can be used to train a simple Vision Transformer on images and videos, without requiring any labeled data. This single model learns visual representations that are comparable to or better than single-modality representations on both image and video benchmarks, while using a much simpler architecture. Furthermore, this model can be learned by dropping 90% of the image and 95% of the video patches, enabling extremely fast training of huge model architectures. In particular, we show that our single ViT-Huge model can be finetuned to achieve 86.6% on ImageNet and 75.5% on the challenging Something Something-v2 video benchmark, setting a new state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Girdhar_OmniMAE_Single_Model_Masked_Pretraining_on_Images_and_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Girdhar_OmniMAE_Single_Model_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2206.08356 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Girdhar_OmniMAE_Single_Model_Masked_Pretraining_on_Images_and_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Girdhar_OmniMAE_Single_Model_Masked_Pretraining_on_Images_and_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video | Mohammed Suhail, Erika Lu, Zhengqi Li, Noah Snavely, Leonid Sigal, Forrester Cole | We propose a method to decompose a video into a background and a set of foreground layers, where the background captures stationary elements while the foreground layers capture moving objects along with their associated effects (e.g. shadows and reflections). Our approach is designed for unconstrained monocular videos, with arbitrary camera and object motion. Prior work that tackles this problem assumes that the video can be mapped onto a fixed 2D canvas, severely limiting the possible space of camera motion. Instead, our method applies recent progress in monocular camera pose and depth estimation to create a full, RGBD video layer for the background, along with a video layer for each foreground object. To solve the underconstrained decomposition problem, we propose a new loss formulation based on multi-view consistency. We test our method on challenging videos with complex camera motion and show significant qualitative improvement over current approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Suhail_Omnimatte3D_Associating_Objects_and_Their_Effects_in_Unconstrained_Monocular_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Suhail_Omnimatte3D_Associating_Objects_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Suhail_Omnimatte3D_Associating_Objects_and_Their_Effects_in_Unconstrained_Monocular_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Suhail_Omnimatte3D_Associating_Objects_and_Their_Effects_in_Unconstrained_Monocular_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Real-Time Neural Light Field on Mobile Devices | Junli Cao, Huan Wang, Pavlo Chemerys, Vladislav Shakhrai, Ju Hu, Yun Fu, Denys Makoviichuk, Sergey Tulyakov, Jian Ren | Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow, limiting the application scenarios of utilizing NeRF on resource-constrained hardware, such as mobile devices. Many works have been conducted to reduce the latency of running NeRF models. However, most of them still require high-end GPU for acceleration or extra storage memory, which is all unavailable on mobile devices. Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. Nevertheless, to reach a similar rendering quality as NeRF, the network in NeLF is designed with intensive computation, which is not mobile-friendly. In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering. We follow the setting of NeLF to train our network. Unlike existing works, we introduce a novel network architecture that runs efficiently on mobile devices with low latency and small size, i.e., saving 15x 24x storage compared with MobileNeRF. Our model achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world scenes on mobile devices, e.g., 18.04ms (iPhone 13) for rendering one 1008x756 image of real 3D scenes. Additionally, we achieve similar image quality as NeRF and better quality than MobileNeRF (PSNR 26.15 vs. 25.91 on the real-world forward-facing dataset). | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Real-Time_Neural_Light_Field_on_Mobile_Devices_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Real-Time_Neural_Light_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08057 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Real-Time_Neural_Light_Field_on_Mobile_Devices_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Real-Time_Neural_Light_Field_on_Mobile_Devices_CVPR_2023_paper.html | CVPR 2023 | null |
Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class | Chao Shang, Hongliang Li, Fanman Meng, Qingbo Wu, Heqian Qiu, Lanxiao Wang | Class-incremental semantic segmentation aims to incrementally learn new classes while maintaining the capability to segment old ones, and suffers catastrophic forgetting since the old-class labels are unavailable. Most existing methods are based on convolutional networks and prevent forgetting through knowledge distillation, which (1) need to add additional convolutional layers to predict new classes, and (2) ignore to distinguish different regions corresponding to old and new classes during knowledge distillation and roughly distill all the features, thus limiting the learning of new classes. Based on the above observations, we propose a new transformer framework for class-incremental semantic segmentation, dubbed Incrementer, which only needs to add new class tokens to the transformer decoder for new-class learning. Based on the Incrementer, we propose a new knowledge distillation scheme that focuses on the distillation in the old-class regions, which reduces the constraints of the old model on the new-class learning, thus improving the plasticity. Moreover, we propose a class deconfusion strategy to alleviate the overfitting to new classes and the confusion of similar classes. Our method is simple and effective, and extensive experiments show that our method outperforms the SOTAs by a large margin (5 15 absolute points boosts on both Pascal VOC and ADE20k). We hope that our Incrementer can serve as a new strong pipeline for class-incremental semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.html | CVPR 2023 | null |
