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A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation From a Single RGB Image | Changlong Jiang, Yang Xiao, Cunlin Wu, Mingyang Zhang, Jinghong Zheng, Zhiguo Cao, Joey Tianyi Zhou | 3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands' local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer's non-local encoding-decoding framework to build A2J-Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints' articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_A2J-Transformer_Anchor-to-Joint_Transformer_Network_for_3D_Interacting_Hand_Pose_Estimation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_A2J-Transformer_Anchor-to-Joint_Transformer_Network_for_3D_Interacting_Hand_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_A2J-Transformer_Anchor-to-Joint_Transformer_Network_for_3D_Interacting_Hand_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
The Treasure Beneath Multiple Annotations: An Uncertainty-Aware Edge Detector | Caixia Zhou, Yaping Huang, Mengyang Pu, Qingji Guan, Li Huang, Haibin Ling | Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at https://github.com/ZhouCX117/UAED. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_The_Treasure_Beneath_Multiple_Annotations_An_Uncertainty-Aware_Edge_Detector_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_The_Treasure_Beneath_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11828 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_The_Treasure_Beneath_Multiple_Annotations_An_Uncertainty-Aware_Edge_Detector_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_The_Treasure_Beneath_Multiple_Annotations_An_Uncertainty-Aware_Edge_Detector_CVPR_2023_paper.html | CVPR 2023 | null |
DP-NeRF: Deblurred Neural Radiance Field With Physical Scene Priors | Dogyoon Lee, Minhyeok Lee, Chajin Shin, Sangyoun Lee | Neural Radiance Field (NeRF) has exhibited outstanding three-dimensional (3D) reconstruction quality via the novel view synthesis from multi-view images and paired calibrated camera parameters. However, previous NeRF-based systems have been demonstrated under strictly controlled settings, with little attention paid to less ideal scenarios, including with the presence of noise such as exposure, illumination changes, and blur. In particular, though blur frequently occurs in real situations, NeRF that can handle blurred images has received little attention. The few studies that have investigated NeRF for blurred images have not considered geometric and appearance consistency in 3D space, which is one of the most important factors in 3D reconstruction. This leads to inconsistency and the degradation of the perceptual quality of the constructed scene. Hence, this paper proposes a DP-NeRF, a novel clean NeRF framework for blurred images, which is constrained with two physical priors. These priors are derived from the actual blurring process during image acquisition by the camera. DP-NeRF proposes rigid blurring kernel to impose 3D consistency utilizing the physical priors and adaptive weight proposal to refine the color composition error in consideration of the relationship between depth and blur. We present extensive experimental results for synthetic and real scenes with two types of blur: camera motion blur and defocus blur. The results demonstrate that DP-NeRF successfully improves the perceptual quality of the constructed NeRF ensuring 3D geometric and appearance consistency. We further demonstrate the effectiveness of our model with comprehensive ablation analysis. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_DP-NeRF_Deblurred_Neural_Radiance_Field_With_Physical_Scene_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_DP-NeRF_Deblurred_Neural_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_DP-NeRF_Deblurred_Neural_Radiance_Field_With_Physical_Scene_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_DP-NeRF_Deblurred_Neural_Radiance_Field_With_Physical_Scene_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering | Jingjing Jiang, Nanning Zheng | Recently, finetuning pretrained vision-language models (VLMs) has been a prevailing paradigm for achieving state-of-the-art performance in VQA. However, as VLMs scale, it becomes computationally expensive, storage inefficient, and prone to overfitting when tuning full model parameters for a specific task in low-resource settings. Although current parameter-efficient tuning methods dramatically reduce the number of tunable parameters, there still exists a significant performance gap with full finetuning. In this paper, we propose MixPHM, a redundancy-aware parameter-efficient tuning method that outperforms full finetuning in low-resource VQA. Specifically, MixPHM is a lightweight module implemented by multiple PHM-experts in a mixture-of-experts manner. To reduce parameter redundancy, we reparameterize expert weights in a low-rank subspace and share part of the weights inside and across MixPHM. Moreover, based on our quantitative analysis of representation redundancy, we propose Redundancy Regularization, which facilitates MixPHM to reduce task-irrelevant redundancy while promoting task-relevant correlation. Experiments conducted on VQA v2, GQA, and OK-VQA with different low-resource settings show that our MixPHM outperforms state-of-the-art parameter-efficient methods and is the only one consistently surpassing full finetuning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_MixPHM_Redundancy-Aware_Parameter-Efficient_Tuning_for_Low-Resource_Visual_Question_Answering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_MixPHM_Redundancy-Aware_Parameter-Efficient_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01239 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_MixPHM_Redundancy-Aware_Parameter-Efficient_Tuning_for_Low-Resource_Visual_Question_Answering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_MixPHM_Redundancy-Aware_Parameter-Efficient_Tuning_for_Low-Resource_Visual_Question_Answering_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization | Ji Li, Weixi Wang, Yuesong Nan, Hui Ji | When taking a picture, any camera shake during the shutter time can result in a blurred image. Recovering a sharp image from the one blurred by camera shake is a challenging yet important problem. Most existing deep learning methods use supervised learning to train a deep neural network (DNN) on a dataset of many pairs of blurred/latent images. In contrast, this paper presents a dataset-free deep learning method for removing uniform and non-uniform blur effects from images of static scenes. Our method involves a DNN-based re-parametrization of the latent image, and we propose a Monte Carlo Expectation Maximization (MCEM) approach to train the DNN without requiring any latent images. The Monte Carlo simulation is implemented via Langevin dynamics. Experiments showed that the proposed method outperforms existing methods significantly in removing motion blur from images of static scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Self-Supervised_Blind_Motion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper.html | CVPR 2023 | null |
DeAR: Debiasing Vision-Language Models With Additive Residuals | Ashish Seth, Mayur Hemani, Chirag Agarwal | Large pre-trained vision-language models (VLMs) reduce the time for developing predictive models for various vision-grounded language downstream tasks by providing rich, adaptable image and text representations. However, these models suffer from societal biases owing to the skewed distribution of various identity groups in the training data. These biases manifest as the skewed similarity between the representations for specific text concepts and images of people of different identity groups and, therefore, limit the usefulness of such models in real-world high-stakes applications. In this work, we present DeAR (Debiasing with Additive Residuals), a novel debiasing method that learns additive residual image representations to offset the original representations, ensuring fair output representations. In doing so, it reduces the ability of the representations to distinguish between the different identity groups. Further, we observe that the current fairness tests are performed on limited face image datasets that fail to indicate why a specific text concept should/should not apply to them. To bridge this gap and better evaluate DeAR, we introduce a new context-based bias benchmarking dataset - the Protected Attribute Tag Association (PATA) dataset for evaluating the fairness of large pre-trained VLMs. Additionally, PATA provides visual context for a diverse human population in different scenarios with both positive and negative connotations. Experimental results for fairness and zero-shot performance preservation using multiple datasets demonstrate the efficacy of our framework. | https://openaccess.thecvf.com/content/CVPR2023/papers/Seth_DeAR_Debiasing_Vision-Language_Models_With_Additive_Residuals_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Seth_DeAR_Debiasing_Vision-Language_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10431 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Seth_DeAR_Debiasing_Vision-Language_Models_With_Additive_Residuals_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Seth_DeAR_Debiasing_Vision-Language_Models_With_Additive_Residuals_CVPR_2023_paper.html | CVPR 2023 | null |
E2PN: Efficient SE(3)-Equivariant Point Network | Minghan Zhu, Maani Ghaffari, William A. Clark, Huei Peng | This paper proposes a convolution structure for learning SE(3)-equivariant features from 3D point clouds. It can be viewed as an equivariant version of kernel point convolutions (KPConv), a widely used convolution form to process point cloud data. Compared with existing equivariant networks, our design is simple, lightweight, fast, and easy to be integrated with existing task-specific point cloud learning pipelines. We achieve these desirable properties by combining group convolutions and quotient representations. Specifically, we discretize SO(3) to finite groups for their simplicity while using SO(2) as the stabilizer subgroup to form spherical quotient feature fields to save computations. We also propose a permutation layer to recover SO(3) features from spherical features to preserve the capacity to distinguish rotations. Experiments show that our method achieves comparable or superior performance in various tasks, including object classification, pose estimation, and keypoint-matching, while consuming much less memory and running faster than existing work. The proposed method can foster the development of equivariant models for real-world applications based on point clouds. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_E2PN_Efficient_SE3-Equivariant_Point_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_E2PN_Efficient_SE3-Equivariant_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_E2PN_Efficient_SE3-Equivariant_Point_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_E2PN_Efficient_SE3-Equivariant_Point_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding Masked Image Modeling via Learning Occlusion Invariant Feature | Xiangwen Kong, Xiangyu Zhang | Recently, Masked Image Modeling (MIM) achieves great success in self-supervised visual recognition. However, as a reconstruction-based framework, it is still an open question to understand how MIM works, since MIM appears very different from previous well-studied siamese approaches such as contrastive learning. In this paper, we propose a new viewpoint: MIM implicitly learns occlusion-invariant features, which is analogous to other siamese methods while the latter learns other invariance. By relaxing MIM formulation into an equivalent siamese form, MIM methods can be interpreted in a unified framework with conventional methods, among which only a) data transformations, i.e. what invariance to learn, and b) similarity measurements are different. Furthermore, taking MAE (He et al., 2021) as a representative example of MIM, we empirically find the success of MIM models relates a little to the choice of similarity functions, but the learned occlusion invariant feature introduced by masked image -- it turns out to be a favored initialization for vision transformers, even though the learned feature could be less semantic. We hope our findings could inspire researchers to develop more powerful self-supervised methods in computer vision community. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kong_Understanding_Masked_Image_Modeling_via_Learning_Occlusion_Invariant_Feature_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kong_Understanding_Masked_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.04164 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Understanding_Masked_Image_Modeling_via_Learning_Occlusion_Invariant_Feature_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Understanding_Masked_Image_Modeling_via_Learning_Occlusion_Invariant_Feature_CVPR_2023_paper.html | CVPR 2023 | null |
Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space | Siwon Kim, Jinoh Oh, Sungjin Lee, Seunghak Yu, Jaeyoung Do, Tara Taghavi | Concept-based explanation aims to provide concise and human-understandable explanations of an image classifier. However, existing concept-based explanation methods typically require a significant amount of manually collected concept-annotated images. This is costly and runs the risk of human biases being involved in the explanation. In this paper, we propose counterfactual explanation with text-driven concepts (CounTEX), where the concepts are defined only from text by leveraging a pre-trained multi-modal joint embedding space without additional concept-annotated datasets. A conceptual counterfactual explanation is generated with text-driven concepts. To utilize the text-driven concepts defined in the joint embedding space to interpret target classifier outcome, we present a novel projection scheme for mapping the two spaces with a simple yet effective implementation. We show that CounTEX generates faithful explanations that provide a semantic understanding of model decision rationale robust to human bias. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Grounding_Counterfactual_Explanation_of_Image_Classifiers_to_Textual_Concept_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Grounding_Counterfactual_Explanation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Grounding_Counterfactual_Explanation_of_Image_Classifiers_to_Textual_Concept_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Grounding_Counterfactual_Explanation_of_Image_Classifiers_to_Textual_Concept_Space_CVPR_2023_paper.html | CVPR 2023 | null |
A Dynamic Multi-Scale Voxel Flow Network for Video Prediction | Xiaotao Hu, Zhewei Huang, Ailin Huang, Jun Xu, Shuchang Zhou | The performance of video prediction has been greatly boosted by advanced deep neural networks. However, most of the current methods suffer from large model sizes and require extra inputs, e.g., semantic/depth maps, for promising performance. For efficiency consideration, in this paper, we propose a Dynamic Multi-scale Voxel Flow Network (DMVFN) to achieve better video prediction performance at lower computational costs with only RGB images, than previous methods. The core of our DMVFN is a differentiable routing module that can effectively perceive the motion scales of video frames. Once trained, our DMVFN selects adaptive sub-networks for different inputs at the inference stage. Experiments on several benchmarks demonstrate that our DMVFN is an order of magnitude faster than Deep Voxel Flow and surpasses the state-of-the-art iterative-based OPT on generated image quality. Our code and demo are available at https://huxiaotaostasy.github.io/DMVFN/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_A_Dynamic_Multi-Scale_Voxel_Flow_Network_for_Video_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_A_Dynamic_Multi-Scale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09875 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_A_Dynamic_Multi-Scale_Voxel_Flow_Network_for_Video_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_A_Dynamic_Multi-Scale_Voxel_Flow_Network_for_Video_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View | Shengchao Zhou, Weizhou Liu, Chen Hu, Shuchang Zhou, Chao Ma | In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-modality and single-modality is diverse and complex. Since the multi-modal methods have system complexity while the accuracy of single-modal ones is relatively low, how to make a tradeoff between them is difficult. In this work, we propose a universal cross-modality knowledge distillation framework (UniDistill) to improve the performance of single-modality detectors. Specifically, during training, UniDistill projects the features of both the teacher and the student detector into Bird's-Eye-View (BEV), which is a friendly representation for different modalities. Then, three distillation losses are calculated to sparsely align the foreground features, helping the student learn from the teacher without introducing additional cost during inference. Taking advantage of the similar detection paradigm of different detectors in BEV, UniDistill easily supports LiDAR-to-camera, camera-to-LiDAR, fusion-to-LiDAR and fusion-to-camera distillation paths. Furthermore, the three distillation losses can filter the effect of misaligned background information and balance between objects of different sizes, improving the distillation effectiveness. Extensive experiments on nuScenes demonstrate that UniDistill effectively improves the mAP and NDS of student detectors by 2.0% 3.2%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_UniDistill_A_Universal_Cross-Modality_Knowledge_Distillation_Framework_for_3D_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_UniDistill_A_Universal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_UniDistill_A_Universal_Cross-Modality_Knowledge_Distillation_Framework_for_3D_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_UniDistill_A_Universal_Cross-Modality_Knowledge_Distillation_Framework_for_3D_Object_CVPR_2023_paper.html | CVPR 2023 | null |
SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation | Huimin Huang, Shiao Xie, Lanfen Lin, Ruofeng Tong, Yen-Wei Chen, Yuexiang Li, Hong Wang, Yawen Huang, Yefeng Zheng | Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples. These successes concentrate on the pixel-wise consistency by using convolutional neural networks (CNNs) but fail to address both global learning capability and class-level features for unlabeled data. Recent works raise a new trend that Trans- former achieves superior performance on the entire feature map in various tasks. In this paper, we unify the current dominant Mean-Teacher approaches by reconciling intra- model and inter-model properties for semi-supervised segmentation to produce a novel algorithm, SemiCVT, that absorbs the quintessence of CNNs and Transformer in a comprehensive way. Specifically, we first design a parallel CNN-Transformer architecture (CVT) with introducing an intra-model local-global interaction schema (LGI) in Fourier domain for full integration. The inter-model class- wise consistency is further presented to complement the class-level statistics of CNNs and Transformer in a cross- teaching manner. Extensive empirical evidence shows that SemiCVT yields consistent improvements over the state-of- the-art methods in two public benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_SemiCVT_Semi-Supervised_Convolutional_Vision_Transformer_for_Semantic_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_SemiCVT_Semi-Supervised_Convolutional_Vision_Transformer_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_SemiCVT_Semi-Supervised_Convolutional_Vision_Transformer_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Fine-Tuned CLIP Models Are Efficient Video Learners | Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan | Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain. In this pursuit, new parametric modules are added to learn temporal information and inter-frame relationships which require meticulous design efforts. Furthermore, when the resulting models are learned on videos, they tend to overfit on the given task distribution and lack in generalization aspect. This begs the following question: How to effectively transfer image-level CLIP representations to videos? In this work, we show that a simple Video Fine-tuned CLIP (ViFi-CLIP) baseline is generally sufficient to bridge the domain gap from images to videos. Our qualitative analysis illustrates that the frame-level processing from CLIP image-encoder followed by feature pooling and similarity matching with corresponding text embeddings helps in implicitly modeling the temporal cues within ViFi-CLIP. Such fine-tuning helps the model to focus on scene dynamics, moving objects and inter-object relationships. For low-data regimes where full fine-tuning is not viable, we propose a 'bridge and prompt' approach that first uses finetuning to bridge the domain gap and then learns prompts on language and vision side to adapt CLIP representations. We extensively evaluate this simple yet strong baseline on zero-shot, base-to-novel generalization, few-shot and fully supervised settings across five video benchmarks. Our code and models will be publicly released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rasheed_Fine-Tuned_CLIP_Models_Are_Efficient_Video_Learners_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rasheed_Fine-Tuned_CLIP_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03640 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rasheed_Fine-Tuned_CLIP_Models_Are_Efficient_Video_Learners_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rasheed_Fine-Tuned_CLIP_Models_Are_Efficient_Video_Learners_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Open-World Segmentation of Parts | Tai-Yu Pan, Qing Liu, Wei-Lun Chao, Brian Price | Segmenting object parts such as cup handles and animal bodies is important in many real-world applications but requires more annotation effort. The largest dataset nowadays contains merely two hundred object categories, implying the difficulty to scale up part segmentation to an unconstrained setting. To address this, we propose to explore a seemingly simplified but empirically useful and scalable task, class-agnostic part segmentation. In this problem, we disregard the part class labels in training and instead treat all of them as a single part class. We argue and demonstrate that models trained without part classes can better localize parts and segment them on objects unseen in training. We then present two further improvements. First, we propose to make the model object-aware, leveraging the fact that parts are "compositions" whose extents are bounded by objects, whose appearances are by nature not independent but bundled. Second, we introduce a novel approach to improve part segmentation on unseen objects, inspired by an interesting finding --- for unseen objects, the pixel-wise features extracted by the model often reveal high-quality part segments. To this end, we propose a novel self-supervised procedure that iterates between pixel clustering and supervised contrastive learning that pulls pixels closer or pushes them away. Via extensive experiments on PartImageNet and Pascal-Part, we show notable and consistent gains by our approach, essentially a critical step towards open-world part segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_Towards_Open-World_Segmentation_of_Parts_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pan_Towards_Open-World_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Towards_Open-World_Segmentation_of_Parts_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Towards_Open-World_Segmentation_of_Parts_CVPR_2023_paper.html | CVPR 2023 | null |
