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Fair Scratch Tickets: Finding Fair Sparse Networks Without Weight Training | Pengwei Tang, Wei Yao, Zhicong Li, Yong Liu | Recent studies suggest that computer vision models come at the risk of compromising fairness. There are extensive works to alleviate unfairness in computer vision using pre-processing, in-processing, and post-processing methods. In this paper, we lead a novel fairness-aware learning paradigm for in-processing methods through the lens of the lottery ticket hypothesis (LTH) in the context of computer vision fairness. We randomly initialize a dense neural network and find appropriate binary masks for the weights to obtain fair sparse subnetworks without any weight training. Interestingly, to the best of our knowledge, we are the first to discover that such sparse subnetworks with inborn fairness exist in randomly initialized networks, achieving an accuracy-fairness trade-off comparable to that of dense neural networks trained with existing fairness-aware in-processing approaches. We term these fair subnetworks as Fair Scratch Tickets (FSTs). We also theoretically provide fairness and accuracy guarantees for them. In our experiments, we investigate the existence of FSTs on various datasets, target attributes, random initialization methods, sparsity patterns, and fairness surrogates. We also find that FSTs can transfer across datasets and investigate other properties of FSTs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Fair_Scratch_Tickets_Finding_Fair_Sparse_Networks_Without_Weight_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Fair_Scratch_Tickets_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Fair_Scratch_Tickets_Finding_Fair_Sparse_Networks_Without_Weight_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Fair_Scratch_Tickets_Finding_Fair_Sparse_Networks_Without_Weight_Training_CVPR_2023_paper.html | CVPR 2023 | null |
Generative Bias for Robust Visual Question Answering | Jae Won Cho, Dong-Jin Kim, Hyeonggon Ryu, In So Kweon | The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Various previous ensemble based debiasing methods have been proposed where an additional model is purposefully trained to be biased in order to train a robust target model. However, these methods compute the bias for a model simply from the label statistics of the training data or from single modal branches. In this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model directly from the target model, called GenB. In particular, GenB employs a generative network to learn the bias in the target model through a combination of the adversarial objective and knowledge distillation. We then debias our target model with GenB as a bias model, and show through extensive experiments the effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1, GQA-OOD, and VQA-CE, and show state-of-the-art results with the LXMERT architecture on VQA-CP2. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_Generative_Bias_for_Robust_Visual_Question_Answering_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.00690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Generative_Bias_for_Robust_Visual_Question_Answering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Generative_Bias_for_Robust_Visual_Question_Answering_CVPR_2023_paper.html | CVPR 2023 | null |
Data-Free Sketch-Based Image Retrieval | Abhra Chaudhuri, Ayan Kumar Bhunia, Yi-Zhe Song, Anjan Dutta | Rising concerns about privacy and anonymity preservation of deep learning models have facilitated research in data-free learning. Primarily based on data-free knowledge distillation, models developed in this area so far have only been able to operate in a single modality, performing the same kind of task as that of the teacher. For the first time, we propose Data-Free Sketch-Based Image Retrieval (DF-SBIR), a cross-modal data-free learning setting, where teachers trained for classification in a single modality have to be leveraged by students to learn a cross-modal metric-space for retrieval. The widespread availability of pre-trained classification models, along with the difficulty in acquiring paired photo-sketch datasets for SBIR justify the practicality of this setting. We present a methodology for DF-SBIR, which can leverage knowledge from models independently trained to perform classification on photos and sketches. We evaluate our model on the Sketchy, TU-Berlin, and QuickDraw benchmarks, designing a variety of baselines based on existing data-free learning literature, and observe that our method surpasses all of them by significant margins. Our method also achieves mAPs competitive with data-dependent approaches, all the while requiring no training data. Implementation is available at https://github.com/abhrac/data-free-sbir. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chaudhuri_Data-Free_Sketch-Based_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07775 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Object Manipulation via Object-Centric Neural Scattering Functions | Stephen Tian, Yancheng Cai, Hong-Xing Yu, Sergey Zakharov, Katherine Liu, Adrien Gaidon, Yunzhu Li, Jiajun Wu | Learned visual dynamics models have proven effective for robotic manipulation tasks. Yet, it remains unclear how best to represent scenes involving multi-object interactions. Current methods decompose a scene into discrete objects, yet they struggle with precise modeling and manipulation amid challenging lighting conditions since they only encode appearance tied with specific illuminations. In this work, we propose using object-centric neural scattering functions (OSFs) as object representations in a model-predictive control framework. OSFs model per-object light transport, enabling compositional scene re-rendering under object rearrangement and varying lighting conditions. By combining this approach with inverse parameter estimation and graph-based neural dynamics models, we demonstrate improved model-predictive control performance and generalization in compositional multi-object environments, even in previously unseen scenarios and harsh lighting conditions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_Multi-Object_Manipulation_via_Object-Centric_Neural_Scattering_Functions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_Multi-Object_Manipulation_via_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Multi-Object_Manipulation_via_Object-Centric_Neural_Scattering_Functions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Multi-Object_Manipulation_via_Object-Centric_Neural_Scattering_Functions_CVPR_2023_paper.html | CVPR 2023 | null |
The Wisdom of Crowds: Temporal Progressive Attention for Early Action Prediction | Alexandros Stergiou, Dima Damen | Early action prediction deals with inferring the ongoing action from partially-observed videos, typically at the outset of the video. We propose a bottleneck-based attention model that captures the evolution of the action, through progressive sampling over fine-to-coarse scales. Our proposed Temporal Progressive (TemPr) model is composed of multiple attention towers, one for each scale. The predicted action label is based on the collective agreement considering confidences of these towers. Extensive experiments over four video datasets showcase state-of-the-art performance on the task of Early Action Prediction across a range of encoder architectures. We demonstrate the effectiveness and consistency of TemPr through detailed ablations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Stergiou_The_Wisdom_of_Crowds_Temporal_Progressive_Attention_for_Early_Action_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Stergiou_The_Wisdom_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.13340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Stergiou_The_Wisdom_of_Crowds_Temporal_Progressive_Attention_for_Early_Action_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Stergiou_The_Wisdom_of_Crowds_Temporal_Progressive_Attention_for_Early_Action_CVPR_2023_paper.html | CVPR 2023 | null |
Invertible Neural Skinning | Yash Kant, Aliaksandr Siarohin, Riza Alp Guler, Menglei Chai, Jian Ren, Sergey Tulyakov, Igor Gilitschenski | Building animatable and editable models of clothed humans from raw 3D scans and poses is a challenging problem. Existing reposing methods suffer from the limited expressiveness of Linear Blend Skinning (LBS), require costly mesh extraction to generate each new pose, and typically do not preserve surface correspondences across different poses. In this work, we introduce Invertible Neural Skinning (INS) to address these shortcomings. To maintain correspondences, we propose a Pose-conditioned Invertible Network (PIN) architecture, which extends the LBS process by learning additional pose-varying deformations. Next, we combine PIN with a differentiable LBS module to build an expressive and end-to-end Invertible Neural Skinning (INS) pipeline. We demonstrate the strong performance of our method by outperforming the state-of-the-art reposing techniques on clothed humans and preserving surface correspondences, while being an order of magnitude faster. We also perform an ablation study, which shows the usefulness of our pose-conditioning formulation, and our qualitative results display that INS can rectify artefacts introduced by LBS well. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kant_Invertible_Neural_Skinning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kant_Invertible_Neural_Skinning_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.09227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kant_Invertible_Neural_Skinning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kant_Invertible_Neural_Skinning_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor | Hyeokjun Kweon, Sung-Hoon Yoon, Kuk-Jin Yoon | In Weakly Supervised Semantic Segmentation (WSSS), Class Activation Maps (CAMs) usually 1) do not cover the whole object and 2) be activated on irrelevant regions. To address the issues, we propose a novel WSSS framework via adversarial learning of a classifier and an image reconstructor. When an image is perfectly decomposed into class-wise segments, information (i.e., color or texture) of a single segment could not be inferred from the other segments. Therefore, inferability between the segments can represent the preciseness of segmentation. We quantify the inferability as a reconstruction quality of one segment from the other segments. If one segment could be reconstructed from the others, then the segment would be imprecise. To bring this idea into WSSS, we simultaneously train two models: a classifier generating CAMs that decompose an image into segments and a reconstructor that measures the inferability between the segments. As in GANs, while being alternatively trained in an adversarial manner, two networks provide positive feedback to each other. We verify the superiority of the proposed framework with extensive ablation studies. Our method achieves new state-of-the-art performances on both PASCAL VOC 2012 and MS COCO 2014. The code is available at https://github.com/sangrockEG/ACR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kweon_Weakly_Supervised_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.html | CVPR 2023 | null |
Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models | Qu Tang, Xiangyu Zhu, Zhen Lei, Zhaoxiang Zhang | The ability to discover abstract physical concepts and understand how they work in the world through observing lies at the core of human intelligence. The acquisition of this ability is based on compositionally perceiving the environment in terms of objects and relations in an unsupervised manner. Recent approaches learn object-centric representations and capture visually observable concepts of objects, e.g., shape, size, and location. In this paper, we take a step forward and try to discover and represent intrinsic physical concepts such as mass and charge. We introduce the PHYsical Concepts Inference NEtwork (PHYCINE), a system that infers physical concepts in different abstract levels without supervision. The key insights underlining PHYCINE are two-fold, commonsense knowledge emerges with prediction, and physical concepts of different abstract levels should be reasoned in a bottom-to-up fashion. Empirical evaluation demonstrates that variables inferred by our system work in accordance with the properties of the corresponding physical concepts. We also show that object representations containing the discovered physical concepts variables could help achieve better performance in causal reasoning tasks, i.e., COMPHY. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Intrinsic_Physical_Concepts_Discovery_With_Object-Centric_Predictive_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Intrinsic_Physical_Concepts_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01869 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Intrinsic_Physical_Concepts_Discovery_With_Object-Centric_Predictive_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Intrinsic_Physical_Concepts_Discovery_With_Object-Centric_Predictive_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition | Leming Guo, Wanli Xue, Qing Guo, Bo Liu, Kaihua Zhang, Tiantian Yuan, Shengyong Chen | Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9,20,25,36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thorough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distillation learning objective to aggregate the two types of context and the linguistic prior. The knowledge distillation enables the resultant one-branch temporal aggregation module to perceive local-global temporal and semantic context. This shallow temporal perception module structure facilitates spatial perception module learning. Extensive experiments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Distilling_Cross-Temporal_Contexts_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Automatic High Resolution Wire Segmentation and Removal | Mang Tik Chiu, Xuaner Zhang, Zijun Wei, Yuqian Zhou, Eli Shechtman, Connelly Barnes, Zhe Lin, Florian Kainz, Sohrab Amirghodsi, Humphrey Shi | Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely tedious and may take up to hours, especially on high-resolution photos where wires may span the entire space. In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds. We observe several unique challenges: wires are thin, lengthy, and sparse. These are rare properties of subjects that common segmentation tasks cannot handle, especially in high-resolution images. We thus propose a two-stage method that leverages both global and local context to accurately segment wires in high-resolution images efficiently, and a tile-based inpainting strategy to remove the wires given our predicted segmentation masks. We also introduce the first wire segmentation benchmark dataset, WireSegHR. Finally, we demonstrate quantitatively and qualitatively that our wire clean-up system enables fully automated wire removal for great generalization to various wire appearances. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chiu_Automatic_High_Resolution_Wire_Segmentation_and_Removal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chiu_Automatic_High_Resolution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chiu_Automatic_High_Resolution_Wire_Segmentation_and_Removal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chiu_Automatic_High_Resolution_Wire_Segmentation_and_Removal_CVPR_2023_paper.html | CVPR 2023 | null |
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning | Joshua C. Zhao, Ahmed Roushdy Elkordy, Atul Sharma, Yahya H. Ezzeldin, Salman Avestimehr, Saurabh Bagchi | Secure aggregation promises a heightened level of privacy in federated learning, maintaining that a server only has access to a decrypted aggregate update. Within this setting, linear layer leakage methods are the only data reconstruction attacks able to scale and achieve a high leakage rate regardless of the number of clients or batch size. This is done through increasing the size of an injected fully-connected (FC) layer. We show that this results in a resource overhead which grows larger with an increasing number of clients. We show that this resource overhead is caused by an incorrect perspective in all prior work that treats an attack on an aggregate update in the same way as an individual update with a larger batch size. Instead, by attacking the update from the perspective that aggregation is combining multiple individual updates, this allows the application of sparsity to alleviate resource overhead. We show that the use of sparsity can decrease the model size overhead by over 327x and the computation time by 3.34x compared to SOTA while maintaining equivalent total leakage rate, 77% even with 1000 clients in aggregation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_The_Resource_Problem_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14868 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration | Guofeng Mei, Hao Tang, Xiaoshui Huang, Weijie Wang, Juan Liu, Jian Zhang, Luc Van Gool, Qiang Wu | Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. To address these issues, we propose UDPReg, an unsupervised deep probabilistic registration framework for point clouds with partial overlaps. Specifically, we first adopt a network to learn posterior probability distributions of Gaussian mixture models (GMMs) from point clouds. To handle partial point cloud registration, we apply the Sinkhorn algorithm to predict the distribution-level correspondences under the constraint of the mixing weights of GMMs. To enable unsupervised learning, we design three distribution consistency-based losses: self-consistency, cross-consistency, and local contrastive. The self-consistency loss is formulated by encouraging GMMs in Euclidean and feature spaces to share identical posterior distributions. The cross-consistency loss derives from the fact that the points of two partially overlapping point clouds belonging to the same clusters share the cluster centroids. The cross-consistency loss allows the network to flexibly learn a transformation-invariant posterior distribution of two aligned point clouds. The local contrastive loss facilitates the network to extract discriminative local features. Our UDPReg achieves competitive performance on the 3DMatch/3DLoMatch and ModelNet/ModelLoNet benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mei_Unsupervised_Deep_Probabilistic_Approach_for_Partial_Point_Cloud_Registration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mei_Unsupervised_Deep_Probabilistic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13290 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Unsupervised_Deep_Probabilistic_Approach_for_Partial_Point_Cloud_Registration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Unsupervised_Deep_Probabilistic_Approach_for_Partial_Point_Cloud_Registration_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training | Dezhao Luo, Jiabo Huang, Shaogang Gong, Hailin Jin, Yang Liu | The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding. Without sufficient temporal boundary annotations, it is non-trivial to learn universal video-text alignments. In this work, we explore multi-modal correlations derived from large-scale image-text data to facilitate generalisable VMR. To address the limitations of image-text pre-training models on capturing the video changes, we propose a generic method, referred to as Visual-Dynamic Injection (VDI), to empower the model's understanding of video moments. Whilst existing VMR methods are focusing on building temporal-aware video features, being aware of the text descriptions about the temporal changes is also critical but originally overlooked in pre-training by matching static images with sentences. Therefore, we extract visual context and spatial dynamic information from video frames and explicitly enforce their alignments with the phrases describing video changes (e.g. verb). By doing so, the potentially relevant visual and motion patterns in videos are encoded in the corresponding text embeddings (injected) so to enable more accurate video-text alignments. We conduct extensive experiments on two VMR benchmark datasets (Charades-STA and ActivityNet-Captions) and achieve state-of-the-art performances. Especially, VDI yields notable advantages when being tested on the out-of-distribution splits where the testing samples involve novel scenes and vocabulary. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Towards_Generalisable_Video_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00040 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Adaptive Dense Event Stereo From the Image Domain | Hoonhee Cho, Jegyeong Cho, Kuk-Jin Yoon | Recently, event-based stereo matching has been studied due to its robustness in poor light conditions. However, existing event-based stereo networks suffer severe performance degradation when domains shift. Unsupervised domain adaptation (UDA) aims at resolving this problem without using the target domain ground-truth. However, traditional UDA still needs the input event data with ground-truth in the source domain, which is more challenging and costly to obtain than image data. To tackle this issue, we propose a novel unsupervised domain Adaptive Dense Event Stereo (ADES), which resolves gaps between the different domains and input modalities. The proposed ADES framework adapts event-based stereo networks from abundant image datasets with ground-truth on the source domain to event datasets without ground-truth on the target domain, which is a more practical setup. First, we propose a self-supervision module that trains the network on the target domain through image reconstruction, while an artifact prediction network trained on the source domain assists in removing intermittent artifacts in the reconstructed image. Secondly, we utilize the feature-level normalization scheme to align the extracted features along the epipolar line. Finally, we present the motion-invariant consistency module to impose the consistent output between the perturbed motion. Our experiments demonstrate that our approach achieves remarkable results in the adaptation ability of event-based stereo matching from the image domain. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cho_Learning_Adaptive_Dense_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.html | CVPR 2023 | null |
Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation | Chaohui Yu, Qiang Zhou, Jingliang Li, Jianlong Yuan, Zhibin Wang, Fan Wang | Modern incremental learning for semantic segmentation methods usually learn new categories based on dense annotations. Although achieve promising results, pixel-by-pixel labeling is costly and time-consuming. Weakly incremental learning for semantic segmentation (WILSS) is a novel and attractive task, which aims at learning to segment new classes from cheap and widely available image-level labels. Despite the comparable results, the image-level labels can not provide details to locate each segment, which limits the performance of WILSS. This inspires us to think how to improve and effectively utilize the supervision of new classes given image-level labels while avoiding forgetting old ones. In this work, we propose a novel and data-efficient framework for WILSS, named FMWISS. Specifically, we propose pre-training based co-segmentation to distill the knowledge of complementary foundation models for generating dense pseudo labels. We further optimize the noisy pseudo masks with a teacher-student architecture, where a plug-in teacher is optimized with a proposed dense contrastive loss. Moreover, we introduce memory-based copy-paste augmentation to improve the catastrophic forgetting problem of old classes. Extensive experiments on Pascal VOC and COCO datasets demonstrate the superior performance of our framework, e.g., FMWISS achieves 70.7% and 73.3% in the 15-5 VOC setting, outperforming the state-of-the-art method by 3.4% and 6.1%, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Foundation_Model_Drives_Weakly_Incremental_Learning_for_Semantic_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.14250 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Foundation_Model_Drives_Weakly_Incremental_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Foundation_Model_Drives_Weakly_Incremental_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing a Rose in Five Thousand Ways | Yunzhi Zhang, Shangzhe Wu, Noah Snavely, Jiajun Wu | What is a rose, visually? A rose comprises its intrinsics, including the distribution of geometry, texture, and material specific to its object category. With knowledge of these intrinsic properties, we may render roses of different sizes and shapes, in different poses, and under different lighting conditions. In this work, we build a generative model that learns to capture such object intrinsics from a single image, such as a photo of a bouquet. Such an image includes multiple instances of an object type. These instances all share the same intrinsics, but appear different due to a combination of variance within these intrinsics and differences in extrinsic factors, such as pose and illumination. Experiments show that our model successfully learns object intrinsics (distribution of geometry, texture, and material) for a wide range of objects, each from a single Internet image. Our method achieves superior results on multiple downstream tasks, including intrinsic image decomposition, shape and image generation, view synthesis, and relighting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Seeing_a_Rose_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos | Liao Wang, Qiang Hu, Qihan He, Ziyu Wang, Jingyi Yu, Tinne Tuytelaars, Lan Xu, Minye Wu | The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has inspired numerous attempts on dynamic scenes. Current techniques that utilize neural rendering for facilitating free-view videos (FVVs) are restricted to either offline rendering or are capable of processing only brief sequences with minimal motion. In this paper, we present a novel technique, Residual Radiance Field or ReRF, as a highly compact neural representation to achieve real-time FVV rendering on long-duration dynamic scenes. ReRF explicitly models the residual information between adjacent timestamps in the spatial-temporal feature space, with a global coordinate-based tiny MLP as the feature decoder. Specifically, ReRF employs a compact motion grid along with a residual feature grid to exploit inter-frame feature similarities. We show such a strategy can handle large motions without sacrificing quality. We further present a sequential training scheme to maintain the smoothness and the sparsity of the motion/residual grids. Based on ReRF, we design a special FVV codec that achieves three orders of magnitudes compression rate and provides a companion ReRF player to support online streaming of long-duration FVVs of dynamic scenes. Extensive experiments demonstrate the effectiveness of ReRF for compactly representing dynamic radiance fields, enabling an unprecedented free-viewpoint viewing experience in speed and quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Neural_Residual_Radiance_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.04452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation | Kehan Li, Zhennan Wang, Zesen Cheng, Runyi Yu, Yian Zhao, Guoli Song, Chang Liu, Li Yuan, Jie Chen | Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class-aware information that semantically identical pixel embeddings in the representation space gather together to form sophisticated concepts. However, leveraging the learned models to ascertain semantically consistent pixel groups or regions in the image is non-trivial since over/ under-clustering overwhelms the conceptualization procedure under various semantic distributions of different images. In this work, we investigate the pixel-level semantic aggregation in self-supervised ViT pre-trained models as image Segmentation and propose the Adaptive Conceptualization approach for USS, termed ACSeg. Concretely, we explicitly encode concepts into learnable prototypes and design the Adaptive Concept Generator (ACG), which adaptively maps these prototypes to informative concepts for each image. Meanwhile, considering the scene complexity of different images, we propose the modularity loss to optimize ACG independent of the concept number based on estimating the intensity of pixel pairs belonging to the same concept. Finally, we turn the USS task into classifying the discovered concepts in an unsupervised manner. Extensive experiments with state-of-the-art results demonstrate the effectiveness of the proposed ACSeg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ACSeg_Adaptive_Conceptualization_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ACSeg_Adaptive_Conceptualization_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2210.05944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ACSeg_Adaptive_Conceptualization_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ACSeg_Adaptive_Conceptualization_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds | Chen Yang, Peihao Li, Zanwei Zhou, Shanxin Yuan, Bingbing Liu, Xiaokang Yang, Weichao Qiu, Wei Shen | We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views. To address this issue, we utilize the holistic priors, including pseudo depth maps and view coverage information, from neural reconstruction to guide the learning of implicit neural representations of 3D indoor scenes. Concretely, an off-the-shelf neural reconstruction method is leveraged to generate a geometry scaffold. Then, two loss functions based on the holistic priors are proposed to improve the learning of NeRF: 1) A robust depth loss that can tolerate the error of the pseudo depth map to guide the geometry learning of NeRF; 2) A variance loss to regularize the variance of implicit neural representations to reduce the geometry and color ambiguity in the learning procedure. These two loss functions are modulated during NeRF optimization according to the view coverage information to reduce the negative influence brought by the view coverage imbalance. Extensive results demonstrate that our NeRFVS outperforms state-of-the-art view synthesis methods quantitatively and qualitatively on indoor scenes, achieving high-fidelity free navigation results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_NeRFVS_Neural_Radiance_Fields_for_Free_View_Synthesis_via_Geometry_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_NeRFVS_Neural_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06287 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_NeRFVS_Neural_Radiance_Fields_for_Free_View_Synthesis_via_Geometry_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_NeRFVS_Neural_Radiance_Fields_for_Free_View_Synthesis_via_Geometry_CVPR_2023_paper.html | CVPR 2023 | null |
Reproducible Scaling Laws for Contrastive Language-Image Learning | Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev | Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.com/LAION-AI/scaling-laws-openclip. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cherti_Reproducible_Scaling_Laws_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Similarity Metric Learning for RGB-Infrared Group Re-Identification | Jianghao Xiong, Jianhuang Lai | Group re-identification (G-ReID) aims to re-identify a group of people that is observed from non-overlapping camera systems. The existing literature has mainly addressed RGB-based problems, but RGB-infrared (RGB-IR) cross-modality matching problem has not been studied yet. In this paper, we propose a metric learning method Closest Permutation Matching (CPM) for RGB-IR G-ReID. We model each group as a set of single-person features which are extracted by MPANet, then we propose the metric Closest Permutation Distance (CPD) to measure the similarity between two sets of features. CPD is invariant with order changes of group members so that it solves the layout change problem in G-ReID. Furthermore, we introduce the problem of G-ReID without person labels. In the weak-supervised case, we design the Relation-aware Module (RAM) that exploits visual context and relations among group members to produce a modality-invariant order of features in each group, with which group member features within a set can be sorted to form a robust group representation against modality change. To support the study on RGB-IR G-ReID, we construct a new large-scale RGB-IR G-ReID dataset CM-Group. The dataset contains 15,440 RGB images and 15,506 infrared images of 427 groups and 1,013 identities. Extensive experiments on the new dataset demonstrate the effectiveness of the proposed models and the complexity of CM-Group. The code and dataset are available at: https://github.com/WhollyOat/CM-Group. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiong_Similarity_Metric_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-Time Mobile Telepresence | Yonggan Fu, Yuecheng Li, Chenghui Li, Jason Saragih, Peizhao Zhang, Xiaoliang Dai, Yingyan (Celine) Lin | Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one key bottleneck: the considerable computational expense needed to accurately infer facial expressions captured from headset-mounted cameras with a quality level that can match the realism of the avatar's human appearance. To this end, we propose a framework called Auto-CARD, which for the first time enables real-time and robust driving of Codec Avatars when exclusively using merely on-device computing resources. This is achieved by minimizing two sources of redundancy. First, we develop a dedicated neural architecture search technique called AVE-NAS for avatar encoding in AR/VR, which explicitly boosts both the searched architectures' robustness in the presence of extreme facial expressions and hardware friendliness on fast evolving AR/VR headsets. Second, we leverage the temporal redundancy in consecutively captured images during continuous rendering and develop a mechanism dubbed LATEX to skip the computation of redundant frames. Specifically, we first identify an opportunity from the linearity of the latent space derived by the avatar decoder and then propose to perform adaptive latent extrapolation for redundant frames. For evaluation, we demonstrate the efficacy of our Auto-CARD framework in real-time Codec Avatar driving settings, where we achieve a 5.05x speed-up on Meta Quest 2 while maintaining a comparable or even better animation quality than state-of-the-art avatar encoder designs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fu_Auto-CARD_Efficient_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.html | CVPR 2023 | null |
Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching | Paul Roetzer, Zorah Lähner, Florian Bernard | We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a shortest path in the product graph between both shapes, existing solutions heavily rely on unrealistic prior assumptions to avoid degenerate solutions (e.g. knowledge to which region of the 3D shape each point of the 2D contour is matched). To address this, we propose a novel 2D-3D shape matching formalism based on the conjugate product graph between the 2D contour and the 3D shape. Doing so allows us for the first time to consider higher-order costs, i.e. defined for edge chains, as opposed to costs defined for single edges. This offers substantially more flexibility, which we utilise to incorporate a local rigidity prior. By doing so, we effectively circumvent degenerate solutions and thereby obtain smoother and more realistic matchings, even when using only a one-dimensional feature descriptor. Overall, our method finds globally optimal and continuous 2D-3D matchings, has the same asymptotic complexity as previous solutions, produces state-of-the-art results for shape matching and is even capable of matching partial shapes. Our code is publicly available (https://github.com/paul0noah/sm-2D3D). | https://openaccess.thecvf.com/content/CVPR2023/papers/Roetzer_Conjugate_Product_Graphs_for_Globally_Optimal_2D-3D_Shape_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Roetzer_Conjugate_Product_Graphs_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Roetzer_Conjugate_Product_Graphs_for_Globally_Optimal_2D-3D_Shape_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Roetzer_Conjugate_Product_Graphs_for_Globally_Optimal_2D-3D_Shape_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery | Sheng Zhang, Salman Khan, Zhiqiang Shen, Muzammal Naseer, Guangyi Chen, Fahad Shahbaz Khan | Although existing semi-supervised learning models achieve remarkable success in learning with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled from novel semantic classes due to their closed-set assumption. In this work, we target a pragmatic but under-explored Generalized Novel Category Discovery (GNCD) setting. The GNCD setting aims to categorize unlabeled training data coming from known and novel classes by leveraging the information of partially labeled known classes. We propose a two-stage Contrastive Affinity Learning method with auxiliary visual Prompts, dubbed PromptCAL, to address this challenging problem. Our approach discovers reliable pairwise sample affinities to learn better semantic clustering of both known and novel classes for the class token and visual prompts. First, we propose a discriminative prompt regularization loss to reinforce semantic discriminativeness of prompt-adapted pre-trained vision transformer for refined affinity relationships. Besides, we propose contrastive affinity learning to calibrate semantic representations based on our iterative semi-supervised affinity graph generation method for semantically-enhanced supervision. Extensive experimental evaluation demonstrates that our PromptCAL method is more effective in discovering novel classes even with limited annotations and surpasses the current state-of-the-art on generic and fine-grained benchmarks (e.g., with nearly 11% gain on CUB-200, and 9% on ImageNet-100) on overall accuracy. Our code will be released to the public. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_PromptCAL_Contrastive_Affinity_Learning_via_Auxiliary_Prompts_for_Generalized_Novel_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_PromptCAL_Contrastive_Affinity_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05590 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PromptCAL_Contrastive_Affinity_Learning_via_Auxiliary_Prompts_for_Generalized_Novel_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PromptCAL_Contrastive_Affinity_Learning_via_Auxiliary_Prompts_for_Generalized_Novel_CVPR_2023_paper.html | CVPR 2023 | null |
Train/Test-Time Adaptation With Retrieval | Luca Zancato, Alessandro Achille, Tian Yu Liu, Matthew Trager, Pramuditha Perera, Stefano Soatto | We introduce Train/Test-Time Adaptation with Retrieval (T3AR), a method to adapt models both at train and test time by means of a retrieval module and a searchable pool of external samples. Before inference, T3AR adapts a given model to the downstream task using refined pseudo-labels and a self-supervised contrastive objective function whose noise distribution leverages retrieved real samples to improve feature adaptation on the target data manifold. The retrieval of real images is key to T3AR since it does not rely solely on synthetic data augmentations to compensate for the lack of adaptation data, as typically done by other adaptation algorithms. Furthermore, thanks to the retrieval module, our method gives the user or service provider the possibility to improve model adaptation on the downstream task by incorporating further relevant data or to fully remove samples that may no longer be available due to changes in user preference after deployment. First, we show that T3AR can be used at training time to improve downstream fine-grained classification over standard fine-tuning baselines, and the fewer the adaptation data the higher the relative improvement (up to 13%). Second, we apply T3AR for test-time adaptation and show that exploiting a pool of external images at test-time leads to more robust representations over existing methods on DomainNet-126 and VISDA-C, especially when few adaptation data are available (up to 8%). | https://openaccess.thecvf.com/content/CVPR2023/papers/Zancato_TrainTest-Time_Adaptation_With_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zancato_TrainTest-Time_Adaptation_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14333 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zancato_TrainTest-Time_Adaptation_With_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zancato_TrainTest-Time_Adaptation_With_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
ProxyFormer: Proxy Alignment Assisted Point Cloud Completion With Missing Part Sensitive Transformer | Shanshan Li, Pan Gao, Xiaoyang Tan, Mingqiang Wei | Problems such as equipment defects or limited viewpoints will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones plays an vital role in many practical tasks, and one of the keys lies in the prediction of the missing part. In this paper, we propose a novel point cloud completion approach namely ProxyFormer that divides point clouds into existing (input) and missing (to be predicted) parts and each part communicates information through its proxies. Specifically, we fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies. Then, in order to better perceive the position of missing points, we design a missing part sensitive transformer, which converts random normal distribution into reasonable position information, and uses proxy alignment to refine the missing proxies. It makes the predicted point proxies more sensitive to the features and positions of the missing part, and thus makes these proxies more suitable for subsequent coarse-to-fine processes. Experimental results show that our method outperforms state-of-the-art completion networks on several benchmark datasets and has the fastest inference speed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ProxyFormer_Proxy_Alignment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14435 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.html | CVPR 2023 | null |