End-to-End Video Matting With Trimap Propagation | Wei-Lun Huang, Ming-Sui Lee | The research of video matting mainly focuses on temporal coherence and has gained significant improvement via neural networks. However, matting usually relies on user-annotated trimaps to estimate alpha values, which is a labor-intensive issue. Although recent studies exploit video object segmentation methods to propagate the given trimaps, they suffer inconsistent results. Here we present a more robust and faster end-to-end video matting model equipped with trimap propagation called FTP-VM (Fast Trimap Propagation - Video Matting). The FTP-VM combines trimap propagation and video matting in one model, where the additional backbone in memory matching is replaced with the proposed lightweight trimap fusion module. The segmentation consistency loss is adopted from automotive segmentation to fit trimap segmentation with the collaboration of RNN (Recurrent Neural Network) to improve the temporal coherence. The experimental results demonstrate that the FTP-VM performs competitively both in composited and real videos only with few given trimaps. The efficiency is eight times higher than the state-of-the-art methods, which confirms its robustness and applicability in real-time scenarios. The code is available at https://github.com/csvt32745/FTP-VM | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_End-to-End_Video_Matting_With_Trimap_Propagation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_End-to-End_Video_Matting_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_End-to-End_Video_Matting_With_Trimap_Propagation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_End-to-End_Video_Matting_With_Trimap_Propagation_CVPR_2023_paper.html | CVPR 2023 | null |
DropMAE: Masked Autoencoders With Spatial-Attention Dropout for Tracking Tasks | Qiangqiang Wu, Tianyu Yang, Ziquan Liu, Baoyuan Wu, Ying Shan, Antoni B. Chan | In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-based downstream tasks, including visual object tracking (VOT) and video object segmentation (VOS). A simple extension of MAE is to randomly mask out frame patches in videos and reconstruct the frame pixels. However, we find that this simple baseline heavily relies on spatial cues while ignoring temporal relations for frame reconstruction, thus leading to sub-optimal temporal matching representations for VOT and VOS. To alleviate this problem, we propose DropMAE, which adaptively performs spatial-attention dropout in the frame reconstruction to facilitate temporal correspondence learning in videos. We show that our DropMAE is a strong and efficient temporal matching learner, which achieves better finetuning results on matching-based tasks than the ImageNetbased MAE with 2x faster pre-training speed. Moreover, we also find that motion diversity in pre-training videos is more important than scene diversity for improving the performance on VOT and VOS. Our pre-trained DropMAE model can be directly loaded in existing ViT-based trackers for fine-tuning without further modifications. Notably, DropMAE sets new state-of-the-art performance on 8 out of 9 highly competitive video tracking and segmentation datasets. Our code and pre-trained models are available at https://github.com/jimmy-dq/DropMAE.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_DropMAE_Masked_Autoencoders_With_Spatial-Attention_Dropout_for_Tracking_Tasks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_DropMAE_Masked_Autoencoders_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_DropMAE_Masked_Autoencoders_With_Spatial-Attention_Dropout_for_Tracking_Tasks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_DropMAE_Masked_Autoencoders_With_Spatial-Attention_Dropout_for_Tracking_Tasks_CVPR_2023_paper.html | CVPR 2023 | null |
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning | Wei Ji, Renjie Liang, Zhedong Zheng, Wenqiao Zhang, Shengyu Zhang, Juncheng Li, Mengze Li, Tat-seng Chua | Recent research on video moment retrieval has mostly focused on enhancing the performance of accuracy, efficiency, and robustness, all of which largely rely on the abundance of high-quality annotations. While the precise frame-level annotations are time-consuming and cost-expensive, few attentions have been paid to the labeling process. In this work, we explore a new interactive manner to stimulate the process of human-in-the-loop annotation in video moment retrieval task. The key challenge is to select "ambiguous" frames and videos for binary annotations to facilitate the network training. To be specific, we propose a new hierarchical uncertainty-based modeling that explicitly considers modeling the uncertainty of each frame within the entire video sequence corresponding to the query description, and selecting the frame with the highest uncertainty. Only selected frame will be annotated by the human experts, which can largely reduce the workload. After obtaining a small number of labels provided by the expert, we show that it is sufficient to learn a competitive video moment retrieval model in such a harsh environment. Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection. In general, our active learning strategy for video moment retrieval works not only at the frame level but also at the sequence level. Experiments on two public datasets validate the effectiveness of our proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_Are_Binary_Annotations_Sufficient_Video_Moment_Retrieval_via_Hierarchical_Uncertainty-Based_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Are_Binary_Annotations_Sufficient_Video_Moment_Retrieval_via_Hierarchical_Uncertainty-Based_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Are_Binary_Annotations_Sufficient_Video_Moment_Retrieval_via_Hierarchical_Uncertainty-Based_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity Clothed Avatar Reconstruction From a Single Image | Tingting Liao, Xiaomei Zhang, Yuliang Xiu, Hongwei Yi, Xudong Liu, Guo-Jun Qi, Yong Zhang, Xuan Wang, Xiangyu Zhu, Zhen Lei | This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence of the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes. The codes will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liao_High-Fidelity_Clothed_Avatar_Reconstruction_From_a_Single_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liao_High-Fidelity_Clothed_Avatar_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.03903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_High-Fidelity_Clothed_Avatar_Reconstruction_From_a_Single_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_High-Fidelity_Clothed_Avatar_Reconstruction_From_a_Single_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Object Counting | Jingyi Xu, Hieu Le, Vu Nguyen, Viresh Ranjan, Dimitris Samaras | Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods require human-annotated exemplars as inputs which are often unavailable for novel categories, especially for autonomous systems. Thus, we propose zero-shot object counting (ZSC), a new setting where only the class name is available during test time. Such a counting system does not require human annotators in the loop and can operate automatically. Starting from a class name, we propose a method that can accurately identify the optimal patches which can then be used as counting exemplars. Specifically, we first construct a class prototype to select the patches that are likely to contain the objects of interest, namely class-relevant patches. Furthermore, we introduce a model that can quantitatively measure how suitable an arbitrary patch is as a counting exemplar. By applying this model to all the candidate patches, we can select the most suitable patches as exemplars for counting. Experimental results on a recent class-agnostic counting dataset, FSC-147, validate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Zero-Shot_Object_Counting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Zero-Shot_Object_Counting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Zero-Shot_Object_Counting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Zero-Shot_Object_Counting_CVPR_2023_paper.html | CVPR 2023 | null |
Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective | Jinjing Zhu, Haotian Bai, Lin Wang | Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention in ViT for direct domain alignment. However, as the performance of cross-attention highly relies on the quality of pseudo labels for targeted samples, it becomes less effective when the domain gap becomes large. We solve this problem from a game theory's perspective with the proposed model dubbed as PMTrans, which bridges source and target domains with an intermediate domain. Specifically, we propose a novel ViT-based module called PatchMix that effectively builds up the intermediate domain, i.e., probability distribution, by learning to sample patches from both domains based on the game-theoretical models. This way, it learns to mix the patches from the source and target domains to maximize the cross entropy (CE), while exploiting two semi-supervised mixup losses in the feature and label spaces to minimize it. As such, we interpret the process of UDA as a min-max CE game with three players, including the feature extractor, classifier, and PatchMix, to find the Nash Equilibria. Moreover, we leverage attention maps from ViT to re-weight the label of each patch by its importance, making it possible to obtain more domain-discriminative feature representations. We conduct extensive experiments on four benchmark datasets, and the results show that PMTrans significantly surpasses the ViT-based and CNN-based SoTA methods by +3.6% on Office-Home, +1.4% on Office-31, and +17.7% on DomainNet, respectively. https://vlis2022.github.io/cvpr23/PMTrans | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Patch-Mix_Transformer_for_Unsupervised_Domain_Adaptation_A_Game_Perspective_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Patch-Mix_Transformer_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Patch-Mix_Transformer_for_Unsupervised_Domain_Adaptation_A_Game_Perspective_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Patch-Mix_Transformer_for_Unsupervised_Domain_Adaptation_A_Game_Perspective_CVPR_2023_paper.html | CVPR 2023 | null |
Implicit Diffusion Models for Continuous Super-Resolution | Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, Baochang Zhang | Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and most work only with fixed magnifications. This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation. Furthermore, we design a scale-controllable conditioning mechanism that consists of a low-resolution (LR) conditioning network and a scaling factor. The scaling factor regulates the resolution and accordingly modulates the proportion of the LR information and generated features in the final output, which enables the model to accommodate the continuous-resolution requirement. Extensive experiments validate the effectiveness of our IDM and demonstrate its superior performance over prior arts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Implicit_Diffusion_Models_for_Continuous_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_Implicit_Diffusion_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16491 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Implicit_Diffusion_Models_for_Continuous_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Implicit_Diffusion_Models_for_Continuous_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