Stitchable Neural Networks | Zizheng Pan, Jianfei Cai, Bohan Zhuang | The public model zoo containing enormous powerful pretrained model families (e.g., ResNet/DeiT) has reached an unprecedented scope than ever, which significantly contributes to the success of deep learning. As each model family consists of pretrained models with diverse scales (e.g., DeiT-Ti/S/B), it naturally arises a fundamental question of how to efficiently assemble these readily available models in a family for dynamic accuracy-efficiency trade-offs at runtime. To this end, we present Stitchable Neural Networks (SN-Net), a novel scalable and efficient framework for model deployment. It cheaply produces numerous networks with different complexity and performance trade-offs given a family of pretrained neural networks, which we call anchors. Specifically, SN-Net splits the anchors across the blocks/layers and then stitches them together with simple stitching layers to map the activations from one anchor to another. With only a few epochs of training, SN-Net effectively interpolates between the performance of anchors with varying scales. At runtime, SN-Net can instantly adapt to dynamic resource constraints by switching the stitching positions. Extensive experiments on ImageNet classification demonstrate that SN-Net can obtain on-par or even better performance than many individually trained networks while supporting diverse deployment scenarios. For example, by stitching Swin Transformers, we challenge hundreds of models in Timm model zoo with a single network. We believe this new elastic model framework can serve as a strong baseline for further research in wider communities. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_Stitchable_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pan_Stitchable_Neural_Networks_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.06586 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Stitchable_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Stitchable_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Collaborative Diffusion for Multi-Modal Face Generation and Editing | Ziqi Huang, Kelvin C.K. Chan, Yuming Jiang, Ziwei Liu | Diffusion models arise as a powerful generative tool recently. Despite the great progress, existing diffusion models mainly focus on uni-modal control, i.e., the diffusion process is driven by only one modality of condition. To further unleash the users' creativity, it is desirable for the model to be controllable by multiple modalities simultaneously, e.g., generating and editing faces by describing the age (text-driven) while drawing the face shape (mask-driven). In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. Our key insight is that diffusion models driven by different modalities are inherently complementary regarding the latent denoising steps, where bilateral connections can be established upon. Specifically, we propose dynamic diffuser, a meta-network that adaptively hallucinates multi-modal denoising steps by predicting the spatial-temporal influence functions for each pre-trained uni-modal model. Collaborative Diffusion not only collaborates generation capabilities from uni-modal diffusion models, but also integrates multiple uni-modal manipulations to perform multi-modal editing. Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in both image quality and condition consistency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Collaborative_Diffusion_for_Multi-Modal_Face_Generation_and_Editing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Collaborative_Diffusion_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10530 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Collaborative_Diffusion_for_Multi-Modal_Face_Generation_and_Editing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Collaborative_Diffusion_for_Multi-Modal_Face_Generation_and_Editing_CVPR_2023_paper.html | CVPR 2023 | null |
DejaVu: Conditional Regenerative Learning To Enhance Dense Prediction | Shubhankar Borse, Debasmit Das, Hyojin Park, Hong Cai, Risheek Garrepalli, Fatih Porikli | We present DejaVu, a novel framework which leverages conditional image regeneration as additional supervision during training to improve deep networks for dense prediction tasks such as segmentation, depth estimation, and surface normal prediction. First, we apply redaction to the input image, which removes certain structural information by sparse sampling or selective frequency removal. Next, we use a conditional regenerator, which takes the redacted image and the dense predictions as inputs, and reconstructs the original image by filling in the missing structural information. In the redacted image, structural attributes like boundaries are broken while semantic context is largely preserved. In order to make the regeneration feasible, the conditional generator will then require the structure information from the other input source, i.e., the dense predictions. As such, by including this conditional regeneration objective during training, DejaVu encourages the base network to learn to embed accurate scene structure in its dense prediction. This leads to more accurate predictions with clearer boundaries and better spatial consistency. When it is feasible to leverage additional computation, DejaVu can be extended to incorporate an attention-based regeneration module within the dense prediction network, which further improves accuracy. Through extensive experiments on multiple dense prediction benchmarks such as Cityscapes, COCO, ADE20K, NYUD-v2, and KITTI, we demonstrate the efficacy of employing DejaVu during training, as it outperforms SOTA methods at no added computation cost. | https://openaccess.thecvf.com/content/CVPR2023/papers/Borse_DejaVu_Conditional_Regenerative_Learning_To_Enhance_Dense_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Borse_DejaVu_Conditional_Regenerative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01573 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Borse_DejaVu_Conditional_Regenerative_Learning_To_Enhance_Dense_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Borse_DejaVu_Conditional_Regenerative_Learning_To_Enhance_Dense_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
MACARONS: Mapping and Coverage Anticipation With RGB Online Self-Supervision | Antoine Guédon, Tom Monnier, Pascal Monasse, Vincent Lepetit | We introduce a method that simultaneously learns to explore new large environments and to reconstruct them in 3D from color images only. This is closely related to the Next Best View problem (NBV), where one has to identify where to move the camera next to improve the coverage of an unknown scene. However, most of the current NBV methods rely on depth sensors, need 3D supervision and/or do not scale to large scenes. Our method requires only a color camera and no 3D supervision. It simultaneously learns in a self-supervised fashion to predict a volume occupancy field from color images and, from this field, to predict the NBV. Thanks to this approach, our method performs well on new scenes as it is not biased towards any training 3D data. We demonstrate this on a recent dataset made of various 3D scenes and show it performs even better than recent methods requiring a depth sensor, which is not a realistic assumption for outdoor scenes captured with a flying drone. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guedon_MACARONS_Mapping_and_Coverage_Anticipation_With_RGB_Online_Self-Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guedon_MACARONS_Mapping_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guedon_MACARONS_Mapping_and_Coverage_Anticipation_With_RGB_Online_Self-Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guedon_MACARONS_Mapping_and_Coverage_Anticipation_With_RGB_Online_Self-Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Audio-Visual Grouping Network for Sound Localization From Mixtures | Shentong Mo, Yapeng Tian | Sound source localization is a typical and challenging task that predicts the location of sound sources in a video. Previous single-source methods mainly used the audio-visual association as clues to localize sounding objects in each frame. Due to the mixed property of multiple sound sources in the original space, there exist rare multi-source approaches to localizing multiple sources simultaneously, except for one recent work using a contrastive random walk in the graph with images and separated sound as nodes. Despite their promising performance, they can only handle a fixed number of sources, and they cannot learn compact class-aware representations for individual sources. To alleviate this shortcoming, in this paper, we propose a novel audio-visual grouping network, namely AVGN, that can directly learn category-wise semantic features for each source from the input audio mixture and frame to localize multiple sources simultaneously. Specifically, our AVGN leverages learnable audio-visual class tokens to aggregate class-aware source features. Then, the aggregated semantic features for each source can be used as guidance to localize the corresponding visual regions. Compared to existing multi-source methods, our new framework can localize a flexible number of sources and disentangle category-aware audio-visual representations for individual sound sources. We conduct extensive experiments on MUSIC, VGGSound-Instruments, and VGG-Sound Sources benchmarks. The results demonstrate that the proposed AVGN can achieve state-of-the-art sounding object localization performance on both single-source and multi-source scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mo_Audio-Visual_Grouping_Network_for_Sound_Localization_From_Mixtures_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mo_Audio-Visual_Grouping_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17056 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mo_Audio-Visual_Grouping_Network_for_Sound_Localization_From_Mixtures_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mo_Audio-Visual_Grouping_Network_for_Sound_Localization_From_Mixtures_CVPR_2023_paper.html | CVPR 2023 | null |
Fair Federated Medical Image Segmentation via Client Contribution Estimation | Meirui Jiang, Holger R. Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, Ziyue Xu | How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Fair_Federated_Medical_Image_Segmentation_via_Client_Contribution_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_Fair_Federated_Medical_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16520 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Fair_Federated_Medical_Image_Segmentation_via_Client_Contribution_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Fair_Federated_Medical_Image_Segmentation_via_Client_Contribution_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Generative Targeted Attacks With Pattern Injection | Weiwei Feng, Nanqing Xu, Tianzhu Zhang, Yongdong Zhang | Adversarial attacks can evaluate model robustness and have been of great concerns in recent years. Among various attacks, targeted attacks aim at misleading victim models to output adversary-desired predictions, which are more challenging and threatening than untargeted ones. Existing targeted attacks can be roughly divided into instancespecific and instance-agnostic attacks. Instance-specific attacks craft adversarial examples via iterative gradient updating on the specific instance. In contrast, instanceagnostic attacks learn a universal perturbation or a generative model on the global dataset to perform attacks. However they rely too much on the classification boundary of substitute models, ignoring the realistic distribution of target class, which may result in limited targeted attack performance. And there is no attempt to simultaneously combine the information of the specific instance and the global dataset. To deal with these limitations, we first conduct an analysis via a causal graph and propose to craft transferable targeted adversarial examples by injecting target patterns. Based on this analysis, we introduce a generative attack model composed of a cross-attention guided convolution module and a pattern injection module. Concretely, the former adopts a dynamic convolution kernel and a static convolution kernel for the specific instance and the global dataset, respectively, which can inherit the advantages of both instance-specific and instance-agnostic attacks. And the pattern injection module utilizes a pattern prototype to encode target patterns, which can guide the generation of targeted adversarial examples. Besides, we also provide rigorous theoretical analysis to guarantee the effectiveness of our method. Extensive experiments demonstrate that our method show superior performance than 10 existing adversarial attacks against 13 models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Dynamic_Generative_Targeted_Attacks_With_Pattern_Injection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Dynamic_Generative_Targeted_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Dynamic_Generative_Targeted_Attacks_With_Pattern_Injection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Dynamic_Generative_Targeted_Attacks_With_Pattern_Injection_CVPR_2023_paper.html | CVPR 2023 | null |
Tracking Multiple Deformable Objects in Egocentric Videos | Mingzhen Huang, Xiaoxing Li, Jun Hu, Honghong Peng, Siwei Lyu | Most existing multiple object tracking (MOT) methods that solely rely on appearance features struggle in tracking highly deformable objects. Other MOT methods that use motion clues to associate identities across frames have difficulty handling egocentric videos effectively or efficiently. In this work, we propose DETracker, a new MOT method that jointly detects and tracks deformable objects in egocentric videos. DETracker uses three novel modules, namely the motion disentanglement network (MDN), the patch association network (PAN) and the patch memory network (PMN), to explicitly tackle the difficulties caused by severe ego motion and fast morphing target objects. DETracker is end-to-end trainable and achieves near real-time speed. We also present DogThruGlasses, a large-scale deformable multi-object tracking dataset, with 150 videos and 73K annotated frames, collected by smart glasses. DETracker outperforms existing state-of-the-art method on the DogThruGlasses dataset and YouTube-Hand dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Tracking_Multiple_Deformable_Objects_in_Egocentric_Videos_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Tracking_Multiple_Deformable_Objects_in_Egocentric_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Tracking_Multiple_Deformable_Objects_in_Egocentric_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Recognition by Request | Chufeng Tang, Lingxi Xie, Xiaopeng Zhang, Xiaolin Hu, Qi Tian | Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establish a new paradigm named visual recognition by request (ViRReq) to bridge the gap. The key lies in decomposing visual recognition into atomic tasks named requests and leveraging a knowledge base, a hierarchical and text-based dictionary, to assist task definition. ViRReq allows for (i) learning complicated whole-part hierarchies from highly incomplete annotations and (ii) inserting new concepts with minimal efforts. We also establish a solid baseline by integrating language-driven recognition into recent semantic and instance segmentation methods, and demonstrate its flexible recognition ability on CPP and ADE20K, two datasets with hierarchical whole-part annotations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Visual_Recognition_by_Request_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Visual_Recognition_by_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.14227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Visual_Recognition_by_Request_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Visual_Recognition_by_Request_CVPR_2023_paper.html | CVPR 2023 | null |
SmartBrush: Text and Shape Guided Object Inpainting With Diffusion Model | Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang | Generic image inpainting aims to complete a corrupted image by borrowing surrounding information, which barely generates novel content. By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, e.g., a text prompt can be used to describe an object with richer attributes, and a mask can be used to constrain the shape of the inpainted object rather than being only considered as a missing area. We propose a new diffusion-based model named SmartBrush for completing a missing region with an object using both text and shape-guidance. While previous work such as DALLE-2 and Stable Diffusion can do text-guided inapinting they do not support shape guidance and tend to modify background texture surrounding the generated object. Our model incorporates both text and shape guidance with precision control. To preserve the background better, we propose a novel training and sampling strategy by augmenting the diffusion U-net with object-mask prediction. Lastly, we introduce a multi-task training strategy by jointly training inpainting with text-to-image generation to leverage more training data. We conduct extensive experiments showing that our model outperforms all baselines in terms of visual quality, mask controllability, and background preservation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_SmartBrush_Text_and_Shape_Guided_Object_Inpainting_With_Diffusion_Model_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.05034 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_SmartBrush_Text_and_Shape_Guided_Object_Inpainting_With_Diffusion_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_SmartBrush_Text_and_Shape_Guided_Object_Inpainting_With_Diffusion_Model_CVPR_2023_paper.html | CVPR 2023 | null |
REC-MV: REconstructing 3D Dynamic Cloth From Monocular Videos | Lingteng Qiu, Guanying Chen, Jiapeng Zhou, Mutian Xu, Junle Wang, Xiaoguang Han | Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these methods cannot separate the garment surface from the body. Moreover, despite existing garment reconstruction methods based on feature curve representation demonstrating impressive results for garment reconstruction from a single image, they struggle to generate temporally consistent surfaces for the video input. To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video. We introduce a novel approach, called REC-MV to jointly optimize the explicit feature curves and the implicit signed distance field (SDF) of the garments. Then the open garment meshes can be extracted via garment template registration in the canonical space. Experiments on multiple casually captured datasets show that our approach outperforms existing methods and can produce high-quality dynamic garment surfaces. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qiu_REC-MV_REconstructing_3D_Dynamic_Cloth_From_Monocular_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qiu_REC-MV_REconstructing_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_REC-MV_REconstructing_3D_Dynamic_Cloth_From_Monocular_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qiu_REC-MV_REconstructing_3D_Dynamic_Cloth_From_Monocular_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