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners | Zitian Chen, Yikang Shen, Mingyu Ding, Zhenfang Chen, Hengshuang Zhao, Erik G. Learned-Miller, Chuang Gan | Optimization in multi-task learning (MTL) is more challenging than single-task learning (STL), as the gradient from different tasks can be contradictory. When tasks are related, it can be beneficial to share some parameters among them (cooperation). However, some tasks require additional parameters with expertise in a specific type of data or discrimination (specialization). To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad'). This structure allows us to formalize cooperation and specialization as the process of matching experts and tasks. We optimize this matching process during the training of a single model. Specifically, we incorporate mixture of experts (MoE) layers into a transformer model, with a new loss that incorporates the mutual dependence between tasks and experts. As a result, only a small set of experts are activated for each task. This prevents the sharing of the entire backbone model between all tasks, which strengthens the model, especially when the training set size and the number of tasks scale up. More interestingly, for each task, we can extract the small set of experts as a standalone model that maintains the same performance as the large model. Extensive experiments on the Taskonomy dataset with 13 vision tasks and the PASCALContext dataset with 5 vision tasks show the superiority of our approach. The project page can be accessed at https://vis-www.cs.umass.edu/mod-squad. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Mod-Squad_Designing_Mixtures_of_Experts_As_Modular_Multi-Task_Learners_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Mod-Squad_Designing_Mixtures_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Mod-Squad_Designing_Mixtures_of_Experts_As_Modular_Multi-Task_Learners_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Mod-Squad_Designing_Mixtures_of_Experts_As_Modular_Multi-Task_Learners_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Customized Visual Models With Retrieval-Augmented Knowledge | Haotian Liu, Kilho Son, Jianwei Yang, Ce Liu, Jianfeng Gao, Yong Jae Lee, Chunyuan Li | Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure broad concept coverage, followed by expensive pre-training to feed all the knowledge into model weights. Alternatively, we propose REACT, REtrieval-Augmented CusTomization, a framework to acquire the relevant web knowledge to build customized visual models for target domains. We retrieve the most relevant image-text pairs ( 3% of CLIP pre-training data) from the web-scale database as external knowledge and propose to customize the model by only training new modularized blocks while freezing all the original weights. The effectiveness of REACT is demonstrated via extensive experiments on classification, retrieval, detection and segmentation tasks, including zero, few, and full-shot settings. Particularly, on the zero-shot classification task, compared with CLIP, it achieves up to 5.4% improvement on ImageNet and 3.7% on the ELEVATER benchmark (20 datasets). | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Learning_Customized_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07094 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Realism Image Compression With a Conditional Generator | Eirikur Agustsson, David Minnen, George Toderici, Fabian Mentzer | By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models. However, previous methods do not explicitly control how much detail is synthesized, which results in a common criticism of these methods: users might be worried that a misleading reconstruction far from the input image is generated. In this work, we alleviate these concerns by training a decoder that can bridge the two regimes and navigate the distortion-realism trade-off. From a single compressed representation, the receiver can decide to either reconstruct a low mean squared error reconstruction that is close to the input, a realistic reconstruction with high perceptual quality, or anything in between. With our method, we set a new state-of-the-art in distortion-realism, pushing the frontier of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before. | https://openaccess.thecvf.com/content/CVPR2023/papers/Agustsson_Multi-Realism_Image_Compression_With_a_Conditional_Generator_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Agustsson_Multi-Realism_Image_Compression_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.13824 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Agustsson_Multi-Realism_Image_Compression_With_a_Conditional_Generator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Agustsson_Multi-Realism_Image_Compression_With_a_Conditional_Generator_CVPR_2023_paper.html | CVPR 2023 | null |
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks | Jierun Chen, Shiu-hong Kao, Hao He, Weipeng Zhuo, Song Wen, Chul-Ho Lee, S.-H. Gary Chan | To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does not necessarily lead to a similar level of reduction in latency. This mainly stems from inefficiently low floating-point operations per second (FLOPS). To achieve faster networks, we revisit popular operators and demonstrate that such low FLOPS is mainly due to frequent memory access of the operators, especially the depthwise convolution. We hence propose a novel partial convolution (PConv) that extracts spatial features more efficiently, by cutting down redundant computation and memory access simultaneously. Building upon our PConv, we further propose FasterNet, a new family of neural networks, which attains substantially higher running speed than others on a wide range of devices, without compromising on accuracy for various vision tasks. For example, on ImageNet-1k, our tiny FasterNet-T0 is 2.8x, 3.3x, and 2.4x faster than MobileViT-XXS on GPU, CPU, and ARM processors, respectively, while being 2.9% more accurate. Our large FasterNet-L achieves impressive 83.5% top-1 accuracy, on par with the emerging Swin-B, while having 36% higher inference throughput on GPU, as well as saving 37% compute time on CPU. Code is available at https://github.com/JierunChen/FasterNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Run_Dont_Walk_Chasing_Higher_FLOPS_for_Faster_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Run_Dont_Walk_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Run_Dont_Walk_Chasing_Higher_FLOPS_for_Faster_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Run_Dont_Walk_Chasing_Higher_FLOPS_for_Faster_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance | Xianmin Xu, Yuxin Lin, Haoyang Zhou, Chong Zeng, Yaxin Yu, Kun Zhou, Hongzhi Wu | We propose a unified structured light, consisting of an LED array and an LCD mask, for high-quality acquisition of both shape and reflectance from a single view. For geometry, one LED projects a set of learned mask patterns to accurately encode spatial information; the decoded results from multiple LEDs are then aggregated to produce a final depth map. For appearance, learned light patterns are cast through a transparent mask to efficiently probe angularly-varying reflectance. Per-point BRDF parameters are differentiably optimized with respect to corresponding measurements, and stored in texture maps as the final reflectance. We establish a differentiable pipeline for the joint capture to automatically optimize both the mask and light patterns towards optimal acquisition quality. The effectiveness of our light is demonstrated with a wide variety of physical objects. Our results compare favorably with state-of-the-art techniques. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_A_Unified_Spatial-Angular_Structured_Light_for_Single-View_Acquisition_of_Shape_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_A_Unified_Spatial-Angular_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_A_Unified_Spatial-Angular_Structured_Light_for_Single-View_Acquisition_of_Shape_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_A_Unified_Spatial-Angular_Structured_Light_for_Single-View_Acquisition_of_Shape_CVPR_2023_paper.html | CVPR 2023 | null |
Best of Both Worlds: Multimodal Contrastive Learning With Tabular and Imaging Data | Paul Hager, Martin J. Menten, Daniel Rueckert | Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necessity for unsupervised methods that can pretrain multimodally and predict unimodally has risen. To address these needs, we propose the first self-supervised contrastive learning framework that takes advantage of images and tabular data to train unimodal encoders. Our solution combines SimCLR and SCARF, two leading contrastive learning strategies, and is simple and effective. In our experiments, we demonstrate the strength of our framework by predicting risks of myocardial infarction and coronary artery disease (CAD) using cardiac MR images and 120 clinical features from 40,000 UK Biobank subjects. Furthermore, we show the generalizability of our approach to natural images using the DVM car advertisement dataset. We take advantage of the high interpretability of tabular data and through attribution and ablation experiments find that morphometric tabular features, describing size and shape, have outsized importance during the contrastive learning process and improve the quality of the learned embeddings. Finally, we introduce a novel form of supervised contrastive learning, label as a feature (LaaF), by appending the ground truth label as a tabular feature during multimodal pretraining, outperforming all supervised contrastive baselines. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hager_Best_of_Both_Worlds_Multimodal_Contrastive_Learning_With_Tabular_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hager_Best_of_Both_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hager_Best_of_Both_Worlds_Multimodal_Contrastive_Learning_With_Tabular_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hager_Best_of_Both_Worlds_Multimodal_Contrastive_Learning_With_Tabular_and_CVPR_2023_paper.html | CVPR 2023 | null |
On the Difficulty of Unpaired Infrared-to-Visible Video Translation: Fine-Grained Content-Rich Patches Transfer | Zhenjie Yu, Shuang Li, Yirui Shen, Chi Harold Liu, Shuigen Wang | Explicit visible videos can provide sufficient visual information and facilitate vision applications. Unfortunately, the image sensors of visible cameras are sensitive to light conditions like darkness or overexposure. To make up for this, recently, infrared sensors capable of stable imaging have received increasing attention in autonomous driving and monitoring. However, most prosperous vision models are still trained on massive clear visible data, facing huge visual gaps when deploying to infrared imaging scenarios. In such cases, transferring the infrared video to a distinct visible one with fine-grained semantic patterns is a worthwhile endeavor. Previous works improve the outputs by equally optimizing each patch on the translated visible results, which is unfair for enhancing the details on content-rich patches due to the long-tail effect of pixel distribution. Here we propose a novel CPTrans framework to tackle the challenge via balancing gradients of different patches, achieving the fine-grained Content-rich Patches Transferring. Specifically, the content-aware optimization module encourages model optimization along gradients of target patches, ensuring the improvement of visual details. Additionally, the content-aware temporal normalization module enforces the generator to be robust to the motions of target patches. Moreover, we extend the existing dataset InfraredCity to more challenging adverse weather conditions (rain and snow), dubbed as InfraredCity-Adverse. Extensive experiments show that the proposed CPTrans achieves state-of-the-art performance under diverse scenes while requiring less training time than competitive methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_On_the_Difficulty_of_Unpaired_Infrared-to-Visible_Video_Translation_Fine-Grained_Content-Rich_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_On_the_Difficulty_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_On_the_Difficulty_of_Unpaired_Infrared-to-Visible_Video_Translation_Fine-Grained_Content-Rich_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_On_the_Difficulty_of_Unpaired_Infrared-to-Visible_Video_Translation_Fine-Grained_Content-Rich_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Images