VGFlow: Visibility Guided Flow Network for Human Reposing | Rishabh Jain, Krishna Kumar Singh, Mayur Hemani, Jingwan Lu, Mausoom Sarkar, Duygu Ceylan, Balaji Krishnamurthy | The task of human reposing involves generating a realistic image of a model standing in an arbitrary conceivable pose. There are multiple difficulties in generating perceptually accurate images and existing methods suffers from limitations in preserving texture, maintaining pattern coherence, respecting cloth boundaries, handling occlusions, manipulating skin generation etc. These difficulties are further exacerbated by the fact that the possible space of pose orientation for humans is large and variable, the nature of clothing items are highly non-rigid and the diversity in body shape differ largely among the population. To alleviate these difficulties and synthesize perceptually accurate images, we propose VGFlow, a model which uses a visibility guided flow module to disentangle the flow into visible and invisible parts of the target for simultaneous texture preservation and style manipulation. Furthermore, to tackle distinct body shapes and avoid network artifacts, we also incorporate an a self-supervised patch-wise "realness" loss to further improve the output. VGFlow achieves state-of-the-art results as observed qualitatively and quantitatively on different image quality metrics(SSIM, LPIPS, FID). | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_VGFlow_Visibility_Guided_Flow_Network_for_Human_Reposing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_VGFlow_Visibility_Guided_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.08540 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_VGFlow_Visibility_Guided_Flow_Network_for_Human_Reposing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_VGFlow_Visibility_Guided_Flow_Network_for_Human_Reposing_CVPR_2023_paper.html | CVPR 2023 | null |
Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection | Yi Yu, Feipeng Da | With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://github.com/open-mmlab/mmrotate. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Phase-Shifting_Coder_Predicting_Accurate_Orientation_in_Oriented_Object_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.06368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Phase-Shifting_Coder_Predicting_Accurate_Orientation_in_Oriented_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Phase-Shifting_Coder_Predicting_Accurate_Orientation_in_Oriented_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Selective Visual Question Answering by Learning From Your Peers | Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary, Ramakrishna Vedantam, Stefan Scherer, Xinlei Chen, Matthieu Cord, Marcus Rohrbach | Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-box, can have difficulties abstaining from answering when they are wrong. The option to abstain, also called Selective Prediction, is highly relevant when deploying systems to users who must trust the system's output (e.g., VQA assistants for users with visual impairments). For such scenarios, abstention can be especially important as users may provide out-of-distribution (OOD) or adversarial inputs that make incorrect answers more likely. In this work, we explore Selective VQA in both in-distribution (ID) and OOD scenarios, where models are presented with mixtures of ID and OOD data. The goal is to maximize the number of questions answered while minimizing the risk of error on those questions. We propose a simple yet effective Learning from Your Peers (LYP) approach for training multimodal selection functions for making abstention decisions. Our approach uses predictions from models trained on distinct subsets of the training data as targets for optimizing a Selective VQA model. It does not require additional manual labels or held-out data and provides a signal for identifying examples that are easy/difficult to generalize to. In our extensive evaluations, we show this benefits a number of models across different architectures and scales. Overall, for ID, we reach 32.92% in the selective prediction metric coverage at 1% risk of error (C@1%) which doubles the previous best coverage of 15.79% on this task. For mixed ID/OOD, using models' softmax confidences for abstention decisions performs very poorly, answering <5% of questions at 1% risk of error even when faced with only 10% OOD examples, but a learned selection function with LYP can increase that to 25.38% C@1%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dancette_Improving_Selective_Visual_Question_Answering_by_Learning_From_Your_Peers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dancette_Improving_Selective_Visual_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dancette_Improving_Selective_Visual_Question_Answering_by_Learning_From_Your_Peers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dancette_Improving_Selective_Visual_Question_Answering_by_Learning_From_Your_Peers_CVPR_2023_paper.html | CVPR 2023 | null |
CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis | Juntian Zheng, Qingyuan Zheng, Lixing Fang, Yun Liu, Li Yi | In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories. Given an object geometry, an initial human hand pose as well as a sparse control sequence of object poses, our goal is to generate a physically reasonable hand-object manipulation sequence that performs like human beings. To address such a challenge, we first design CAnonicalized Manipulation Spaces (CAMS), a two-level space hierarchy that canonicalizes the hand poses in an object-centric and contact-centric view. Benefiting from the representation capability of CAMS, we then present a two-stage framework for synthesizing human-like manipulation animations. Our framework achieves state-of-the-art performance for both rigid and articulated categories with impressive visual effects. Codes and video results can be found at our project homepage: https://cams-hoi.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_CAMS_CAnonicalized_Manipulation_Spaces_for_Category-Level_Functional_Hand-Object_Manipulation_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_CAMS_CAnonicalized_Manipulation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15469 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_CAMS_CAnonicalized_Manipulation_Spaces_for_Category-Level_Functional_Hand-Object_Manipulation_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_CAMS_CAnonicalized_Manipulation_Spaces_for_Category-Level_Functional_Hand-Object_Manipulation_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Lens Modeling | Wenqi Xian, Aljaž Božič, Noah Snavely, Christoph Lassner | Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process. However, this approach is currently limited: effects of the optical hardware stack and in particular lenses are hard to model in a unified way. This limits the quality that can be achieved for camera calibration and the fidelity of the results of 3D reconstruction. In this paper, we propose NeuroLens, a neural lens model for distortion and vignetting that can be used for point projection and ray casting and can be optimized through both operations. This means that it can (optionally) be used to perform pre-capture calibration using classical calibration targets, and can later be used to perform calibration or refinement during 3D reconstruction, e.g., while optimizing a radiance field. To evaluate the performance of our proposed model, we create a comprehensive dataset assembled from the Lensfun database with a multitude of lenses. Using this and other real-world datasets, we show that the quality of our proposed lens model outperforms standard packages as well as recent approaches while being much easier to use and extend. The model generalizes across many lens types and is trivial to integrate into existing 3D reconstruction and rendering systems. Visit our project website at: https://neural-lens.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xian_Neural_Lens_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xian_Neural_Lens_Modeling_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xian_Neural_Lens_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xian_Neural_Lens_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
CoralStyleCLIP: Co-Optimized Region and Layer Selection for Image Editing | Ambareesh Revanur, Debraj Basu, Shradha Agrawal, Dhwanit Agarwal, Deepak Pai | Edit fidelity is a significant issue in open-world controllable generative image editing. Recently, CLIP-based approaches have traded off simplicity to alleviate these problems by introducing spatial attention in a handpicked layer of a StyleGAN. In this paper, we propose CoralStyleCLIP, which incorporates a multi-layer attention-guided blending strategy in the feature space of StyleGAN2 for obtaining high-fidelity edits. We propose multiple forms of our co-optimized region and layer selection strategy to demonstrate the variation of time complexity with the quality of edits over different architectural intricacies while preserving simplicity. We conduct extensive experimental analysis and benchmark our method against state-of-the-art CLIP-based methods. Our findings suggest that CoralStyleCLIP results in high-quality edits while preserving the ease of use. | https://openaccess.thecvf.com/content/CVPR2023/papers/Revanur_CoralStyleCLIP_Co-Optimized_Region_and_Layer_Selection_for_Image_Editing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Revanur_CoralStyleCLIP_Co-Optimized_Region_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Revanur_CoralStyleCLIP_Co-Optimized_Region_and_Layer_Selection_for_Image_Editing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Revanur_CoralStyleCLIP_Co-Optimized_Region_and_Layer_Selection_for_Image_Editing_CVPR_2023_paper.html | CVPR 2023 | null |
GLeaD: Improving GANs With a Generator-Leading Task | Qingyan Bai, Ceyuan Yang, Yinghao Xu, Xihui Liu, Yujiu Yang, Yujun Shen | Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition. Towards a fairer game in GANs, we propose a new paradigm for adversarial training, which makes G assign a task to D as well. Specifically, given an image, we expect D to extract representative features that can be adequately decoded by G to reconstruct the input. That way, instead of learning freely, D is urged to align with the view of G for domain classification. Experimental results on various datasets demonstrate the substantial superiority of our approach over the baselines. For instance, we improve the FID of StyleGAN2 from 4.30 to 2.55 on LSUN Bedroom and from 4.04 to 2.82 on LSUN Church. We believe that the pioneering attempt present in this work could inspire the community with better designed generator-leading tasks for GAN improvement. Project page is at https://ezioby.github.io/glead/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_GLeaD_Improving_GANs_With_a_Generator-Leading_Task_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_GLeaD_Improving_GANs_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03752 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_GLeaD_Improving_GANs_With_a_Generator-Leading_Task_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_GLeaD_Improving_GANs_With_a_Generator-Leading_Task_CVPR_2023_paper.html | CVPR 2023 | null |
GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis | Ming Tao, Bing-Kun Bao, Hao Tang, Changsheng Xu | Synthesizing high-fidelity complex images from text is challenging. Based on large pretraining, the autoregressive and diffusion models can synthesize photo-realistic images. Although these large models have shown notable progress, there remain three flaws. 1) These models require tremendous training data and parameters to achieve good performance. 2) The multi-step generation design slows the image synthesis process heavily. 3) The synthesized visual features are challenging to control and require delicately designed prompts. To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP. GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator. Specifically, we propose a CLIP-based discriminator. The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality. Furthermore, we propose a CLIP-empowered generator that induces the visual concepts from CLIP through bridge features and prompts. The CLIP-integrated generator and discriminator boost training efficiency, and as a result, our model only requires about 3% training data and 6% learnable parameters, achieving comparable results to large pretrained autoregressive and diffusion models. Moreover, our model achieves 120 times faster synthesis speed and inherits the smooth latent space from GAN. The extensive experimental results demonstrate the excellent performance of our GALIP. Code is available at https://github.com/tobran/GALIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tao_GALIP_Generative_Adversarial_CLIPs_for_Text-to-Image_Synthesis_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.12959 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_GALIP_Generative_Adversarial_CLIPs_for_Text-to-Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_GALIP_Generative_Adversarial_CLIPs_for_Text-to-Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence | Mohammed Alloulah, Maximilian Arnold | Next generation cellular networks will implement radio sensing functions alongside customary communications, thereby enabling unprecedented worldwide sensing coverage outdoors. Deep learning has revolutionised computer vision but has had limited application to radio perception tasks, in part due to lack of systematic datasets and benchmarks dedicated to the study of the performance and promise of radio sensing. To address this gap, we present MaxRay: a synthetic radio-visual dataset and benchmark that facilitate precise target localisation in radio. We further propose to learn to localise targets in radio without supervision by extracting self-coordinates from radio-visual correspondence. We use such self-supervised coordinates to train a radio localiser network. We characterise our performance against a number of state-of-the-art baselines. Our results indicate that accurate radio target localisation can be automatically learned from paired radio-visual data without labels, which is important for empirical data. This opens the door for vast data scalability and may prove key to realising the promise of robust radio sensing atop a unified communication-perception cellular infrastructure. Dataset will be hosted on IEEE DataPort. | https://openaccess.thecvf.com/content/CVPR2023/papers/Alloulah_Look_Radiate_and_Learn_Self-Supervised_Localisation_via_Radio-Visual_Correspondence_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Alloulah_Look_Radiate_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.06424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Alloulah_Look_Radiate_and_Learn_Self-Supervised_Localisation_via_Radio-Visual_Correspondence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Alloulah_Look_Radiate_and_Learn_Self-Supervised_Localisation_via_Radio-Visual_Correspondence_CVPR_2023_paper.html | CVPR 2023 | null |
Multiplicative Fourier Level of Detail | Yishun Dou, Zhong Zheng, Qiaoqiao Jin, Bingbing Ni | We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated by a sinusoidal function and then element-wisely multiplied by a linear transformation of previous layer's representation in a layer-to-layer recursive manner, yielding the scale-aggregated encodings for a subsequent simple linear forward to get final output. In contrast to previous hybrid representations relying on interleaved multilevel fusion and nonlinear activation-based decoding, MFLOD could be elegantly characterized as a linear combination of sine basis functions with varying amplitude, frequency, and phase upon the learned multilevel features, thus offering great feasibility in Fourier analysis. Comprehensive experimental results on implicit neural representation learning tasks including image fitting, 3D shape representation, and neural radiance fields well demonstrate the superior quality and generalizability achieved by the proposed MFLOD scheme. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dou_Multiplicative_Fourier_Level_of_Detail_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dou_Multiplicative_Fourier_Level_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dou_Multiplicative_Fourier_Level_of_Detail_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dou_Multiplicative_Fourier_Level_of_Detail_CVPR_2023_paper.html | CVPR 2023 | null |
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