JRDB-Pose: A Large-Scale Dataset for Multi-Person Pose Estimation and Tracking | Edward Vendrow, Duy Tho Le, Jianfei Cai, Hamid Rezatofighi | Autonomous robotic systems operating in human environments must understand their surroundings to make accurate and safe decisions. In crowded human scenes with close-up human-robot interaction and robot navigation, a deep understanding of surrounding people requires reasoning about human motion and body dynamics over time with human body pose estimation and tracking. However, existing datasets captured from robot platforms either do not provide pose annotations or do not reflect the scene distribution of social robots. In this paper, we introduce JRDB-Pose, a large-scale dataset and benchmark for multi-person pose estimation and tracking. JRDB-Pose extends the existing JRDB which includes videos captured from a social navigation robot in a university campus environment, containing challenging scenes with crowded indoor and outdoor locations and a diverse range of scales and occlusion types. JRDB-Pose provides human pose annotations with per-keypoint occlusion labels and track IDs consistent across the scene and with existing annotations in JRDB. We conduct a thorough experimental study of state-of-the-art multi-person pose estimation and tracking methods on JRDB-Pose, showing that our dataset imposes new challenges for the existing methods. JRDB-Pose is available at https://jrdb.erc.monash.edu/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Vendrow_JRDB-Pose_A_Large-Scale_Dataset_for_Multi-Person_Pose_Estimation_and_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vendrow_JRDB-Pose_A_Large-Scale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vendrow_JRDB-Pose_A_Large-Scale_Dataset_for_Multi-Person_Pose_Estimation_and_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vendrow_JRDB-Pose_A_Large-Scale_Dataset_for_Multi-Person_Pose_Estimation_and_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection | Yipeng Gao, Kun-Yu Lin, Junkai Yan, Yaowei Wang, Wei-Shi Zheng | In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target labeled images are available for training in addition to sufficient source labeled images. Critically, in FSDAOD, the data-scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves for augmenting the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is available at https://github.com/Hlings/AsyFOD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_AsyFOD_An_Asymmetric_Adaptation_Paradigm_for_Few-Shot_Domain_Adaptive_Object_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_AsyFOD_An_Asymmetric_Adaptation_Paradigm_for_Few-Shot_Domain_Adaptive_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_AsyFOD_An_Asymmetric_Adaptation_Paradigm_for_Few-Shot_Domain_Adaptive_Object_CVPR_2023_paper.html | CVPR 2023 | null |
RUST: Latent Neural Scene Representations From Unposed Imagery | Mehdi S. M. Sajjadi, Aravindh Mahendran, Thomas Kipf, Etienne Pot, Daniel Duckworth, Mario Lučić, Klaus Greff | Inferring the structure of 3D scenes from 2D observations is a fundamental challenge in computer vision. Recently popularized approaches based on neural scene representations have achieved tremendous impact and have been applied across a variety of applications. One of the major remaining challenges in this space is training a single model which can provide latent representations which effectively generalize beyond a single scene. Scene Representation Transformer (SRT) has shown promise in this direction, but scaling it to a larger set of diverse scenes is challenging and necessitates accurately posed ground truth data. To address this problem, we propose RUST (Really Unposed Scene representation Transformer), a pose-free approach to novel view synthesis trained on RGB images alone. Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis. We perform an empirical investigation into the learned latent pose structure and show that it allows meaningful test-time camera transformations and accurate explicit pose readouts. Perhaps surprisingly, RUST achieves similar quality as methods which have access to perfect camera pose, thereby unlocking the potential for large-scale training of amortized neural scene representations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sajjadi_RUST_Latent_Neural_Scene_Representations_From_Unposed_Imagery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sajjadi_RUST_Latent_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14306 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sajjadi_RUST_Latent_Neural_Scene_Representations_From_Unposed_Imagery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sajjadi_RUST_Latent_Neural_Scene_Representations_From_Unposed_Imagery_CVPR_2023_paper.html | CVPR 2023 | null |
PointCert: Point Cloud Classification With Deterministic Certified Robustness Guarantees | Jinghuai Zhang, Jinyuan Jia, Hongbin Liu, Neil Zhenqiang Gong | Point cloud classification is an essential component in many security-critical applications such as autonomous driving and augmented reality. However, point cloud classifiers are vulnerable to adversarially perturbed point clouds. Existing certified defenses against adversarial point clouds suffer from a key limitation: their certified robustness guarantees are probabilistic, i.e., they produce an incorrect certified robustness guarantee with some probability. In this work, we propose a general framework, namely PointCert, that can transform an arbitrary point cloud classifier to be certifiably robust against adversarial point clouds with deterministic guarantees. PointCert certifiably predicts the same label for a point cloud when the number of arbitrarily added, deleted, and/or modified points is less than a threshold. Moreover, we propose multiple methods to optimize the certified robustness guarantees of PointCert in three application scenarios. We systematically evaluate PointCert on ModelNet and ScanObjectNN benchmark datasets. Our results show that PointCert substantially outperforms state-of-the-art certified defenses even though their robustness guarantees are probabilistic. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_PointCert_Point_Cloud_Classification_With_Deterministic_Certified_Robustness_Guarantees_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_PointCert_Point_Cloud_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01959 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PointCert_Point_Cloud_Classification_With_Deterministic_Certified_Robustness_Guarantees_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PointCert_Point_Cloud_Classification_With_Deterministic_Certified_Robustness_Guarantees_CVPR_2023_paper.html | CVPR 2023 | null |
Open Set Action Recognition via Multi-Label Evidential Learning | Chen Zhao, Dawei Du, Anthony Hoogs, Christopher Funk | Existing methods for open set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world. We propose a new method for open set action recognition and novelty detection via MUlti-Label Evidential learning (MULE), that goes beyond previous novel action detection methods by addressing the more general problems of single or multiple actors in the same scene, with simultaneous action(s) by any actor. Our Beta Evidential Neural Network estimates multi-action uncertainty with Beta densities based on actor-context-object relation representations. An evidence debiasing constraint is added to the objective func- tion for optimization to reduce the static bias of video representations, which can incorrectly correlate predictions and static cues. We develop a primal-dual average scheme update-based learning algorithm to optimize the proposed problem and provide corresponding theoretical analysis. Besides, uncertainty and belief-based novelty estimation mechanisms are formulated to detect novel actions. Extensive experiments on two real-world video datasets show that our proposed approach achieves promising performance in single/multi-actor, single/multi-action settings. Our code and models are released at https://github.com/charliezhaoyinpeng/mule. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Open_Set_Action_Recognition_via_Multi-Label_Evidential_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Open_Set_Action_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12698 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Open_Set_Action_Recognition_via_Multi-Label_Evidential_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Open_Set_Action_Recognition_via_Multi-Label_Evidential_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-Training Model | Yatai Ji, Junjie Wang, Yuan Gong, Lin Zhang, Yanru Zhu, Hongfa Wang, Jiaxing Zhang, Tetsuya Sakai, Yujiu Yang | Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the exiting deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_MAP_Multimodal_Uncertainty-Aware_Vision-Language_Pre-Training_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ji_MAP_Multimodal_Uncertainty-Aware_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.05335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_MAP_Multimodal_Uncertainty-Aware_Vision-Language_Pre-Training_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_MAP_Multimodal_Uncertainty-Aware_Vision-Language_Pre-Training_Model_CVPR_2023_paper.html | CVPR 2023 | null |
DualRel: Semi-Supervised Mitochondria Segmentation From a Prototype Perspective | Huayu Mai, Rui Sun, Tianzhu Zhang, Zhiwei Xiong, Feng Wu | Automatic mitochondria segmentation enjoys great popularity with the development of deep learning. However, existing methods rely heavily on the labor-intensive manual gathering by experienced domain experts. And naively applying semi-supervised segmentation methods in the natural image field to mitigate the labeling cost is undesirable. In this work, we analyze the gap between mitochondrial images and natural images and rethink how to achieve effective semi-supervised mitochondria segmentation, from the perspective of reliable prototype-level supervision. We propose a novel end-to-end dual-reliable (DualRel) network, including a reliable pixel aggregation module and a reliable prototype selection module. The proposed DualRel enjoys several merits. First, to learn the prototypes well without any explicit supervision, we carefully design the referential correlation to rectify the direct pairwise correlation. Second, the reliable prototype selection module is responsible for further evaluating the reliability of prototypes in constructing prototype-level consistency regularization. Extensive experimental results on three challenging benchmarks demonstrate that our method performs favorably against state-of-the-art semi-supervised segmentation methods. Importantly, with extremely few samples used for training, DualRel is also on par with current state-of-the-art fully supervised methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mai_DualRel_Semi-Supervised_Mitochondria_Segmentation_From_a_Prototype_Perspective_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mai_DualRel_Semi-Supervised_Mitochondria_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mai_DualRel_Semi-Supervised_Mitochondria_Segmentation_From_a_Prototype_Perspective_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mai_DualRel_Semi-Supervised_Mitochondria_Segmentation_From_a_Prototype_Perspective_CVPR_2023_paper.html | CVPR 2023 | null |
Federated Learning With Data-Agnostic Distribution Fusion | Jian-hui Duan, Wenzhong Li, Derun Zou, Ruichen Li, Sanglu Lu | Federated learning has emerged as a promising distributed machine learning paradigm to preserve data privacy. One of the fundamental challenges of federated learning is that data samples across clients are usually not independent and identically distributed (non-IID), leading to slow convergence and severe performance drop of the aggregated global model. To facilitate model aggregation on non-IID data, it is desirable to infer the unknown global distributions without violating privacy protection policy. In this paper, we propose a novel data-agnostic distribution fusion based model aggregation method called FedFusion to optimize federated learning with non-IID local datasets, based on which the heterogeneous clients' data distributions can be represented by a global distribution of several virtual fusion components with different parameters and weights. We develop a Variational AutoEncoder (VAE) method to learn the optimal parameters of the distribution fusion components based on limited statistical information extracted from the local models, and apply the derived distribution fusion model to optimize federated model aggregation with non-IID data. Extensive experiments based on various federated learning scenarios with real-world datasets show that FedFusion achieves significant performance improvement compared to the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Duan_Federated_Learning_With_Data-Agnostic_Distribution_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Duan_Federated_Learning_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Duan_Federated_Learning_With_Data-Agnostic_Distribution_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Duan_Federated_Learning_With_Data-Agnostic_Distribution_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval? | Wenhao Wu, Haipeng Luo, Bo Fang, Jingdong Wang, Wanli Ouyang | Most existing text-video retrieval methods focus on cross-modal matching between the visual content of videos and textual query sentences. However, in real-world scenarios, online videos are often accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This insight has motivated us to propose a novel approach to text-video retrieval, where we directly generate associated captions from videos using zero-shot video captioning with knowledge from web-scale pre-trained models (e.g., CLIP and GPT-2). Given the generated captions, a natural question arises: what benefits do they bring to text-video retrieval? To answer this, we introduce Cap4Video, a new framework that leverages captions in three ways: i) Input data: video-caption pairs can augment the training data. ii) Intermediate feature interaction: we perform cross-modal feature interaction between the video and caption to produce enhanced video representations. iii) Output score: the Query-Caption matching branch can complement the original Query-Video matching branch for text-video retrieval. We conduct comprehensive ablation studies to demonstrate the effectiveness of our approach. Without any post-processing, Cap4Video achieves state-of-the-art performance on four standard text-video retrieval benchmarks: MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%). The code is available at https://github.com/whwu95/Cap4Video. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Cap4Video_What_Can_Auxiliary_Captions_Do_for_Text-Video_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Cap4Video_What_Can_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.00184 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Cap4Video_What_Can_Auxiliary_Captions_Do_for_Text-Video_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Cap4Video_What_Can_Auxiliary_Captions_Do_for_Text-Video_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning | Man Liu, Feng Li, Chunjie Zhang, Yunchao Wei, Huihui Bai, Yao Zhao | Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information. Prior works mainly localize regions corresponding to the sharing attributes. When various visual appearances correspond to the same attribute, the sharing attributes inevitably introduce semantic ambiguity, hampering the exploration of accurate semantic-visual interactions. In this paper, we deploy the dual semantic-visual transformer module (DSVTM) to progressively model the correspondences between attribute prototypes and visual features, constituting a progressive semantic-visual mutual adaption (PSVMA) network for semantic disambiguation and knowledge transferability improvement. Specifically, DSVTM devises an instance-motivated semantic encoder that learns instance-centric prototypes to adapt to different images, enabling the recast of the unmatched semantic-visual pair into the matched one. Then, a semantic-motivated instance decoder strengthens accurate cross-domain interactions between the matched pair for semantic-related instance adaption, encouraging the generation of unambiguous visual representations. Moreover, to mitigate the bias towards seen classes in GZSL, a debiasing loss is proposed to pursue response consistency between seen and unseen predictions. The PSVMA consistently yields superior performances against other state-of-the-art methods. Code will be available at: https://github.com/ManLiuCoder/PSVMA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Progressive_Semantic-Visual_Mutual_Adaption_for_Generalized_Zero-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Progressive_Semantic-Visual_Mutual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15322 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Progressive_Semantic-Visual_Mutual_Adaption_for_Generalized_Zero-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Progressive_Semantic-Visual_Mutual_Adaption_for_Generalized_Zero-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement | Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan | Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validate our approach and sets a new state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. Our codes and models are available at https://github.com/nanmehta/GMTNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mehta_Gated_Multi-Resolution_Transfer_Network_for_Burst_Restoration_and_Enhancement_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mehta_Gated_Multi-Resolution_Transfer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06703 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mehta_Gated_Multi-Resolution_Transfer_Network_for_Burst_Restoration_and_Enhancement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mehta_Gated_Multi-Resolution_Transfer_Network_for_Burst_Restoration_and_Enhancement_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles | Shuquan Ye, Yujia Xie, Dongdong Chen, Yichong Xu, Lu Yuan, Chenguang Zhu, Jing Liao | This paper focuses on analyzing and improving the commonsense ability of recent popular vision-language (VL) models. Despite the great success, we observe that existing VL-models still lack commonsense knowledge/reasoning ability (e.g., "Lemons are sour"), which is a vital component towards artificial general intelligence. Through our analysis, we find one important reason is that existing large-scale VL datasets do not contain much commonsense knowledge, which motivates us to improve the commonsense of VL-models from the data perspective. Rather than collecting a new VL training dataset, we propose a more scalable strategy, i.e., "Data Augmentation with kNowledge graph linearization for CommonsensE capability" (DANCE). It can be viewed as one type of data augmentation technique, which can inject commonsense knowledge into existing VL datasets on the fly during training. More specifically, we leverage the commonsense knowledge graph (e.g., ConceptNet) and create variants of text description in VL datasets via bidirectional sub-graph sequentialization. For better commonsense evaluation, we further propose the first retrieval-based commonsense diagnostic benchmark. By conducting extensive experiments on some representative VL-models, we demonstrate that our DANCE technique is able to significantly improve the commonsense ability while maintaining the performance on vanilla retrieval tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_Improving_Commonsense_in_Vision-Language_Models_via_Knowledge_Graph_Riddles_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_Improving_Commonsense_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16504 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Improving_Commonsense_in_Vision-Language_Models_via_Knowledge_Graph_Riddles_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Improving_Commonsense_in_Vision-Language_Models_via_Knowledge_Graph_Riddles_CVPR_2023_paper.html | CVPR 2023 | null |