Are Counterfactual Samples for Robust Fine-Tuning | Yao Xiao, Ziyi Tang, Pengxu Wei, Cong Liu, Liang Lin | Deep learning models are challenged by the distribution shift between the training data and test data. Recently, the large models pre-trained on diverse data have demonstrated unprecedented robustness to various distribution shifts. However, fine-tuning these models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. Existing methods for tackling this trade-off do not explicitly address the OOD robustness problem. In this paper, based on causal analysis of the aforementioned problems, we propose a novel fine-tuning method, which uses masked images as counterfactual samples that help improve the robustness of the fine-tuning model. Specifically, we mask either the semantics-related or semantics-unrelated patches of the images based on class activation map to break the spurious correlation, and refill the masked patches with patches from other images. The resulting counterfactual samples are used in feature-based distillation with the pre-trained model. Extensive experiments verify that regularizing the fine-tuning with the proposed masked images can achieve a better trade-off between ID and OOD performance, surpassing previous methods on the OOD performance. Our code is available at https://github.com/Coxy7/robust-finetuning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_Masked_Images_Are_Counterfactual_Samples_for_Robust_Fine-Tuning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiao_Masked_Images_Are_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Masked_Images_Are_Counterfactual_Samples_for_Robust_Fine-Tuning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Masked_Images_Are_Counterfactual_Samples_for_Robust_Fine-Tuning_CVPR_2023_paper.html | CVPR 2023 | null |
StepFormer: Self-Supervised Step Discovery and Localization in Instructional Videos | Nikita Dvornik, Isma Hadji, Ran Zhang, Konstantinos G. Derpanis, Richard P. Wildes, Allan D. Jepson | Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, with most of the video being irrelevant to the procedure. This motivates the need to temporally localize the instruction steps in such videos, i.e. the task called key-step localization. Traditional methods for key-step localization require video-level human annotations and thus do not scale to large datasets. In this work, we tackle the problem with no human supervision and introduce StepFormer, a self-supervised model that discovers and localizes instruction steps in a video. StepFormer is a transformer decoder that attends to the video with learnable queries, and produces a sequence of slots capturing the key-steps in the video. We train our system on a large dataset of instructional videos, using their automatically-generated subtitles as the only source of supervision. In particular, we supervise our system with a sequence of text narrations using an order-aware loss function that filters out irrelevant phrases. We show that our model outperforms all previous unsupervised and weakly-supervised approaches on step detection and localization by a large margin on three challenging benchmarks. Moreover, our model demonstrates an emergent property to solve zero-shot multi-step localization and outperforms all relevant baselines at this task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dvornik_StepFormer_Self-Supervised_Step_Discovery_and_Localization_in_Instructional_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dvornik_StepFormer_Self-Supervised_Step_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.13265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dvornik_StepFormer_Self-Supervised_Step_Discovery_and_Localization_in_Instructional_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dvornik_StepFormer_Self-Supervised_Step_Discovery_and_Localization_in_Instructional_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations | Yiwu Zhong, Licheng Yu, Yang Bai, Shangwen Li, Xueting Yan, Yin Li | The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification (+2.8% / +3.3% on COIN / EPIC-Kitchens) and step forecasting (+7.4% on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and forecasting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Learning_Procedure-Aware_Video_Representation_From_Instructional_Videos_and_Their_Narrations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Learning_Procedure-Aware_Video_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17839 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Learning_Procedure-Aware_Video_Representation_From_Instructional_Videos_and_Their_Narrations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Learning_Procedure-Aware_Video_Representation_From_Instructional_Videos_and_Their_Narrations_CVPR_2023_paper.html | CVPR 2023 | null |
Open Vocabulary Semantic Segmentation With Patch Aligned Contrastive Learning | Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed, Rui Wang, Ashish Shah, Philip H.S. Torr, Ser-Nam Lim | We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of the vision encoder and the CLS token of the text encoder. With such an alignment, a model can identify regions of an image corresponding to a given text input, and therefore transfer seamlessly to the task of open vocabulary semantic segmentation without requiring any segmentation annotations during training. Using pre-trained CLIP encoders with PACL, we are able to set the state-of-the-art on the task of open vocabulary zero-shot segmentation on 4 different segmentation benchmarks: Pascal VOC, Pascal Context, COCO Stuff and ADE20K. Furthermore, we show that PACL is also applicable to image-level predictions and when used with a CLIP backbone, provides a general improvement in zero-shot classification accuracy compared to CLIP, across a suite of 12 image classification datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mukhoti_Open_Vocabulary_Semantic_Segmentation_With_Patch_Aligned_Contrastive_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mukhoti_Open_Vocabulary_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04994 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mukhoti_Open_Vocabulary_Semantic_Segmentation_With_Patch_Aligned_Contrastive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mukhoti_Open_Vocabulary_Semantic_Segmentation_With_Patch_Aligned_Contrastive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP the Gap: A Single Domain Generalization Approach for Object Detection | Vidit Vidit, Martin Engilberge, Mathieu Salzmann | Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remains almost non-existent. To address the challenges of simultaneously learning robust object localization and representation, we propose to leverage a pre-trained vision-language model to introduce semantic domain concepts via textual prompts. We achieve this via a semantic augmentation strategy acting on the features extracted by the detector backbone, as well as a text-based classification loss. Our experiments evidence the benefits of our approach, outperforming by 10% the only existing SDG object detection method, Single-DGOD[49], on their own diverse weather-driving benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Vidit_CLIP_the_Gap_A_Single_Domain_Generalization_Approach_for_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vidit_CLIP_the_Gap_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05499 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_CLIP_the_Gap_A_Single_Domain_Generalization_Approach_for_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_CLIP_the_Gap_A_Single_Domain_Generalization_Approach_for_Object_CVPR_2023_paper.html | CVPR 2023 | null |
Co-Training 2L Submodels for Visual Recognition | Hugo Touvron, Matthieu Cord, Maxime Oquab, Piotr Bojanowski, Jakob Verbeek, Hervé Jégou | This paper introduces submodel co-training, a regularization method related to co-training, self-distillation and stochastic depth. Given a neural network to be trained, for each sample we implicitly instantiate two altered networks, "submodels", with stochastic depth: i.e. activating only a subset of the layers and skipping others. Each network serves as a soft teacher to the other, by providing a cross-entropy loss that complements the regular softmax cross-entropy loss provided by the one-hot label. Our approach, dubbed "cosub", uses a single set of weights, and does not involve a pre-trained external model or temporal averaging. Experimentally, we show that submodel co-training is effective to train backbones for recognition tasks such as image classification and semantic segmentation, and that our approach is compatible with multiple recent architectures, including RegNet, PiT, and Swin. We report new state-of-the-art results for vision transformers trained on ImageNet only. For instance, a ViT-B pre-trained with cosub on Imagenet-21k achieves 87.4% top-1 acc. on Imagenet-val. | https://openaccess.thecvf.com/content/CVPR2023/papers/Touvron_Co-Training_2L_Submodels_for_Visual_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Touvron_Co-Training_2L_Submodels_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Touvron_Co-Training_2L_Submodels_for_Visual_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Touvron_Co-Training_2L_Submodels_for_Visual_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks | HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch, Yitong Li, Yannick Verdie, Jifei Song, Yiren Zhou, Anil Armagan, Slobodan Ilic, Aleš Leonardis, Nassir Navab, Benjamin Busam | Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problematic for structure from motion and stereo, reflective material poses issues for active sensing, and distances for translucent objects are intricate to measure with existing hardware. Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities. These effects remain unnoticed if the sensor measurement is considered as ground truth during the evaluation. This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction. We rigorously show the significant impact of sensor characteristics on the learned predictions and notice generalisation issues arising from various technologies in everyday household environments. For evaluation, we introduce a carefully designed dataset comprising measurements from commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular RGB+P. Our study quantifies the considerable sensor noise impact and paves the way to improved dense vision estimates and targeted data fusion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jung_On_the_Importance_of_Accurate_Geometry_Data_for_Dense_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jung_On_the_Importance_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14840 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_On_the_Importance_of_Accurate_Geometry_Data_for_Dense_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_On_the_Importance_of_Accurate_Geometry_Data_for_Dense_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Camouflaged Instance Segmentation via Explicit De-Camouflaging | Naisong Luo, Yuwen Pan, Rui Sun, Tianzhu Zhang, Zhiwei Xiong, Feng Wu | Camouflaged Instance Segmentation (CIS) aims at predicting the instance-level masks of camouflaged objects, which are usually the animals in the wild adapting their appearance to match the surroundings. Previous instance segmentation methods perform poorly on this task as they are easily disturbed by the deceptive camouflage. To address these challenges, we propose a novel De-camouflaging Network (DCNet) including a pixel-level camouflage decoupling module and an instance-level camouflage suppression module. The proposed DCNet enjoys several merits. First, the pixel-level camouflage decoupling module can extract camouflage characteristics based on the Fourier transformation. Then a difference attention mechanism is proposed to eliminate the camouflage characteristics while reserving target object characteristics in the pixel feature. Second, the instance-level camouflage suppression module can aggregate rich instance information from pixels by use of instance prototypes. To mitigate the effect of background noise during segmentation, we introduce some reliable reference points to build a more robust similarity measurement. With the aid of these two modules, our DCNet can effectively model de-camouflaging and achieve accurate segmentation for camouflaged instances. Extensive experimental results on two benchmarks demonstrate that our DCNet performs favorably against state-of-the-art CIS methods, e.g., with more than 5% performance gains on COD10K and NC4K datasets in average precision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Camouflaged_Instance_Segmentation_via_Explicit_De-Camouflaging_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Camouflaged_Instance_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Camouflaged_Instance_Segmentation_via_Explicit_De-Camouflaging_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Camouflaged_Instance_Segmentation_via_Explicit_De-Camouflaging_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding Masked Autoencoders via Hierarchical Latent Variable Models | Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang | Masked autoencoder (MAE), a simple and effective self-supervised learning framework based on the reconstruction of masked image regions, has recently achieved prominent success in a variety of vision tasks. Despite the emergence of intriguing empirical observations on MAE, a theoretically principled understanding is still lacking. In this work, we formally characterize and justify existing empirical insights and provide theoretical guarantees of MAE. We formulate the underlying data-generating process as a hierarchical latent variable model, and show that under reasonable assumptions, MAE provably identifies a set of latent variables in the hierarchical model, explaining why MAE can extract high-level information from pixels. Further, we show how key hyperparameters in MAE (the masking ratio and the patch size) determine which true latent variables to be recovered, therefore influencing the level of semantic information in the representation. Specifically, extremely large or small masking ratios inevitably lead to low-level representations. Our theory offers coherent explanations of existing empirical observations and provides insights for potential empirical improvements and fundamental limitations of the masked-reconstruction paradigm. We conduct extensive experiments to validate our theoretical insights. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kong_Understanding_Masked_Autoencoders_via_Hierarchical_Latent_Variable_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kong_Understanding_Masked_Autoencoders_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Understanding_Masked_Autoencoders_via_Hierarchical_Latent_Variable_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Understanding_Masked_Autoencoders_via_Hierarchical_Latent_Variable_Models_CVPR_2023_paper.html | CVPR 2023 | null |
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance | Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, Angjoo Kanazawa | We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses d-choose-2 planes to represent a d-dimensional scene, providing a seamless way to go from static (d=3) to dynamic (d=4) scenes. This planar factorization makes adding dimension-specific priors easy, e.g. temporal smoothness and multi-resolution spatial structure, and induces a natural decomposition of static and dynamic components of a scene. We use a linear feature decoder with a learned color basis that yields similar performance as a nonlinear black-box MLP decoder. Across a range of synthetic and real, static and dynamic, fixed and varying appearance scenes, k-planes yields competitive and often state-of-the-art reconstruction fidelity with low memory usage, achieving 1000x compression over a full 4D grid, and fast optimization with a pure PyTorch implementation. For video results and code, please see sarafridov.github.io/K-Planes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fridovich-Keil_K-Planes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fridovich-Keil_K-Planes_Explicit_Radiance_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fridovich-Keil_K-Planes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fridovich-Keil_K-Planes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation Learning | Kaiyou Song, Jin Xie, Shan Zhang, Zimeng Luo | Self-supervised learning (SSL) has made remarkable progress in visual representation learning. Some studies combine SSL with knowledge distillation (SSL-KD) to boost the representation learning performance of small models. In this study, we propose a Multi-mode Online Knowledge Distillation method (MOKD) to boost self-supervised visual representation learning. Different from existing SSL-KD methods that transfer knowledge from a static pre-trained teacher to a student, in MOKD, two different models learn collaboratively in a self-supervised manner. Specifically, MOKD consists of two distillation modes: self-distillation and cross-distillation modes. Among them, self-distillation performs self-supervised learning for each model independently, while cross-distillation realizes knowledge interaction between different models. In cross-distillation, a cross-attention feature search strategy is proposed to enhance the semantic feature alignment between different models. As a result, the two models can absorb knowledge from each other to boost their representation learning performance. Extensive experimental results on different backbones and datasets demonstrate that two heterogeneous models can benefit from MOKD and outperform their independently trained baseline. In addition, MOKD also outperforms existing SSL-KD methods for both the student and teacher models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Multi-Mode_Online_Knowledge_Distillation_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_Multi-Mode_Online_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06461 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Multi-Mode_Online_Knowledge_Distillation_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Multi-Mode_Online_Knowledge_Distillation_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Unbalanced Optimal Transport: A Unified Framework for Object Detection | Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool | During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies include matching to the closest ground truth box (mostly used in combination with anchors), or matching via the Hungarian algorithm (mostly used in anchor-free methods). Each of these strategies comes with its own properties, underlying losses, and heuristics. We show how Unbalanced Optimal Transport unifies these different approaches and opens a whole continuum of methods in between. This allows for a finer selection of the desired properties. Experimentally, we show that training an object detection model with Unbalanced Optimal Transport is able to reach the state-of-the-art both in terms of Average Precision and Average Recall as well as to provide a faster initial convergence. The approach is well suited for GPU implementation, which proves to be an advantage for large-scale models. | https://openaccess.thecvf.com/content/CVPR2023/papers/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/De_Plaen_Unbalanced_Optimal_Transport_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Viewpoint Equivariance for Multi-View 3D Object Detection | Dian Chen, Jie Li, Vitor Guizilini, Rares Andrei Ambrus, Adrien Gaidon | 3D object detection from visual sensors is a cornerstone capability of robotic systems. State-of-the-art methods focus on reasoning and decoding object bounding boxes from multi-view camera input. In this work we gain intuition from the integral role of multi-view consistency in 3D scene understanding and geometric learning. To this end, we introduce VEDet, a novel 3D object detection framework that exploits 3D multi-view geometry to improve localization through viewpoint awareness and equivariance. VEDet leverages a query-based transformer architecture and encodes the 3D scene by augmenting image features with positional encodings from their 3D perspective geometry. We design view-conditioned queries at the output level, which enables the generation of multiple virtual frames during training to learn viewpoint equivariance by enforcing multi-view consistency. The multi-view geometry injected at the input level as positional encodings and regularized at the loss level provides rich geometric cues for 3D object detection, leading to state-of-the-art performance on the nuScenes benchmark. The code and model are made available at https://github.com/TRI-ML/VEDet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Viewpoint_Equivariance_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Viewpoint_Equivariance_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14548 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Viewpoint_Equivariance_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Viewpoint_Equivariance_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Photo Pre-Training, but for Sketch | Ke Li, Kaiyue Pang, Yi-Zhe Song | The sketch community has faced up to its unique challenges over the years, that of data scarcity however still remains the most significant to date. This lack of sketch data has imposed on the community a few "peculiar" design choices -- the most representative of them all is perhaps the coerced utilisation of photo-based pre-training (i.e., no sketch), for many core tasks that otherwise dictates specific sketch understanding. In this paper, we ask just the one question -- can we make such photo-based