S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning | Wei Suo, Mengyang Sun, Weisong Liu, Yiqi Gao, Peng Wang, Yanning Zhang, Qi Wu | VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S3C), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the S3C can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Suo_S3C_Semi-Supervised_VQA_Natural_Language_Explanation_via_Self-Critical_Learning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Suo_S3C_Semi-Supervised_VQA_Natural_Language_Explanation_via_Self-Critical_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Suo_S3C_Semi-Supervised_VQA_Natural_Language_Explanation_via_Self-Critical_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Spatio-Focal Bidirectional Disparity Estimation From a Dual-Pixel Image | Donggun Kim, Hyeonjoong Jang, Inchul Kim, Min H. Kim | Dual-pixel photography is monocular RGB-D photography with an ultra-high resolution, enabling many applications in computational photography. However, there are still several challenges to fully utilizing dual-pixel photography. Unlike the conventional stereo pair, the dual pixel exhibits a bidirectional disparity that includes positive and negative values, depending on the focus plane depth in an image. Furthermore, capturing a wide range of dual-pixel disparity requires a shallow depth of field, resulting in a severely blurred image, degrading depth estimation performance. Recently, several data-driven approaches have been proposed to mitigate these two challenges. However, due to the lack of the ground-truth dataset of the dual-pixel disparity, existing data-driven methods estimate either inverse depth or blurriness map. In this work, we propose a self-supervised learning method that learns bidirectional disparity by utilizing the nature of anisotropic blur kernels in dual-pixel photography. We observe that the dual-pixel left/right images have reflective-symmetric anisotropic kernels, so their sum is equivalent to that of a conventional image. We take a self-supervised training approach with the novel kernel-split symmetry loss accounting for the phenomenon. Our method does not rely on a training dataset of dual-pixel disparity that does not exist yet. Our method can estimate a complete disparity map with respect to the focus-plane depth from a dual-pixel image, outperforming the baseline dual-pixel methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Spatio-Focal_Bidirectional_Disparity_Estimation_From_a_Dual-Pixel_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Spatio-Focal_Bidirectional_Disparity_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Spatio-Focal_Bidirectional_Disparity_Estimation_From_a_Dual-Pixel_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Spatio-Focal_Bidirectional_Disparity_Estimation_From_a_Dual-Pixel_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Block Selection Method for Using Feature Norm in Out-of-Distribution Detection | Yeonguk Yu, Sungho Shin, Seongju Lee, Changhyun Jun, Kyoobin Lee | Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of a network derived from the highly activated feature map. In this study, we first revealed that a norm of the feature map obtained from the other block than the last block can be a better indicator of OOD detection. Motivated by this, we propose a simple framework consisting of FeatureNorm: a norm of the feature map and NormRatio: a ratio of FeatureNorm for ID and OOD to measure the OOD detection performance of each block. In particular, to select the block that provides the largest difference between FeatureNorm of ID and FeatureNorm of OOD, we create jigsaw puzzles as pseudo OOD from ID training samples and calculate NormRatio, and the block with the largest value is selected. After the suitable block is selected, OOD detection with the FeatureNorm outperforms other OOD detection methods by reducing FPR95 by up to 52.77% on CIFAR10 benchmark and by up to 48.53% on ImageNet benchmark. We demonstrate that our framework can generalize to various architectures and the importance of block selection, which can improve previous OOD detection methods as well. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Block_Selection_Method_for_Using_Feature_Norm_in_Out-of-Distribution_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Block_Selection_Method_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02295 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Block_Selection_Method_for_Using_Feature_Norm_in_Out-of-Distribution_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Block_Selection_Method_for_Using_Feature_Norm_in_Out-of-Distribution_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
PIDNet: A Real-Time Semantic Segmentation Network Inspired by PID Controllers | Jiacong Xu, Zixiang Xiong, Shankar P. Bhattacharyya | Two-branch network architecture has shown its efficiency and effectiveness in real-time semantic segmentation tasks. However, direct fusion of high-resolution details and low-frequency context has the drawback of detailed features being easily overwhelmed by surrounding contextual information. This overshoot phenomenon limits the improvement of the segmentation accuracy of existing two-branch models. In this paper, we make a connection between Convolutional Neural Networks (CNN) and Proportional-Integral-Derivative (PID) controllers and reveal that a two-branch network is equivalent to a Proportional-Integral (PI) controller, which inherently suffers from similar overshoot issues. To alleviate this problem, we propose a novel three-branch network architecture: PIDNet, which contains three branches to parse detailed, context and boundary information, respectively, and employs boundary attention to guide the fusion of detailed and context branches. Our family of PIDNets achieve the best trade-off between inference speed and accuracy and their accuracy surpasses all the existing models with similar inference speed on the Cityscapes and CamVid datasets. Specifically, PIDNet-S achieves 78.6 mIOU with inference speed of 93.2 FPS on Cityscapes and 80.1 mIOU with speed of 153.7 FPS on CamVid. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_PIDNet_A_Real-Time_Semantic_Segmentation_Network_Inspired_by_PID_Controllers_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2206.02066 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_PIDNet_A_Real-Time_Semantic_Segmentation_Network_Inspired_by_PID_Controllers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_PIDNet_A_Real-Time_Semantic_Segmentation_Network_Inspired_by_PID_Controllers_CVPR_2023_paper.html | CVPR 2023 | null |
Four-View Geometry With Unknown Radial Distortion | Petr Hruby, Viktor Korotynskiy, Timothy Duff, Luke Oeding, Marc Pollefeys, Tomas Pajdla, Viktor Larsson | We present novel solutions to previously unsolved problems of relative pose estimation from images whose calibration parameters, namely focal lengths and radial distortion, are unknown. Our approach enables metric reconstruction without modeling these parameters. The minimal case for reconstruction requires 13 points in 4 views for both the calibrated and uncalibrated cameras. We describe and implement the first solution to these minimal problems. In the calibrated case, this may be modeled as a polynomial system of equations with 3584 solutions. Despite the apparent intractability, the problem decomposes spectacularly. Each solution falls into a Euclidean symmetry class of size 16, and we can estimate 224 class representatives by solving a sequence of three subproblems with 28, 2, and 4 solutions. We highlight the relationship between internal constraints on the radial quadrifocal tensor and the relations among the principal minors of a 4x4 matrix. We also address the case of 4 upright cameras, where 7 points are minimal. Finally, we evaluate our approach on simulated and real data and benchmark against previous calibration-free solutions, and show that our method provides an efficient startup for an SfM pipeline with radial cameras. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hruby_Four-View_Geometry_With_Unknown_Radial_Distortion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hruby_Four-View_Geometry_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hruby_Four-View_Geometry_With_Unknown_Radial_Distortion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hruby_Four-View_Geometry_With_Unknown_Radial_Distortion_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Optical Flow From Geometric Matching Consistent Perspective | Qiaole Dong, Chenjie Cao, Yanwei Fu | Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning based optical flow models have achieved considerable success. However, these models often train networks from the scratch on standard optical flow data, which restricts their ability to robustly and geometrically match image features. In this paper, we propose a rethinking to previous optical flow estimation. We particularly leverage Geometric Image Matching (GIM) as a pre-training task for the optical flow estimation (MatchFlow) with better feature representations, as GIM shares some common challenges as optical flow estimation, and with massive labeled real-world data. Thus, matching static scenes helps to learn more fundamental feature correlations of objects and scenes with consistent displacements. Specifically, the proposed MatchFlow model employs a QuadTree attention-based network pre-trained on MegaDepth to extract coarse features for further flow regression. Extensive experiments show that our model has great cross-dataset generalization. Our method achieves 11.5% and 10.1% error reduction from GMA on Sintel clean pass and KITTI test set. At the time of anonymous submission, our MatchFlow(G) enjoys state-of-theart performance on Sintel clean and final pass compared to published approaches with comparable computation and memory footprint. Codes and models will be released in https://github.com/DQiaole/MatchFlow. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Rethinking_Optical_Flow_From_Geometric_Matching_Consistent_Perspective_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Rethinking_Optical_Flow_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08384 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Rethinking_Optical_Flow_From_Geometric_Matching_Consistent_Perspective_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Rethinking_Optical_Flow_From_Geometric_Matching_Consistent_Perspective_CVPR_2023_paper.html | CVPR 2023 | null |
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning | Qiang He, Huangyuan Su, Jieyu Zhang, Xinwen Hou | Deep reinforcement learning (DRL) gives the promise that an agent learns good policy from high-dimensional information, whereas representation learning removes irrelevant and redundant information and retains pertinent information. In this work, we demonstrate that the learned representation of the Q-network and its target Q-network should, in theory, satisfy a favorable distinguishable representation property. Specifically, there exists an upper bound on the representation similarity of the value functions of two adjacent time steps in a typical DRL setting. However, through illustrative experiments, we show that the learned DRL agent may violate this property and lead to a sub-optimal policy. Therefore, we propose a simple yet effective regularizer called Policy Evaluation with Easy Regularization on Representation (PEER), which aims to maintain the distinguishable representation property via explicit regularization on internal representations. And we provide the convergence rate guarantee of PEER. Implementing PEER requires only one line of code. Our experiments demonstrate that incorporating PEER into DRL can significantly improve performance and sample efficiency. Comprehensive experiments show that PEER achieves state-of-the-art performance on all 4 environments on PyBullet, 9 out of 12 tasks on DMControl, and 19 out of 26 games on Atari. To the best of our knowledge, PEER is the first work to study the inherent representation property of Q-network and its target. Our code is available at https://sites.google.com/view/peer-cvpr2023/. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Frustratingly_Easy_Regularization_on_Representation_Can_Boost_Deep_Reinforcement_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Frustratingly_Easy_Regularization_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2205.14557 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Frustratingly_Easy_Regularization_on_Representation_Can_Boost_Deep_Reinforcement_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Frustratingly_Easy_Regularization_on_Representation_Can_Boost_Deep_Reinforcement_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection | Linfeng Zhang, Runpei Dong, Hung-Shuo Tai, Kaisheng Ma | The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, these applications usually have an urgent requirement for not only accurate but also efficient 3D object detection. Recently, knowledge distillation has been proposed as an effective model compression technique, which transfers the knowledge from an over-parameterized teacher to a lightweight student and achieves consistent effectiveness in 2D vision. However, due to point clouds' sparsity and irregularity, directly applying previous image-based knowledge distillation methods to point cloud detectors usually leads to unsatisfactory performance. To fill the gap, this paper proposes PointDistiller, a structured knowledge distillation framework for point clouds-based 3D detection. Concretely, PointDistiller includes local distillation which extracts and distills the local geometric structure of point clouds with dynamic graph convolution and reweighted learning strategy, which highlights student learning on the critical points or voxels to improve knowledge distillation efficiency. Extensive experiments on both voxels-based and raw points-based detectors have demonstrated the effectiveness of our method over seven previous knowledge distillation methods. For instance, our 4X compressed PointPillars student achieves 2.8 and 3.4 mAP improvements on BEV and 3D object detection, outperforming its teacher by 0.9 and 1.8 mAP, respectively. Codes are available in the supplementary material and will be released on Github. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_PointDistiller_Structured_Knowledge_Distillation_Towards_Efficient_and_Compact_3D_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_PointDistiller_Structured_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.11098 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PointDistiller_Structured_Knowledge_Distillation_Towards_Efficient_and_Compact_3D_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PointDistiller_Structured_Knowledge_Distillation_Towards_Efficient_and_Compact_3D_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Optical Expansion From Scale Matching | Han Ling, Yinghui Sun, Quansen Sun, Zhenwen Ren | This paper address the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3D vision tasks. Previous methods estimate optical expansion mainly from optical flow results, but this two-stage architecture makes their results limited by the accuracy of optical flow and less robust. To solve these problems, we propose the concept of 3D optical flow by integrating optical expansion into the 2D optical flow, which is implemented by a plug-and-play module, namely TPCV. TPCV implements matching features at the correct location and scale, thus allowing the simultaneous optimization of optical flow and optical expansion tasks. Experimentally, we apply TPCV to the RAFT optical flow baseline. Experimental results show that the baseline optical flow performance is substantially improved. Moreover, we apply the optical flow and optical expansion results to various dynamic 3D vision tasks, including motion-in-depth, time-to-collision, and scene flow, often achieving significant improvement over the prior SOTA. Code will be available at https://github.com/HanLingsgjk/TPCV. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ling_Learning_Optical_Expansion_From_Scale_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ling_Learning_Optical_Expansion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ling_Learning_Optical_Expansion_From_Scale_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ling_Learning_Optical_Expansion_From_Scale_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
LEMaRT: Label-Efficient Masked Region Transform for Image Harmonization | Sheng Liu, Cong Phuoc Huynh, Cong Chen, Maxim Arap, Raffay Hamid | We present a simple yet effective self-supervised pretraining method for image harmonization which can leverage large-scale unannotated image datasets. To achieve this goal, we first generate pre-training data online with our Label-Efficient Masked Region Transform (LEMaRT) pipeline. Given an image, LEMaRT generates a foreground mask and then applies a set of transformations to perturb various visual attributes, e.g., defocus blur, contrast, saturation, of the region specified by the generated mask. We then pre-train image harmonization models by recovering the original image from the perturbed image. Secondly, we introduce an image harmonization model, namely SwinIH, by retrofitting the Swin Transformer [27] with a combination of local and global self-attention mechanisms. Pretraining SwinIH with LEMaRT results in a new state of the art for image harmonization, while being label-efficient, i.e., consuming less annotated data for fine-tuning than existing methods. Notably, on iHarmony4 dataset [8], SwinIH outperforms the state of the art, i.e., SCS-Co [16] by a margin of 0.4 dB when it is fine-tuned on only 50% of the training data, and by 1.0 dB when it is trained on the full training dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_LEMaRT_Label-Efficient_Masked_Region_Transform_for_Image_Harmonization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_LEMaRT_Label-Efficient_Masked_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.13166 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_LEMaRT_Label-Efficient_Masked_Region_Transform_for_Image_Harmonization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_LEMaRT_Label-Efficient_Masked_Region_Transform_for_Image_Harmonization_CVPR_2023_paper.html | CVPR 2023 | null |
How To Prevent the Poor Performance Clients for Personalized Federated Learning? | Zhe Qu, Xingyu Li, Xiao Han, Rui Duan, Chengchao Shen, Lixing Chen | Personalized federated learning (pFL) collaboratively trains personalized models, which provides a customized model solution for individual clients in the presence of heterogeneous distributed local data. Although many recent studies have applied various algorithms to enhance personalization in pFL, they mainly focus on improving the performance from averaging or top perspective. However, part of the clients may fall into poor performance and are not clearly discussed. Therefore, how to prevent these poor clients should be considered critically. Intuitively, these poor clients may come from biased universal information shared with others. To address this issue, we propose a novel pFL strategy, called Personalize Locally, Generalize Universally (PLGU). PLGU generalizes the fine-grained universal information and moderates its biased performance by designing a Layer-Wised Sharpness Aware Minimization (LWSAM) algorithm while keeping the personalization local. Specifically, we embed our proposed PLGU strategy into two pFL schemes concluded in this paper: with/without a global model, and present the training procedures in detail. Through in-depth study, we show that the proposed PLGU strategy achieves competitive generalization bounds on both considered pFL schemes. Our extensive experimental results show that all the proposed PLGU based-algorithms achieve state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qu_How_To_Prevent_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.html | CVPR 2023 | null |
TopDiG: Class-Agnostic Topological Directional Graph Extraction From Remote Sensing Images | Bingnan Yang, Mi Zhang, Zhan Zhang, Zhili Zhang, Xiangyun Hu | Rapid development in automatic vector extraction from remote sensing images has been witnessed in recent years. However, the vast majority of existing works concentrate on a specific target, fragile to category variety, and hardly achieve stable performance crossing different categories. In this work, we propose an innovative class-agnostic model, namely TopDiG, to directly extract topological directional graphs from remote sensing images and solve these issues. Firstly, TopDiG employs a topology-concentrated node detector (TCND) to detect nodes and obtain compact perception of topological components. Secondly, we propose a dynamic graph supervision (DGS) strategy to dynamically generate adjacency graph labels from unordered nodes. Finally, the directional graph (DiG) generator module is designed to construct topological directional graphs from predicted nodes. Experiments on the Inria, CrowdAI, GID, GF2 and Massachusetts datasets empirically demonstrate that TopDiG is class-agnostic and achieves competitive performance on all datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_TopDiG_Class-Agnostic_Topological_Directional_Graph_Extraction_From_Remote_Sensing_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_TopDiG_Class-Agnostic_Topological_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_TopDiG_Class-Agnostic_Topological_Directional_Graph_Extraction_From_Remote_Sensing_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_TopDiG_Class-Agnostic_Topological_Directional_Graph_Extraction_From_Remote_Sensing_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-per-Second | Vincent-Pierre Berges, Andrew Szot, Devendra Singh Chaplot, Aaron Gokaslan, Roozbeh Mottaghi, Dhruv Batra, Eric Undersander | We present Galactic, a large-scale simulation and reinforcement-learning (RL) framework for robotic mobile manipulation in indoor environments. Specifically, a Fetch robot (equipped with a mobile base, 7DoF arm, RGBD camera, egomotion, and onboard sensing) is spawned in a home environment and asked to rearrange objects -- by navigating to an object, picking it up, navigating to a target location, and then placing the object at the target location. Galactic is fast. In terms of simulation speed (rendering + physics), Galactic achieves over 421,000 steps-per-second (SPS) on an 8-GPU node, which is 54x faster than Habitat 2.0 (7699 SPS). More importantly, Galactic was designed to optimize the entire rendering+physics+RL interplay since any bottleneck in the interplay slows down training. In terms of simulation+RL speed (rendering + physics + inference + learning), Galactic achieves over 108,000 SPS, which 88x faster than Habitat 2.0 (1243 SPS). These massive speed-ups not only drastically cut the wall-clock training time of existing experiments, but also unlock an unprecedented scale of new experiments. First, Galactic can train a mobile pick skill to >80% accuracy in under 16 minutes, a 100x speedup compared to the over 24 hours it takes to train the same skill in Habitat 2.0. Second, we use Galactic to perform the largest-scale experiment to date for rearrangement using 5B steps of experience in 46 hours, which is equivalent to 20 years of robot experience. This scaling results in a single neural network composed of task-agnostic components achieving 85% success in GeometricGoal rearrangement, compared to 0% success reported in Habitat 2.0 for the same approach. The code is available at github.com/facebookresearch/galactic. | https://openaccess.thecvf.com/content/CVPR2023/papers/Berges_Galactic_Scaling_End-to-End_Reinforcement_Learning_for_Rearrangement_at_100k_Steps-per-Second_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Berges_Galactic_Scaling_End-to-End_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Berges_Galactic_Scaling_End-to-End_Reinforcement_Learning_for_Rearrangement_at_100k_Steps-per-Second_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Berges_Galactic_Scaling_End-to-End_Reinforcement_Learning_for_Rearrangement_at_100k_Steps-per-Second_CVPR_2023_paper.html | CVPR 2023 | null |
StyleIPSB: Identity-Preserving Semantic Basis of StyleGAN for High Fidelity Face Swapping | Diqiong Jiang, Dan Song, Ruofeng Tong, Min Tang | Recent researches reveal that StyleGAN can generate highly realistic images, inspiring researchers to use pretrained StyleGAN to generate high-fidelity swapped faces. However, existing methods fail to meet the expectations in two essential aspects of high-fidelity face swapping. Their results are blurry without pore-level details and fail to preserve identity for challenging cases. To overcome the above artifacts, we innovatively construct a series of identity-preserving semantic bases of StyleGAN (called StyleIPSB) in respect of pose, expression, and illumination. Each basis of StyleIPSB controls one specific semantic attribute and disentangles with the others. The StyleIPSB constrains style code in the subspace of W+ space to preserve pore-level details. StyleIPSB gives us a novel tool for high-fidelity face swapping, and we propose a three-stage framework for face swapping with StyleIPSB. Firstly, we transform the target facial images' attributes to the source image. We learn the mapping from 3D Morphable Model (3DMM) parameters, which capture the prominent semantic variance, to the coordinates of StyleIPSB that show higher identity-preserving and fidelity. Secondly, to transform detailed attributes which 3DMM does not capture, we learn the residual attribute between the reenacted face and the target face. Finally, the face is blended into the background of the target image. Extensive results and comparisons demonstrate that StyleIPSB can effectively preserve identity and pore-level details. The results of face swapping can achieve state-of-the-art performance. We will release our code at https://github.com/a686432/StyleIPSB. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_StyleIPSB_Identity-Preserving_Semantic_Basis_of_StyleGAN_for_High_Fidelity_Face_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_StyleIPSB_Identity-Preserving_Semantic_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_StyleIPSB_Identity-Preserving_Semantic_Basis_of_StyleGAN_for_High_Fidelity_Face_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_StyleIPSB_Identity-Preserving_Semantic_Basis_of_StyleGAN_for_High_Fidelity_Face_CVPR_2023_paper.html | CVPR 2023 | null |
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects | Wenteng Liang, Feng Xue, Yihao Liu, Guofeng Zhong, Anlong Ming | The recently proposed open-world object and open-set detection have achieved a breakthrough in finding never-seen-before objects and distinguishing them from known ones. However, their studies on knowledge transfer from known classes to unknown ones are not deep enough, resulting in the scanty capability for detecting unknowns hidden in the background. In this paper, we propose the unknown sniffer (UnSniffer) to find both unknown and known objects. Firstly, the generalized object confidence (GOC) score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the background. Significantly, such confidence score learned from known objects can be generalized to unknown ones. Additionally, we propose a negative energy suppression loss to further suppress the non-object samples in the background. Next, the best box of each unknown is hard to obtain during inference due to lacking their semantic information in training. To solve this issue, we introduce a graph-based determination scheme to replace hand-designed non-maximum suppression (NMS) post-processing. Finally, we present the Unknown Object Detection Benchmark, the first publicly benchmark that encompasses precision evaluation for unknown detection to our knowledge. Experiments show that our method is far better than the existing state-of-the-art methods. Code is available at: https://github.com/Went-Liang/UnSniffer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_Unknown_Sniffer_for_Object_Detection_Dont_Turn_a_Blind_Eye_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liang_Unknown_Sniffer_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Unknown_Sniffer_for_Object_Detection_Dont_Turn_a_Blind_Eye_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Unknown_Sniffer_for_Object_Detection_Dont_Turn_a_Blind_Eye_CVPR_2023_paper.html | CVPR 2023 | null |
Discriminator-Cooperated Feature Map Distillation for GAN Compression | Tie Hu, Mingbao Lin, Lizhou You, Fei Chao, Rongrong Ji | Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome "performance maker", knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over 40x MACs and 80x parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://github.com/poopit/DCD-official. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Discriminator-Cooperated_Feature_Map_Distillation_for_GAN_Compression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Discriminator-Cooperated_Feature_Map_Distillation_for_GAN_Compression_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14169 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Discriminator-Cooperated_Feature_Map_Distillation_for_GAN_Compression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Discriminator-Cooperated_Feature_Map_Distillation_for_GAN_Compression_CVPR_2023_paper.html | CVPR 2023 | null |
Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection | Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, Yunchao Wei | Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of 11.4%. The code is released at https://github.com/chuangchuangtan/LGrad. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Learning_on_Gradients_Generalized_Artifacts_Representation_for_GAN-Generated_Images_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Learning_on_Gradients_Generalized_Artifacts_Representation_for_GAN-Generated_Images_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Learning_on_Gradients_Generalized_Artifacts_Representation_for_GAN-Generated_Images_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Don't Lie to Me! Robust and Efficient Explainability With Verified Perturbation Analysis | Thomas Fel, Melanie Ducoffe, David Vigouroux, Rémi Cadène, Mikaël Capelle, Claire Nicodème, Thomas Serre | A variety of methods have been proposed to try to explain how deep neural networks make their decisions. Key to those approaches is the need to sample the pixel space efficiently in order to derive importance maps. However, it has been shown that the sampling methods used to date introduce biases and other artifacts, leading to inaccurate estimates of the importance of individual pixels and severely limit the reliability of current explainability methods. Unfortunately, the alternative -- to exhaustively sample the image space is computationally prohibitive. In this paper, we introduce EVA (Explaining using Verified perturbation Analysis) -- the first explainability method guarantee to have an exhaustive exploration of a perturbation space. Specifically, we leverage the beneficial properties of verified perturbation analysis -- time efficiency, tractability and guaranteed complete coverage of a manifold -- to efficiently characterize the input variables that are most likely to drive the model decision. We evaluate the approach systematically and demonstrate state-of-the-art results on multiple benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fel_Dont_Lie_to_Me_Robust_and_Efficient_Explainability_With_Verified_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fel_Dont_Lie_to_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fel_Dont_Lie_to_Me_Robust_and_Efficient_Explainability_With_Verified_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fel_Dont_Lie_to_Me_Robust_and_Efficient_Explainability_With_Verified_CVPR_2023_paper.html | CVPR 2023 | null |
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning | Yuqian Fu, Yu Xie, Yanwei Fu, Yu-Gang Jiang | Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains. It aims at transferring prior knowledge learned on the source dataset to novel target datasets. The CD-FSL task is especially challenged by the huge domain gap between different datasets. Critically, such a domain gap actually comes from the changes of visual styles, and wave-SAN empirically shows that spanning the style distribution of the source data helps alleviate this issue. However, wave-SAN simply swaps styles of two images. Such a vanilla operation makes the generated styles "real" and "easy", which still fall into the original set of the source styles. Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL. Particularly, our style attack method synthesizes both "virtual" and "hard" adversarial styles for model training. This is achieved by perturbing the original style with the signed style gradients. By continually attacking styles and forcing the model to recognize these challenging adversarial styles, our model is gradually robust to the visual styles, thus boosting the generalization ability for novel target datasets. Besides the typical CNN-based backbone, we also employ our StyleAdv method on large-scale pretrained vision transformer. Extensive experiments conducted on eight various target datasets show the effectiveness of our method. Whether built upon ResNet or ViT, we achieve the new state of the art for CD-FSL. Code is available at https://github.com/lovelyqian/StyleAdv-CDFSL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_StyleAdv_Meta_Style_Adversarial_Training_for_Cross-Domain_Few-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fu_StyleAdv_Meta_Style_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.09309 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_StyleAdv_Meta_Style_Adversarial_Training_for_Cross-Domain_Few-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_StyleAdv_Meta_Style_Adversarial_Training_for_Cross-Domain_Few-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Concept Customization of Text-to-Image Diffusion | Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, Jun-Yan Zhu | While generative models produce high-quality images of concepts learned from a large-scale database, a user often wishes to synthesize instantiations of their own concepts (for example, their family, pets, or items). Can we teach a model to quickly acquire a new concept, given a few examples? Furthermore, can we compose multiple new concepts together? We propose Custom Diffusion, an efficient method for augmenting existing text-to-image models. We find that only optimizing a few parameters in the text-to-image conditioning mechanism is sufficiently powerful to represent new concepts while enabling fast tuning ( 6 minutes). Additionally, we can jointly train for multiple concepts or combine multiple fine-tuned models into one via closed-form constrained optimization. Our fine-tuned model generates variations of multiple new concepts and seamlessly composes them with existing concepts in novel settings. Our method outperforms or performs on par with several baselines and concurrent works in both qualitative and quantitative evaluations, while being memory and computationally efficient. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kumari_Multi-Concept_Customization_of_Text-to-Image_Diffusion_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.04488 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kumari_Multi-Concept_Customization_of_Text-to-Image_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kumari_Multi-Concept_Customization_of_Text-to-Image_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Defending Against Patch-Based Backdoor Attacks on Self-Supervised Learning | Ajinkya Tejankar, Maziar Sanjabi, Qifan Wang, Sinong Wang, Hamed Firooz, Hamed Pirsiavash, Liang Tan | Recently, self-supervised learning (SSL) was shown to be vulnerable to patch-based data poisoning backdoor attacks. It was shown that an adversary can poison a small part of the unlabeled data so that when a victim trains an SSL model on it, the final model will have a backdoor that the adversary can exploit. This work aims to defend self-supervised learning against such attacks. We use a three-step defense pipeline, where we first train a model on the poisoned data. In the second step, our proposed defense algorithm (PatchSearch) uses the trained model to search the training data for poisoned samples and removes them from the training set. In the third step, a final model is trained on the cleaned-up training set. Our results show that PatchSearch is an effective defense. As an example, it improves a model's accuracy on images containing the trigger from 38.2% to 63.7% which is very close to the clean model's accuracy, 64.6%. Moreover, we show that PatchSearch outperforms baselines and state-of-the-art defense approaches including those using additional clean, trusted data. Our code is available at https://github.com/UCDvision/PatchSearch | https://openaccess.thecvf.com/content/CVPR2023/papers/Tejankar_Defending_Against_Patch-Based_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tejankar_Defending_Against_Patch-Based_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tejankar_Defending_Against_Patch-Based_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tejankar_Defending_Against_Patch-Based_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Long-Tailed Visual Recognition via Self-Heterogeneous Integration With Knowledge Excavation | Yan Jin, Mengke Li, Yang Lu, Yiu-ming Cheung, Hanzi Wang | Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heavily biased toward the majority classes. To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution. Experts in these methods are with the same model depth, which neglects the fact that different classes may have different preferences to be fit by models with different depths. To this end, we propose a novel MoE-based method called Self-Heterogeneous Integration with Knowledge Excavation (SHIKE). We first propose Depth-wise Knowledge Fusion (DKF) to fuse features between different shallow parts and the deep part in one network for each expert, which makes experts more diverse in terms of representation. Based on DKF, we further propose Dynamic Knowledge Transfer (DKT) to reduce the influence of the hardest negative class that has a non-negligible impact on the tail classes in our MoE framework. As a result, the classification accuracy of long-tailed data can be significantly improved, especially for the tail classes. SHIKE achieves the state-of-the-art performance of 56.3%, 60.3%, 75.4%, and 41.9% on CIFAR100-LT (IF100), ImageNet-LT, iNaturalist 2018, and Places-LT, respectively. The source code is available at https://github.com/jinyan-06/SHIKE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Long-Tailed_Visual_Recognition_via_Self-Heterogeneous_Integration_With_Knowledge_Excavation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.01279 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Long-Tailed_Visual_Recognition_via_Self-Heterogeneous_Integration_With_Knowledge_Excavation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Long-Tailed_Visual_Recognition_via_Self-Heterogeneous_Integration_With_Knowledge_Excavation_CVPR_2023_paper.html | CVPR 2023 | null |
GeoNet: Benchmarking Unsupervised Adaptation Across Geographies | Tarun Kalluri, Wangdong Xu, Manmohan Chandraker | In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training. An important practical problem pertains to models deployed in a new geography that is under-represented in the training dataset, posing a direct challenge to fair and inclusive computer vision. In this paper, we study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation containing benchmarks across diverse tasks like scene recognition (GeoPlaces), image classification (GeoImNet) and universal adaptation (GeoUniDA). Second, we investigate the nature of distribution shifts typical to the problem of geographic adaptation and hypothesize that the major source of domain shifts arise from significant variations in scene context (context shift), object design (design shift) and label distribution (prior shift) across geographies. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures on GeoNet, showing that they do not suffice for geographical adaptation, and that large-scale pre-training using large vision models also does not lead to geographic robustness. Our dataset is publicly available at https://tarun005.github.io/GeoNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kalluri_GeoNet_Benchmarking_Unsupervised_Adaptation_Across_Geographies_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kalluri_GeoNet_Benchmarking_Unsupervised_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15443 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kalluri_GeoNet_Benchmarking_Unsupervised_Adaptation_Across_Geographies_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kalluri_GeoNet_Benchmarking_Unsupervised_Adaptation_Across_Geographies_CVPR_2023_paper.html | CVPR 2023 | null |
Context De-Confounded Emotion Recognition | Dingkang Yang, Zhaoyu Chen, Yuzheng Wang, Shunli Wang, Mingcheng Li, Siao Liu, Xiao Zhao, Shuai Huang, Zhiyan Dong, Peng Zhai, Lihua Zhang | Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Context_De-Confounded_Emotion_Recognition_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.11921 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Context_De-Confounded_Emotion_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Context_De-Confounded_Emotion_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
LinK: Linear Kernel for LiDAR-Based 3D Perception | Tao Lu, Xiang Ding, Haisong Liu, Gangshan Wu, Limin Wang | Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from data scarcity and sparsity. Previous work has taken the first step to scale up the kernel size from 3x3x3 to 7x7x7 by introducing block-shared weights. However, to reduce the feature variations within a block, it only employs modest block size and fails to achieve larger kernels like the 21x21x21. To address this issue, we propose a new method, called LinK, to achieve a wider-range perception receptive field in a convolution-like manner with two core designs. The first is to replace the static kernel matrix with a linear kernel generator, which adaptively provides weights only for non-empty voxels. The second is to reuse the pre-computed aggregation results in the overlapped blocks to reduce computation complexity. The proposed method successfully enables each voxel to perceive context within a range of 21x21x21. Extensive experiments on two basic perception tasks, 3D object detection and 3D semantic segmentation, demonstrate the effectiveness of our method. Notably, we rank 1st on the public leaderboard of the 3D detection benchmark of nuScenes (LiDAR track), by simply incorporating a LinK-based backbone into the basic detector, CenterPoint. We also boost the strong segmentation baseline's mIoU with 2.7% in the SemanticKITTI test set. Code is available at https://github.com/MCG-NJU/LinK. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_LinK_Linear_Kernel_for_LiDAR-Based_3D_Perception_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_LinK_Linear_Kernel_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16094 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_LinK_Linear_Kernel_for_LiDAR-Based_3D_Perception_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_LinK_Linear_Kernel_for_LiDAR-Based_3D_Perception_CVPR_2023_paper.html | CVPR 2023 | null |
CP3: Channel Pruning Plug-In for Point-Based Networks | Yaomin Huang, Ning Liu, Zhengping Che, Zhiyuan Xu, Chaomin Shen, Yaxin Peng, Guixu Zhang, Xinmei Liu, Feifei Feng, Jian Tang | Channel pruning has been widely studied as a prevailing method that effectively reduces both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success has been achieved in channel pruning for 2D image-based convolutional networks (CNNs), existing works seldom extend the channel pruning methods to 3D point-based neural networks (PNNs). Directly implementing the 2D CNN channel pruning methods to PNNs undermine the performance of PNNs because of the different representations of 2D images and 3D point clouds as well as the network architecture disparity. In this paper, we proposed CP^3, which is a Channel Pruning Plug-in for Point-based network. CP^3 is elaborately designed to leverage the characteristics of point clouds and PNNs in order to enable 2D channel pruning methods for PNNs. Specifically, it presents a coordinate-enhanced channel importance metric to reflect the correlation between dimensional information and individual channel features, and it recycles the discarded points in PNN's sampling process and reconsiders their potentially-exclusive information to enhance the robustness of channel pruning. Experiments on various PNN architectures show that CP^3 constantly improves state-of-the-art 2D CNN pruning approaches on different point cloud tasks. For instance, our compressed PointNeXt-S on ScanObjectNN achieves an accuracy of 88.52% with a pruning rate of 57.8%, outperforming the baseline pruning methods with an accuracy gain of 1.94%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_CP3_Channel_Pruning_Plug-In_for_Point-Based_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_CP3_Channel_Pruning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_CP3_Channel_Pruning_Plug-In_for_Point-Based_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_CP3_Channel_Pruning_Plug-In_for_Point-Based_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
InstructPix2Pix: Learning To Follow Image Editing Instructions | Tim Brooks, Aleksander Holynski, Alexei A. Efros | We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models--a language model (GPT-3) and a text-to-image model (Stable Diffusion)--to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per-example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Brooks_InstructPix2Pix_Learning_To_Follow_Image_Editing_Instructions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Brooks_InstructPix2Pix_Learning_To_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09800 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Brooks_InstructPix2Pix_Learning_To_Follow_Image_Editing_Instructions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Brooks_InstructPix2Pix_Learning_To_Follow_Image_Editing_Instructions_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Transformation-Predictive Representations for Detection and Description of Local Features | Zihao Wang, Chunxu Wu, Yifei Yang, Zhen Li | The task of key-points detection and description is to estimate the stable location and discriminative representation of local features, which is essential for image matching. However, either the rough hard positive or negative labels generated from one-to-one correspondences among images bring indistinguishable samples, called pseudo positives or negatives, which act as inconsistent supervisions while learning key-points used for matching. Such pseudo-labeled samples prevent deep neural networks from learning discriminative descriptions for accurate matching. To tackle this challenge, we propose to learn transformation-predictive representations with self-supervised contrastive learning. We maximize the similarity between corresponded views of the same 3D point (landmark) by using none of the negative sample pairs (including true and pseudo negatives) and avoiding collapsing solutions. Then we design a learnable label prediction mechanism to soften the hard positive labels into soft continuous targets. The aggressively updated soft labels extensively deal with the training bottleneck (derived from the label noise of pseudo positives) and make the model can be trained under a stronger augmentation paradigm. Our self-supervised method outperforms the state-of-the-art on the standard image matching benchmarks by noticeable margins and shows excellent generalization capability on multiple downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Learning_Transformation-Predictive_Representations_for_Detection_and_Description_of_Local_Features_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Learning_Transformation-Predictive_Representations_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Transformation-Predictive_Representations_for_Detection_and_Description_of_Local_Features_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Transformation-Predictive_Representations_for_Detection_and_Description_of_Local_Features_CVPR_2023_paper.html | CVPR 2023 | null |
Two-Way Multi-Label Loss | Takumi Kobayashi | A natural image frequently contains multiple classification targets, accordingly providing multiple class labels rather than a single label per image. While the single-label classification is effectively addressed by applying a softmax cross-entropy loss, the multi-label task is tackled mainly in a binary cross-entropy (BCE) framework. In contrast to the softmax loss, the BCE loss involves issues regarding imbalance as multiple classes are decomposed into a bunch of binary classifications; recent works improve the BCE loss to cope with the issue by means of weighting. In this paper, we propose a multi-label loss by bridging a gap between the softmax loss and the multi-label scenario. The proposed loss function is formulated on the basis of relative comparison among classes which also enables us to further improve discriminative power of features by enhancing classification margin. The loss function is so flexible as to be applicable to a multi-label setting in two ways for discriminating classes as well as samples. In the experiments on multi-label classification, the proposed method exhibits competitive performance to the other multi-label losses, and it also provides transferrable features on single-label ImageNet training. Codes are available at https://github.com/tk1980/TwowayMultiLabelLoss. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kobayashi_Two-Way_Multi-Label_Loss_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kobayashi_Two-Way_Multi-Label_Loss_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kobayashi_Two-Way_Multi-Label_Loss_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kobayashi_Two-Way_Multi-Label_Loss_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head Synthesis | Duomin Wang, Yu Deng, Zixin Yin, Heung-Yeung Shum, Baoyuan Wang | We present a novel one-shot talking head synthesis method that achieves disentangled and fine-grained control over lip motion, eye gaze&blink, head pose, and emotional expression. We represent different motions via disentangled latent representations and leverage an image generator to synthesize talking heads from them. To effectively disentangle each motion factor, we propose a progressive disentangled representation learning strategy by separating the factors in a coarse-to-fine manner, where we first extract unified motion feature from the driving signal, and then isolate each fine-grained motion from the unified feature. We introduce motion-specific contrastive learning and regressing for non-emotional motions, and feature-level decorrelation and self-reconstruction for emotional expression, to fully utilize the inherent properties of each motion factor in unstructured video data to achieve disentanglement. Experiments show that our method provides high quality speech&lip-motion synchronization along with precise and disentangled control over multiple extra facial motions, which can hardly be achieved by previous methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Progressive_Disentangled_Representation_Learning_for_Fine-Grained_Controllable_Talking_Head_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Progressive_Disentangled_Representation_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.14506 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Progressive_Disentangled_Representation_Learning_for_Fine-Grained_Controllable_Talking_Head_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Progressive_Disentangled_Representation_Learning_for_Fine-Grained_Controllable_Talking_Head_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Breaking the "Object" in Video Object Segmentation | Pavel Tokmakov, Jie Li, Adrien Gaidon | The appearance of an object can be fleeting when it transforms. As eggs are broken or paper is torn, their color, shape, and texture can change dramatically, preserving virtually nothing of the original except for the identity itself. Yet, this important phenomenon is largely absent from existing video object segmentation (VOS) benchmarks. In this work, we close the gap by collecting a new dataset for Video Object Segmentation under Transformations (VOST). It consists of more than 700 high-resolution videos, captured in diverse environments, which are 20 seconds long on average and densely labeled with instance masks. A careful, multi-step approach is adopted to ensure that these videos focus on complex object transformations, capturing their full temporal extent. We then extensively evaluate state-of-the-art VOS methods and make a number of important discoveries. In particular, we show that existing methods struggle when applied to this novel task and that their main limitation lies in over-reliance on static, appearance cues. This motivates us to propose a few modifications for the top-performing baseline that improve its performance by better capturing spatio-temporal information. But more broadly, the hope is to stimulate discussion on learning more robust video object representations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tokmakov_Breaking_the_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Where Is My Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization | Mengmeng Xu, Yanghao Li, Cheng-Yang Fu, Bernard Ghanem, Tao Xiang, Juan-Manuel Pérez-Rúa | This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual query localization. We first identify grave implicit biases in current query-conditioned model design and visual query datasets. Then, we directly tackle such biases at both frame and object set levels. Concretely, our method solves these issues by expanding limited annotations and dynamically dropping object proposals during training. Additionally, we propose a novel transformer-based module that allows for object-proposal set context to be considered while incorporating query information. We name our module Conditioned Contextual Transformer or CocoFormer. Our experiments show that the proposed adaptations improve egocentric query detection, leading to a better visual query localization system in both 2D and 3D configurations. Thus, we are able to improve frame-level detection performance from 26.28% to 31.26% in AP, which correspondingly improves the VQ2D and VQ3D localization scores by significant margins. Our improved context-aware query object detector ranked first and second in the VQ2D and VQ3D tasks in the 2nd Ego4D challenge. In addition, we showcase the relevance of our proposed model in the Few-Shot Detection (FSD) task, where we also achieve SOTA results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Where_Is_My_Wallet_Modeling_Object_Proposal_Sets_for_Egocentric_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Where_Is_My_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10528 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Where_Is_My_Wallet_Modeling_Object_Proposal_Sets_for_Egocentric_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Where_Is_My_Wallet_Modeling_Object_Proposal_Sets_for_Egocentric_CVPR_2023_paper.html | CVPR 2023 | null |
Dionysus: Recovering Scene Structures by Dividing Into Semantic Pieces | Likang Wang, Lei Chen | Most existing 3D reconstruction methods result in either detail loss or unsatisfying efficiency. However, effectiveness and efficiency are equally crucial in real-world applications, e.g., autonomous driving and augmented reality. We argue that this dilemma comes from wasted resources on valueless depth samples. This paper tackles the problem by proposing a novel learning-based 3D reconstruction framework named Dionysus. Our main contribution is to find out the most promising depth candidates from estimated semantic maps. This strategy simultaneously enables high effectiveness and efficiency by attending to the most reliable nominators. Specifically, we distinguish unreliable depth candidates by checking the cross-view semantic consistency and allow adaptive sampling by redistributing depth nominators among pixels. Experiments on the most popular datasets confirm our proposed framework's effectiveness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Dionysus_Recovering_Scene_Structures_by_Dividing_Into_Semantic_Pieces_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Dionysus_Recovering_Scene_Structures_by_Dividing_Into_Semantic_Pieces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Dionysus_Recovering_Scene_Structures_by_Dividing_Into_Semantic_Pieces_CVPR_2023_paper.html | CVPR 2023 | null |
ReDirTrans: Latent-to-Latent Translation for Gaze and Head Redirection | Shiwei Jin, Zhen Wang, Lei Wang, Ning Bi, Truong Nguyen | Learning-based gaze estimation methods require large amounts of training data with accurate gaze annotations. Facing such demanding requirements of gaze data collection and annotation, several image synthesis methods were proposed, which successfully redirected gaze directions precisely given the assigned conditions. However, these methods focused on changing gaze directions of the images that only include eyes or restricted ranges of faces with low resolution (less than 128*128) to largely reduce interference from other attributes such as hairs, which limits application scenarios. To cope with this limitation, we proposed a portable network, called ReDirTrans, achieving latent-to-latent translation for redirecting gaze directions and head orientations in an interpretable manner. ReDirTrans projects input latent vectors into aimed-attribute embeddings only and redirects these embeddings with assigned pitch and yaw values. Then both the initial and edited embeddings are projected back (deprojected) to the initial latent space as residuals to modify the input latent vectors by subtraction and addition, representing old status removal and new status addition. The projection of aimed attributes only and subtraction-addition operations for status replacement essentially mitigate impacts on other attributes and the distribution of latent vectors. Thus, by combining ReDirTrans with a pretrained fixed e4e-StyleGAN pair, we created ReDirTrans-GAN, which enables accurately redirecting gaze in full-face images with 1024*1024 resolution while preserving other attributes such as identity, expression, and hairstyle. Furthermore, we presented improvements for the downstream learning-based gaze estimation task, using redirected samples as dataset augmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_ReDirTrans_Latent-to-Latent_Translation_for_Gaze_and_Head_Redirection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_ReDirTrans_Latent-to-Latent_Translation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_ReDirTrans_Latent-to-Latent_Translation_for_Gaze_and_Head_Redirection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_ReDirTrans_Latent-to-Latent_Translation_for_Gaze_and_Head_Redirection_CVPR_2023_paper.html | CVPR 2023 | null |
Advancing Visual Grounding With Scene Knowledge: Benchmark and Method | Zhihong Chen, Ruifei Zhang,Yibing Song, Xiang Wan, Guanbin Li | Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understanding of the images and texts and their reasoning abilities over their joint space. However, most existing VG datasets are constructed using simple description texts, which do not require sufficient reasoning over the images and texts. This has been demonstrated in a recent study, where a simple LSTM-based text encoder without pretraining can achieve state-of-the-art performance on mainstream VG datasets. Therefore, in this paper, we propose a novel benchmark of Scene Knowledge-guided Visual Grounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, we propose two approaches to accept the triple-type input, where the former embeds knowledge into the image features before the image-query interaction; the latter leverages linguistic structure to assist in computing the image-text matching. We conduct extensive experiments to analyze the above methods and show that the proposed approaches achieve promising results but still leave room for improvement, including performance and interpretability. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Advancing_Visual_Grounding_With_Scene_Knowledge_Benchmark_and_Method_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Advancing_Visual_Grounding_With_Scene_Knowledge_Benchmark_and_Method_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Advancing_Visual_Grounding_With_Scene_Knowledge_Benchmark_and_Method_CVPR_2023_paper.html | CVPR 2023 | null |
Noisy Correspondence Learning With Meta Similarity Correction | Haochen Han, Kaiyao Miao, Qinghua Zheng, Minnan Luo | Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_Noisy_Correspondence_Learning_With_Meta_Similarity_Correction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_Noisy_Correspondence_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06275 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Noisy_Correspondence_Learning_With_Meta_Similarity_Correction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Noisy_Correspondence_Learning_With_Meta_Similarity_Correction_CVPR_2023_paper.html | CVPR 2023 | null |
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation | Samir Yitzhak Gadre, Mitchell Wortsman, Gabriel Ilharco, Ludwig Schmidt, Shuran Song | For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasture benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 22 CoW baselines across Habitat, RoboTHOR, and Pasture. In total we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions, but are surprisingly proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration---and no additional training---matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on Habitat MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art RoboTHOR ZSON model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gadre_CoWs_on_Pasture_Baselines_and_Benchmarks_for_Language-Driven_Zero-Shot_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gadre_CoWs_on_Pasture_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2203.10421 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gadre_CoWs_on_Pasture_Baselines_and_Benchmarks_for_Language-Driven_Zero-Shot_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gadre_CoWs_on_Pasture_Baselines_and_Benchmarks_for_Language-Driven_Zero-Shot_Object_CVPR_2023_paper.html | CVPR 2023 | null |
CIGAR: Cross-Modality Graph Reasoning for Domain Adaptive Object Detection | Yabo Liu, Jinghua Wang, Chao Huang, Yaowei Wang, Yong Xu | Unsupervised domain adaptive object detection (UDA-OD) aims to learn a detector by generalizing knowledge from a labeled source domain to an unlabeled target domain. Though the existing graph-based methods for UDA-OD perform well in some cases, they cannot learn a proper node set for the graph. In addition, these methods build the graph solely based on the visual features and do not consider the linguistic knowledge carried by the semantic prototypes, e.g., dataset labels. To overcome these problems, we propose a cross-modality graph reasoning adaptation (CIGAR) method to take advantage of both visual and linguistic knowledge. Specifically, our method performs cross-modality graph reasoning between the linguistic modality graph and visual modality graphs to enhance their representations. We also propose a discriminative feature selector to find the most discriminative features and take them as the nodes of the visual graph for both efficiency and effectiveness. In addition, we employ the linguistic graph matching loss to regulate the update of linguistic graphs and maintain their semantic representation during the training process. Comprehensive experiments validate the effectiveness of our proposed CIGAR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_CIGAR_Cross-Modality_Graph_Reasoning_for_Domain_Adaptive_Object_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_CIGAR_Cross-Modality_Graph_Reasoning_for_Domain_Adaptive_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_CIGAR_Cross-Modality_Graph_Reasoning_for_Domain_Adaptive_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Multiview Compressive Coding for 3D Reconstruction | Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, Georgia Gkioxari | A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and general-purpose representations. But, 3D poses new challenges stemming from occlusions not depicted in the image. Prior works try to overcome these by inferring from multiple views or rely on scarce CAD models and category-specific priors which hinder scaling to novel settings. In this work, we explore single-view 3D reconstruction by learning generalizable representations inspired by advances in self-supervised learning. We introduce a simple framework that operates on 3D points of single objects or whole scenes coupled with category-agnostic large-scale training from diverse RGB-D videos. Our model, Multiview Compressive Coding (MCC), learns to compress the input appearance and geometry to predict the 3D structure by querying a 3D-aware decoder. MCC's generality and efficiency allow it to learn from large-scale and diverse data sources with strong generalization to novel objects imagined by DALL*E 2 or captured in-the-wild with an iPhone. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Multiview_Compressive_Coding_for_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Multiview_Compressive_Coding_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.08247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Multiview_Compressive_Coding_for_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Multiview_Compressive_Coding_for_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics | Artur Grigorev, Michael J. Black, Otmar Hilliges | We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Our method furthermore handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that our method outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Grigorev_HOOD_Hierarchical_Graphs_for_Generalized_Modelling_of_Clothing_Dynamics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Grigorev_HOOD_Hierarchical_Graphs_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Grigorev_HOOD_Hierarchical_Graphs_for_Generalized_Modelling_of_Clothing_Dynamics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Grigorev_HOOD_Hierarchical_Graphs_for_Generalized_Modelling_of_Clothing_Dynamics_CVPR_2023_paper.html | CVPR 2023 | null |
HyperReel: High-Fidelity 6-DoF Video With Ray-Conditioned Sampling | Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhöfer, Johannes Kopf, Matthew O’Toole, Changil Kim | Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques. However, the volume rendering procedures that drive these representations necessitate careful trade-offs in terms of quality, rendering speed, and memory efficiency. In particular, existing methods fail to simultaneously achieve real-time performance, small memory footprint, and high-quality rendering for challenging real-world scenes. To address these issues, we present HyperReel --- a novel 6-DoF video representation. The two core components of HyperReel are: (1) a ray-conditioned sample prediction network that enables high-fidelity, high frame rate rendering at high resolutions and (2) a compact and memory-efficient dynamic volume representation. Our 6-DoF video pipeline achieves the best performance compared to prior and contemporary approaches in terms of visual quality with small memory requirements, while also rendering at up to 18 frames-per-second at megapixel resolution without any custom CUDA code. | https://openaccess.thecvf.com/content/CVPR2023/papers/Attal_HyperReel_High-Fidelity_6-DoF_Video_With_Ray-Conditioned_Sampling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Attal_HyperReel_High-Fidelity_6-DoF_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Attal_HyperReel_High-Fidelity_6-DoF_Video_With_Ray-Conditioned_Sampling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Attal_HyperReel_High-Fidelity_6-DoF_Video_With_Ray-Conditioned_Sampling_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning | AJ Piergiovanni, Weicheng Kuo, Anelia Angelova | We present a simple approach which can turn a ViT encoder into an efficient video model, which can seamlessly work with both image and video inputs. By sparsely sampling the inputs, the model is able to do training and inference from both inputs. The model is easily scalable and can be adapted to large-scale pre-trained ViTs without requiring full finetuning. The model achieves SOTA results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Piergiovanni_Rethinking_Video_ViTs_Sparse_Video_Tubes_for_Joint_Image_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Piergiovanni_Rethinking_Video_ViTs_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03229 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Piergiovanni_Rethinking_Video_ViTs_Sparse_Video_Tubes_for_Joint_Image_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Piergiovanni_Rethinking_Video_ViTs_Sparse_Video_Tubes_for_Joint_Image_and_CVPR_2023_paper.html | CVPR 2023 | null |
Modeling Entities As Semantic Points for Visual Information Extraction in the Wild | Zhibo Yang, Rujiao Long, Pengfei Wang, Sibo Song, Humen Zhong, Wenqing Cheng, Xiang Bai, Cong Yao | Recently, Visual Information Extraction (VIE) has been becoming increasingly important in both academia and industry, due to the wide range of real-world applications. Previously, numerous works have been proposed to tackle this problem. However, the benchmarks used to assess these methods are relatively plain, i.e., scenarios with real-world complexity are not fully represented in these benchmarks. As the first contribution of this work, we curate and release a new dataset for VIE, in which the document images are much more challenging in that they are taken from real applications, and difficulties such as blur, partial occlusion, and printing shift are quite common. All these factors may lead to failures in information extraction. Therefore, as the second contribution, we explore an alternative approach to precisely and robustly extract key information from document images under such tough conditions. Specifically, in contrast to previous methods, which usually either incorporate visual information into a multi-modal architecture or train text spotting and information extraction in an end-to-end fashion, we explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities, which could largely benefit entity labeling and linking. Extensive experiments on standard benchmarks in this field as well as the proposed dataset demonstrate that the proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Modeling_Entities_As_Semantic_Points_for_Visual_Information_Extraction_in_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.13095 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Modeling_Entities_As_Semantic_Points_for_Visual_Information_Extraction_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Modeling_Entities_As_Semantic_Points_for_Visual_Information_Extraction_in_CVPR_2023_paper.html | CVPR 2023 | null |
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning Meets Knowledge Distillation | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Miles_MobileVOS_Real-Time_Video_Object_Segmentation_Contrastive_Learning_Meets_Knowledge_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Miles_MobileVOS_Real-Time_Video_Object_Segmentation_Contrastive_Learning_Meets_Knowledge_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
PCR: Proxy-Based Contrastive Replay for Online Class-Incremental Continual Learning | Huiwei Lin, Baoquan Zhang, Shanshan Feng, Xutao Li, Yunming Ye | Online class-incremental continual learning is a specific task of continual learning. It aims to continuously learn new classes from data stream and the samples of data stream are seen only once, which suffers from the catastrophic forgetting issue, i.e., forgetting historical knowledge of old classes. Existing replay-based methods effectively alleviate this issue by saving and replaying part of old data in a proxy-based or contrastive-based replay manner. Although these two replay manners are effective, the former would incline to new classes due to class imbalance issues, and the latter is unstable and hard to converge because of the limited number of samples. In this paper, we conduct a comprehensive analysis of these two replay manners and find that they can be complementary. Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR). The key operation is to replace the contrastive samples of anchors with corresponding proxies in the contrastive-based way. It alleviates the phenomenon of catastrophic forgetting by effectively addressing the imbalance issue, as well as keeps a faster convergence of the model. We conduct extensive experiments on three real-world benchmark datasets, and empirical results consistently demonstrate the superiority of PCR over various state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_PCR_Proxy-Based_Contrastive_Replay_for_Online_Class-Incremental_Continual_Learning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.04408 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_PCR_Proxy-Based_Contrastive_Replay_for_Online_Class-Incremental_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_PCR_Proxy-Based_Contrastive_Replay_for_Online_Class-Incremental_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Pose Synchronization Under Multiple Pair-Wise Relative Poses | Yifan Sun, Qixing Huang | Pose synchronization, which seeks to estimate consistent absolute poses among a collection of objects from noisy relative poses estimated between pairs of objects in isolation, is a fundamental problem in many inverse applications. This paper studies an extreme setting where multiple relative pose estimates exist between each object pair, and the majority is incorrect. Popular methods that solve pose synchronization via recovering a low-rank matrix that encodes relative poses in block fail under this extreme setting. We introduce a three-step algorithm for pose synchronization under multiple relative pose inputs. The first step performs diffusion and clustering to compute the candidate poses of the input objects. We present a theoretical result to justify our diffusion formulation. The second step jointly optimizes the best pose for each object. The final step refines the output of the second step. Experimental results on benchmark datasets of structurefrom-motion and scan-based geometry reconstruction show that our approach offers more accurate absolute poses than state-of-the-art pose synchronization techniques. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Pose_Synchronization_Under_Multiple_Pair-Wise_Relative_Poses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Pose_Synchronization_Under_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Pose_Synchronization_Under_Multiple_Pair-Wise_Relative_Poses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Pose_Synchronization_Under_Multiple_Pair-Wise_Relative_Poses_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Continual Semantic Adaptation Through Neural Rendering | Zhizheng Liu, Francesco Milano, Jonas Frey, Roland Siegwart, Hermann Blum, Cesar Cadena | An increasing amount of applications rely on data-driven models that are deployed for perception tasks across a sequence of scenes. Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance. In this work, we study continual multi-scene adaptation for the task of semantic segmentation, assuming that no ground-truth labels are available during deployment and that performance on the previous scenes should be maintained. We propose training a Semantic-NeRF network for each scene by fusing the predictions of a segmentation model and then using the view-consistent rendered semantic labels as pseudo-labels to adapt the model. Through joint training with the segmentation model, the Semantic-NeRF model effectively enables 2D-3D knowledge transfer. Furthermore, due to its compact size, it can be stored in a long-term memory and subsequently used to render data from arbitrary viewpoints to reduce forgetting. We evaluate our approach on ScanNet, where we outperform both a voxel-based baseline and a state-of-the-art unsupervised domain adaptation method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Unsupervised_Continual_Semantic_Adaptation_Through_Neural_Rendering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Unsupervised_Continual_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13969 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Unsupervised_Continual_Semantic_Adaptation_Through_Neural_Rendering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Unsupervised_Continual_Semantic_Adaptation_Through_Neural_Rendering_CVPR_2023_paper.html | CVPR 2023 | null |
Controllable Light Diffusion for Portraits | David Futschik, Kelvin Ritland, James Vecore, Sean Fanello, Sergio Orts-Escolano, Brian Curless, Daniel Sýkora, Rohit Pandey | We introduce light diffusion, a novel method to improve lighting in portraits, softening harsh shadows and specular highlights while preserving overall scene illumination. Inspired by professional photographers' diffusers and scrims, our method softens lighting given only a single portrait photo. Previous portrait relighting approaches focus on changing the entire lighting environment, removing shadows (ignoring strong specular highlights), or removing shading entirely. In contrast, we propose a learning based method that allows us to control the amount of light diffusion and apply it on in-the-wild portraits. Additionally, we design a method to synthetically generate plausible external shadows with sub-surface scattering effects while conforming to the shape of the subject's face. Finally, we show how our approach can increase the robustness of higher level vision applications, such as albedo estimation, geometry estimation and semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Futschik_Controllable_Light_Diffusion_for_Portraits_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Futschik_Controllable_Light_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.04745 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Futschik_Controllable_Light_Diffusion_for_Portraits_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Futschik_Controllable_Light_Diffusion_for_Portraits_CVPR_2023_paper.html | CVPR 2023 | null |
Token Boosting for Robust Self-Supervised Visual Transformer Pre-Training | Tianjiao Li, Lin Geng Foo, Ping Hu, Xindi Shang, Hossein Rahmani, Zehuan Yuan, Jun Liu | Learning with large-scale unlabeled data has become a powerful tool for pre-training Visual Transformers (VTs). However, prior works tend to overlook that, in real-world scenarios, the input data may be corrupted and unreliable. Pre-training VTs on such corrupted data can be challenging, especially when we pre-train via the masked autoencoding approach, where both the inputs and masked "ground truth" targets can potentially be unreliable in this case. To address this limitation, we introduce the Token Boosting Module (TBM) as a plug-and-play component for VTs that effectively allows the VT to learn to extract clean and robust features during masked autoencoding pre-training. We provide theoretical analysis to show how TBM improves model pre-training with more robust and generalizable representations, thus benefiting downstream tasks. We conduct extensive experiments to analyze TBM's effectiveness, and results on four corrupted datasets demonstrate that TBM consistently improves performance on downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Token_Boosting_for_Robust_Self-Supervised_Visual_Transformer_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Token_Boosting_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04175 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Token_Boosting_for_Robust_Self-Supervised_Visual_Transformer_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Token_Boosting_for_Robust_Self-Supervised_Visual_Transformer_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-View Adversarial Discriminator: Mine the Non-Causal Factors for Object Detection in Unseen Domains | Mingjun Xu, Lingyun Qin, Weijie Chen, Shiliang Pu, Lei Zhang | Domain shift degrades the performance of object detection models in practical applications. To alleviate the influence of domain shift, plenty of previous work try to decouple and learn the domain-invariant (common) features from source domains via domain adversarial learning (DAL). However, inspired by causal mechanisms, we find that previous methods ignore the implicit insignificant non-causal factors hidden in the common features. This is mainly due to the single-view nature of DAL. In this work, we present an idea to remove non-causal factors from common features by multi-view adversarial training on source domains, because we observe that such insignificant non-causal factors may still be significant in other latent spaces (views) due to the multi-mode structure of data. To summarize, we propose a Multi-view Adversarial Discriminator (MAD) based domain generalization model, consisting of a Spurious Correlations Generator (SCG) that increases the diversity of source domain by random augmentation and a Multi-View Domain Classifier (MVDC) that maps features to multiple latent spaces, such that the non-causal factors are removed and the domain-invariant features are purified. Extensive experiments on six benchmarks show our MAD obtains state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Multi-View_Adversarial_Discriminator_Mine_the_Non-Causal_Factors_for_Object_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.02950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Multi-View_Adversarial_Discriminator_Mine_the_Non-Causal_Factors_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Multi-View_Adversarial_Discriminator_Mine_the_Non-Causal_Factors_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset | Chen Feng, Ioannis Patras | Deep learning has achieved great success in recent years with the aid of advanced neural network structures and large-scale human-annotated datasets. However, it is often costly and difficult to accurately and efficiently annotate large-scale datasets, especially for some specialized domains where fine-grained labels are required. In this setting, coarse labels are much easier to acquire as they do not require expert knowledge. In this work, we propose a contrastive learning method, called masked contrastive learning (MaskCon) to address the under-explored problem setting, where we learn with a coarse-labelled dataset in order to address a finer labelling problem. More specifically, within the contrastive learning framework, for each sample our method generates soft-labels with the aid of coarse labels against other samples and another augmented view of the sample in question. By contrast to self-supervised contrastive learning where only the sample's augmentations are considered hard positives, and in supervised contrastive learning where only samples with the same coarse labels are considered hard positives, we propose soft labels based on sample distances, that are masked by the coarse labels. This allows us to utilize both inter-sample relations and coarse labels. We demonstrate that our method can obtain as special cases many existing state-of-the-art works and that it provides tighter bounds on the generalization error. Experimentally, our method achieves significant improvement over the current state-of-the-art in various datasets, including CIFAR10, CIFAR100, ImageNet-1K, Standford Online Products and Stanford Cars196 datasets. Code and annotations are available at https://github.com/MrChenFeng/MaskCon_CVPR2023. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_MaskCon_Masked_Contrastive_Learning_for_Coarse-Labelled_Dataset_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_MaskCon_Masked_Contrastive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12756 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_MaskCon_Masked_Contrastive_Learning_for_Coarse-Labelled_Dataset_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_MaskCon_Masked_Contrastive_Learning_for_Coarse-Labelled_Dataset_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Low-Data Instance Segmentation by Unsupervised Pre-Training With Saliency Prompt | Hao Li, Dingwen Zhang, Nian Liu, Lechao Cheng, Yalun Dai, Chao Zhang, Xinggang Wang, Junwei Han | Recently, inspired by DETR variants, query-based end-to-end instance segmentation (QEIS) methods have outperformed CNN-based models on large-scale datasets. Yet they would lose efficacy when only a small amount of training data is available since it's hard for the crucial queries/kernels to learn localization and shape priors. To this end, this work offers a novel unsupervised pre-training solution for low-data regimes. Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models by giving Saliency Prompt for queries/kernels. Our method contains three parts: 1) Saliency Masks Proposal is responsible for generating pseudo masks from unlabeled images based on the saliency mechanism. 2) Prompt-Kernel Matching transfers pseudo masks into prompts and injects the corresponding localization and shape priors to the best-matched kernels. 3) Kernel Supervision is applied to supply supervision at the kernel level for robust learning. From a practical perspective, our pre-training method helps QEIS models achieve a similar convergence speed and comparable performance with CNN-based models in low-data regimes. Experimental results show that our method significantly boosts several QEIS models on three datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Boosting_Low-Data_Instance_Segmentation_by_Unsupervised_Pre-Training_With_Saliency_Prompt_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Boosting_Low-Data_Instance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.01171 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Boosting_Low-Data_Instance_Segmentation_by_Unsupervised_Pre-Training_With_Saliency_Prompt_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Boosting_Low-Data_Instance_Segmentation_by_Unsupervised_Pre-Training_With_Saliency_Prompt_CVPR_2023_paper.html | CVPR 2023 | null |
Virtual Occlusions Through Implicit Depth | Jamie Watson, Mohamed Sayed, Zawar Qureshi, Gabriel J. Brostow, Sara Vicente, Oisin Mac Aodha, Michael Firman | For augmented reality (AR), it is important that virtual assets appear to 'sit among' real world objects. The virtual element should variously occlude and be occluded by real matter, based on a plausible depth ordering. This occlusion should be consistent over time as the viewer's camera moves. Unfortunately, small mistakes in the estimated scene depth can ruin the downstream occlusion mask, and thereby the AR illusion. Especially in real-time settings, depths inferred near boundaries or across time can be inconsistent. In this paper, we challenge the need for depth-regression as an intermediate step. We instead propose an implicit model for depth and use that to predict the occlusion mask directly. The inputs to our network are one or more color images, plus the known depths of any virtual geometry. We show how our occlusion predictions are more accurate and more temporally stable than predictions derived from traditional depth-estimation models. We obtain state-of-the-art occlusion results on the challenging ScanNetv2 dataset and superior qualitative results on real scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Watson_Virtual_Occlusions_Through_Implicit_Depth_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.07014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Watson_Virtual_Occlusions_Through_Implicit_Depth_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Watson_Virtual_Occlusions_Through_Implicit_Depth_CVPR_2023_paper.html | CVPR 2023 | null |
AGAIN: Adversarial Training With Attribution Span Enlargement and Hybrid Feature Fusion | Shenglin Yin, Kelu Yao, Sheng Shi, Yangzhou Du, Zhen Xiao | The deep neural networks (DNNs) trained by adversarial training (AT) usually suffered from significant robust generalization gap, i.e., DNNs achieve high training robustness but low test robustness. In this paper, we propose a generic method to boost the robust generalization of AT methods from the novel perspective of attribution span. To this end, compared with standard DNNs, we discover that the generalization gap of adversarially trained DNNs is caused by the smaller attribution span on the input image. In other words, adversarially trained DNNs tend to focus on specific visual concepts on training images, causing its limitation on test robustness. In this way, to enhance the robustness, we propose an effective method to enlarge the learned attribution span. Besides, we use hybrid feature statistics for feature fusion to enrich the diversity of features. Extensive experiments show that our method can effectively improves robustness of adversarially trained DNNs, outperforming previous SOTA methods. Furthermore, we provide a theoretical analysis of our method to prove its effectiveness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_AGAIN_Adversarial_Training_With_Attribution_Span_Enlargement_and_Hybrid_Feature_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yin_AGAIN_Adversarial_Training_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_AGAIN_Adversarial_Training_With_Attribution_Span_Enlargement_and_Hybrid_Feature_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_AGAIN_Adversarial_Training_With_Attribution_Span_Enlargement_and_Hybrid_Feature_CVPR_2023_paper.html | CVPR 2023 | null |
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection | Vibashan VS, Poojan Oza, Vishal M. Patel | Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve generalization on the target domain. Further, UDA methods work under the assumption that the source data is accessible during the adaptation process. However, in real-world scenarios, the labelled source data is often restricted due to privacy regulations, data transmission constraints, or proprietary data concerns. The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data. In this paper, we explore the SFDA setting for the task of adaptive object detection. To this end, we propose a novel training strategy for adapting a source-trained object detector to the target domain without source data. More precisely, we design a novel contrastive loss to enhance the target representations by exploiting the objects relations for a given target domain input. These object instance relations are modelled using an Instance Relation Graph (IRG) network, which are then used to guide the contrastive representation learning. In addition, we utilize a student-teacher to effectively distill knowledge from source-trained model to target domain. Extensive experiments on multiple object detection benchmark datasets show that the proposed approach is able to efficiently adapt source-trained object detectors to the target domain, outperforming state-of-the-art domain adaptive detection methods. Code and models are provided in https://viudomain.github.io/irg-sfda-web/ | https://openaccess.thecvf.com/content/CVPR2023/papers/VS_Instance_Relation_Graph_Guided_Source-Free_Domain_Adaptive_Object_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2203.15793 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/VS_Instance_Relation_Graph_Guided_Source-Free_Domain_Adaptive_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/VS_Instance_Relation_Graph_Guided_Source-Free_Domain_Adaptive_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Instant Multi-View Head Capture Through Learnable Registration | Timo Bolkart, Tianye Li, Michael J. Black | Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow and commonly address the problem in two separate steps; multi-view stereo (MVS) reconstruction followed by non-rigid registration. To simplify this process, we introduce TEMPEH (Towards Estimation of 3D Meshes from Performances of Expressive Heads) to directly infer 3D heads in dense correspondence from calibrated multi-view images. Registering datasets of 3D scans typically requires manual parameter tuning to find the right balance between accurately fitting the scans' surfaces and being robust to scanning noise and outliers. Instead, we propose to jointly register a 3D head dataset while training TEMPEH. Specifically, during training, we minimize a geometric loss commonly used for surface registration, effectively leveraging TEMPEH as a regularizer. Our multi-view head inference builds on a volumetric feature representation that samples and fuses features from each view using camera calibration information. To account for partial occlusions and a large capture volume that enables head movements, we use view- and surface-aware feature fusion, and a spatial transformer-based head localization module, respectively. We use raw MVS scans as supervision during training, but, once trained, TEMPEH directly predicts 3D heads in dense correspondence without requiring scans. Predicting one head takes about 0.3 seconds with a median reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art. This enables the efficient capture of large datasets containing multiple people and diverse facial motions. Code, model, and data are publicly available at https://tempeh.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bolkart_Instant_Multi-View_Head_Capture_Through_Learnable_Registration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bolkart_Instant_Multi-View_Head_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bolkart_Instant_Multi-View_Head_Capture_Through_Learnable_Registration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bolkart_Instant_Multi-View_Head_Capture_Through_Learnable_Registration_CVPR_2023_paper.html | CVPR 2023 | null |
DiGA: Distil To Generalize and Then Adapt for Domain Adaptive Semantic Segmentation | Fengyi Shen, Akhil Gurram, Ziyuan Liu, He Wang, Alois Knoll | Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage. However, this popular approach still faces several challenges in each stage: for warm-up, the widely adopted adversarial training often results in limited performance gain, due to blind feature alignment; for self-training, finding proper categorical thresholds is very tricky. To alleviate these issues, we first propose to replace the adversarial training in the warm-up stage by a novel symmetric knowledge distillation module that only accesses the source domain data and makes the model domain generalizable. Surprisingly, this domain generalizable warm-up model brings substantial performance improvement, which can be further amplified via our proposed cross-domain mixture data augmentation technique. Then, for the self-training stage, we propose a threshold-free dynamic pseudo-label selection mechanism to ease the aforementioned threshold problem and make the model better adapted to the target domain. Extensive experiments demonstrate that our framework achieves remarkable and consistent improvements compared to the prior arts on popular benchmarks. Codes and models are available at https://github.com/fy-vision/DiGA | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_DiGA_Distil_To_Generalize_and_Then_Adapt_for_Domain_Adaptive_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_DiGA_Distil_To_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02222 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_DiGA_Distil_To_Generalize_and_Then_Adapt_for_Domain_Adaptive_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_DiGA_Distil_To_Generalize_and_Then_Adapt_for_Domain_Adaptive_CVPR_2023_paper.html | CVPR 2023 | null |
DiffSwap: High-Fidelity and Controllable Face Swapping via 3D-Aware Masked Diffusion | Wenliang Zhao, Yongming Rao, Weikang Shi, Zuyan Liu, Jie Zhou, Jiwen Lu | In this paper, we propose DiffSwap, a diffusion model based framework for high-fidelity and controllable face swapping. Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a conditional inpainting task, performed by a powerful diffusion model guided by the desired face attributes (e.g., identity and landmarks). An important issue that makes it nontrivial to apply diffusion models to face swapping is that we cannot perform the time-consuming multi-step sampling to obtain the generated image during training. To overcome this, we propose a midpoint estimation method to efficiently recover a reasonable diffusion result of the swapped face with only 2 steps, which enables us to introduce identity constraints to improve the face swapping quality. Our framework enjoys several favorable properties more appealing than prior arts: 1) Controllable. Our method is based on conditional masked diffusion on the latent space, where the mask and the conditions can be fully controlled and customized. 2) High-fidelity. The formulation of conditional inpainting can fully exploit the generative ability of diffusion models and can preserve the background of target images with minimal artifacts. 3) Shape-preserving. The controllability of our method enables us to use 3D-aware landmarks as the condition during generation to preserve the shape of the source face. Extensive experiments on both FF++ and FFHQ demonstrate that our method can achieve state-of-the-art face swapping results both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_DiffSwap_High-Fidelity_and_Controllable_Face_Swapping_via_3D-Aware_Masked_Diffusion_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_DiffSwap_High-Fidelity_and_Controllable_Face_Swapping_via_3D-Aware_Masked_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_DiffSwap_High-Fidelity_and_Controllable_Face_Swapping_via_3D-Aware_Masked_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
GINA-3D: Learning To Generate Implicit Neural Assets in the Wild | Bokui Shen, Xinchen Yan, Charles R. Qi, Mahyar Najibi, Boyang Deng, Leonidas Guibas, Yin Zhou, Dragomir Anguelov | Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving. However, manually creating or re-creating real-world-like environments is difficult, expensive, and not scalable. Recent generative model techniques have shown promising progress to address such challenges by learning 3D assets using only plentiful 2D images -- but still suffer limitations as they leverage either human-curated image datasets or renderings from manually-created synthetic 3D environments. In this paper, we introduce GINA-3D, a generative model that uses real-world driving data from camera and LiDAR sensors to create photo-realistic 3D implicit neural assets of diverse vehicles and pedestrians. Compared to the existing image datasets, the real-world driving setting poses new challenges due to occlusions, lighting-variations and long-tail distributions. GINA-3D tackles these challenges by decoupling representation learning and generative modeling into two stages with a learned tri-plane latent structure, inspired by recent advances in generative modeling of images. To evaluate our approach, we construct a large-scale object-centric dataset containing over 520K images of vehicles and pedestrians from the Waymo Open Dataset, and a new set of 80K images of long-tail instances such as construction equipment, garbage trucks, and cable cars. We compare our model with existing approaches and demonstrate that it achieves state-of-the-art performance in quality and diversity for both generated images and geometries. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_GINA-3D_Learning_To_Generate_Implicit_Neural_Assets_in_the_Wild_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_GINA-3D_Learning_To_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_GINA-3D_Learning_To_Generate_Implicit_Neural_Assets_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_GINA-3D_Learning_To_Generate_Implicit_Neural_Assets_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
Consistent Direct Time-of-Flight Video Depth Super-Resolution | Zhanghao Sun, Wei Ye, Jinhui Xiong, Gyeongmin Choe, Jialiang Wang, Shuochen Su, Rakesh Ranjan | Direct time-of-flight (dToF) sensors are promising for next-generation on-device 3D sensing. However, limited by manufacturing capabilities in a compact module, the dToF data has low spatial resolution (e.g., 20x30 for iPhone dToF), and it requires a super-resolution step before being passed to downstream tasks. In this paper, we solve this super-resolution problem by fusing the low-resolution dToF data with the corresponding high-resolution RGB guidance. Unlike the conventional RGB-guided depth enhancement approaches which perform the fusion in a per-frame manner, we propose the first multi-frame fusion scheme to mitigate the spatial ambiguity resulting from the low-resolution dToF imaging. In addition, dToF sensors provide unique depth histogram information for each local patch, and we incorporate this dToF-specific feature in our network design to further alleviate spatial ambiguity. To evaluate our models on complex dynamic indoor environments and to provide a large-scale dToF sensor dataset, we introduce DyDToF, the first synthetic RGB-dToF video dataset that features dynamic objects and a realistic dToF simulator following the physical imaging process. We believe the methods and dataset are beneficial to a broad community as dToF depth sensing is becoming mainstream on mobile devices. Our code and data are publicly available. https://github.com/facebookresearch/DVSR/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Consistent_Direct_Time-of-Flight_Video_Depth_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Consistent_Direct_Time-of-Flight_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.08658 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Consistent_Direct_Time-of-Flight_Video_Depth_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Consistent_Direct_Time-of-Flight_Video_Depth_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Crossing the Gap: Domain Generalization for Image Captioning | Yuchen Ren, Zhendong Mao, Shancheng Fang, Yan Lu, Tong He, Hao Du, Yongdong Zhang, Wanli Ouyang | Existing image captioning methods are under the assumption that the training and testing data are from the same domain or that the data from the target domain (i.e., the domain that testing data lie in) are accessible. However, this assumption is invalid in real-world applications where the data from the target domain is inaccessible. In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process. We first construct a benchmark dataset for DGIC, which helps us to investigate models' domain generalization (DG) ability on unseen domains. With the support of the new benchmark, we further propose a new framework called language-guided semantic metric learning (LSML) for the DGIC setting. Experiments on multiple datasets demonstrate the challenge of the task and the effectiveness of our newly proposed benchmark and LSML framework. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_Crossing_the_Gap_Domain_Generalization_for_Image_Captioning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ren_Crossing_the_Gap_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Crossing_the_Gap_Domain_Generalization_for_Image_Captioning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Crossing_the_Gap_Domain_Generalization_for_Image_Captioning_CVPR_2023_paper.html | CVPR 2023 | null |
Probabilistic Prompt Learning for Dense Prediction | Hyeongjun Kwon, Taeyong Song, Somi Jeong, Jin Kim, Jinhyun Jang, Kwanghoon Sohn | Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approach results in limited performance for dense prediction tasks that require handling more complex and diverse objects, since a single and deterministic description cannot sufficiently represent the entire image. In this paper, we present a novel probabilistic prompt learning to fully exploit the vision-language knowledge in dense prediction tasks. First, we introduce learnable class-agnostic attribute prompts to describe universal attributes across the object class. The attributes are combined with class information and visual-context knowledge to define the class-specific textual distribution. Text representations are sampled and used to guide the dense prediction task using the probabilistic pixel-text matching loss, enhancing the stability and generalization capability of the proposed method. Extensive experiments on different dense prediction tasks and ablation studies demonstrate the effectiveness of our proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kwon_Probabilistic_Prompt_Learning_for_Dense_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kwon_Probabilistic_Prompt_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00779 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kwon_Probabilistic_Prompt_Learning_for_Dense_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kwon_Probabilistic_Prompt_Learning_for_Dense_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Learned Image Compression With Mixed Transformer-CNN Architectures | Jinming Liu, Heming Sun, Jiro Katto | Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages. Exploiting both advantages is a point worth exploring, which has two challenges: 1) how to effectively fuse the two methods? 2) how to achieve higher performance with a suitable complexity? In this paper, we propose an efficient parallel Transformer-CNN Mixture (TCM) block with a controllable complexity to incorporate the local modeling ability of CNN and the non-local modeling ability of transformers to improve the overall architecture of image compression models. Besides, inspired by the recent progress of entropy estimation models and attention modules, we propose a channel-wise entropy model with parameter-efficient swin-transformer-based attention (SWAtten) modules by using channel squeezing. Experimental results demonstrate our proposed method achieves state-of-the-art rate-distortion performances on three different resolution datasets (i.e., Kodak, Tecnick, CLIC Professional Validation) compared to existing LIC methods. The code is at https://github.com/jmliu206/LIC_TCM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Learned_Image_Compression_With_Mixed_Transformer-CNN_Architectures_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Learned_Image_Compression_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14978 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learned_Image_Compression_With_Mixed_Transformer-CNN_Architectures_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learned_Image_Compression_With_Mixed_Transformer-CNN_Architectures_CVPR_2023_paper.html | CVPR 2023 | null |
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