pre-training, to actually benefit sketch? Our answer lies in cultivating the topology of photo data learned at pre-training, and use that as a "free" source of supervision for downstream sketch tasks. In particular, we use fine-grained sketch-based image retrieval (FG-SBIR), one of the most studied and data-hungry sketch tasks, to showcase our new perspective on pre-training. In this context, the topology-informed supervision learned from photos act as a constraint that take effect at every fine-tuning step -- neighbouring photos in the pre-trained model remain neighbours under each FG-SBIR updates. We further portray this neighbourhood consistency constraint as a photo ranking problem and formulate it into a neat cross-modal triplet loss. We also show how this target is better leveraged as a meta objective rather than optimised in parallel with the main FG-SBIR objective. With just this change on pre-training, we beat all previously published results on all five product-level FG-SBIR benchmarks with significant margins (sometimes >10%). And the most beautiful thing, as we note, is such gigantic leap is made possible with just a few extra lines of code! Our implementation is available at https://github.com/KeLi-SketchX/Photo-Pre-Training-But-for-Sketch | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Photo_Pre-Training_but_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralPCI: Spatio-Temporal Neural Field for 3D Point Cloud Multi-Frame Non-Linear Interpolation | Zehan Zheng, Danni Wu, Ruisi Lu, Fan Lu, Guang Chen, Changjun Jiang | In recent years, there has been a significant increase in focus on the interpolation task of computer vision. Despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes the point cloud interpolation task more challenging. In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios. Furthermore, we construct a new multi-frame point cloud interpolation dataset called NL-Drive for large nonlinear motions in autonomous driving scenes to better demonstrate the superiority of our method. Ultimately, NeuralPCI achieves state-of-the-art performance on both DHB (Dynamic Human Bodies) and NL-Drive datasets. Beyond the interpolation task, our method can be naturally extended to point cloud extrapolation, morphing, and auto-labeling, which indicates substantial potential in other domains. Codes are available at https://github.com/ispc-lab/NeuralPCI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_NeuralPCI_Spatio-Temporal_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.html | CVPR 2023 | null |
Bidirectional Cross-Modal Knowledge Exploration for Video Recognition With Pre-Trained Vision-Language Models | Wenhao Wu, Xiaohan Wang, Haipeng Luo, Jingdong Wang, Yi Yang, Wanli Ouyang | Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://github.com/whwu95/BIKE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Bidirectional_Cross-Modal_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.00182 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Plasticity Improvement for Continual Learning | Yan-Shuo Liang, Wu-Jun Li | Many works have tried to solve the catastrophic forgetting (CF) problem in continual learning (lifelong learning). However, pursuing non-forgetting on old tasks may damage the model's plasticity for new tasks. Although some methods have been proposed to achieve stability-plasticity trade-off, no methods have considered evaluating a model's plasticity and improving plasticity adaptively for a new task. In this work, we propose a new method, called adaptive plasticity improvement (API), for continual learning. Besides the ability to overcome CF on old tasks, API also tries to evaluate the model's plasticity and then adaptively improve the model's plasticity for learning a new task if necessary. Experiments on several real datasets show that API can outperform other state-of-the-art baselines in terms of both accuracy and memory usage. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_Adaptive_Plasticity_Improvement_for_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liang_Adaptive_Plasticity_Improvement_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Adaptive_Plasticity_Improvement_for_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Adaptive_Plasticity_Improvement_for_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Pic2Word: Mapping Pictures to Words for Zero-Shot Composed Image Retrieval | Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, Tomas Pfister | In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. Labeling such triplets is expensive and hinders broad applicability of CIR. In this work, we propose to study an important task, Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training. To this end, we propose a novel method, called Pic2Word, that requires only weakly labeled image-caption pairs and unlabeled image datasets to train. Unlike existing supervised CIR models, our model trained on weakly labeled or unlabeled datasets shows strong generalization across diverse ZS-CIR tasks, e.g., attribute editing, object composition, and domain conversion. Our approach outperforms several supervised CIR methods on the common CIR benchmark, CIRR and Fashion-IQ. | https://openaccess.thecvf.com/content/CVPR2023/papers/Saito_Pic2Word_Mapping_Pictures_to_Words_for_Zero-Shot_Composed_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Saito_Pic2Word_Mapping_Pictures_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.03084 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Saito_Pic2Word_Mapping_Pictures_to_Words_for_Zero-Shot_Composed_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Saito_Pic2Word_Mapping_Pictures_to_Words_for_Zero-Shot_Composed_Image_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
MMANet: Margin-Aware Distillation and Modality-Aware Regularization for Incomplete Multimodal Learning | Shicai Wei, Chunbo Luo, Yang Luo | Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to assist incomplete multimodal learning. It consists of three components: the deployment network used for inference, the teacher network transferring comprehensive multimodal information to the deployment network, and the regularization network guiding the deployment network to balance weak modality combinations. Specifically, we propose a novel margin-aware distillation (MAD) to assist the information transfer by weighing the sample contribution with the classification uncertainty. This encourages the deployment network to focus on the samples near decision boundaries and acquire the refined inter-class margin. Besides, we design a modality-aware regularization (MAR) algorithm to mine the weak modality combinations and guide the regularization network to calculate prediction loss for them. This forces the deployment network to improve its representation ability for the weak modality combinations adaptively. Finally, extensive experiments on multimodal classification and segmentation tasks demonstrate that our MMANet outperforms the state-of-the-art significantly. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_MMANet_Margin-Aware_Distillation_and_Modality-Aware_Regularization_for_Incomplete_Multimodal_Learning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.08028 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_MMANet_Margin-Aware_Distillation_and_Modality-Aware_Regularization_for_Incomplete_Multimodal_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_MMANet_Margin-Aware_Distillation_and_Modality-Aware_Regularization_for_Incomplete_Multimodal_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Putting People in Their Place: Affordance-Aware Human Insertion Into Scenes | Sumith Kulal, Tim Brooks, Alex Aiken, Jiajun Wu, Jimei Yang, Jingwan Lu, Alexei A. Efros, Krishna Kumar Singh | We study the problem of inferring scene affordances by presenting a method for realistically inserting people into scenes. Given a scene image with a marked region and an image of a person, we insert the person into the scene while respecting the scene affordances. Our model can infer the set of realistic poses given the scene context, re-pose the reference person, and harmonize the composition. We set up the task in a self-supervised fashion by learning to re- pose humans in video clips. We train a large-scale diffusion model on a dataset of 2.4M video clips that produces diverse plausible poses while respecting the scene context. Given the learned human-scene composition, our model can also hallucinate realistic people and scenes when prompted without conditioning and also enables interactive editing. We conduct quantitative evaluation and show that our method synthesizes more realistic human appearance and more natural human-scene interactions when compared to prior work. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kulal_Putting_People_in_Their_Place_Affordance-Aware_Human_Insertion_Into_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kulal_Putting_People_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kulal_Putting_People_in_Their_Place_Affordance-Aware_Human_Insertion_Into_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kulal_Putting_People_in_Their_Place_Affordance-Aware_Human_Insertion_Into_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
3D Neural Field Generation Using Triplane Diffusion | J. Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, Gordon Wetzstein | Diffusion models have emerged as the state-of-the-art for image generation, among other tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields and factoring them into a set of axis-aligned triplane feature representations. Thus, our 3D training scenes are all represented by 2D feature planes, and we can directly train existing 2D diffusion models on these representations to generate 3D neural fields with high quality and diversity, outperforming alternative approaches to 3D-aware generation. Our approach requires essential modifications to existing triplane factorization pipelines to make the resulting features easy to learn for the diffusion model. We demonstrate state-of-the-art results on 3D generation on several object classes from ShapeNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shue_3D_Neural_Field_Generation_Using_Triplane_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shue_3D_Neural_Field_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16677 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shue_3D_Neural_Field_Generation_Using_Triplane_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shue_3D_Neural_Field_Generation_Using_Triplane_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Regularized Vector Quantization for Tokenized Image Synthesis | Jiahui Zhang, Fangneng Zhan, Christian Theobalt, Shijian Lu | Quantizing images into discrete representations has been a fundamental problem in unified generative modeling. Predominant approaches learn the discrete representation either in a deterministic manner by selecting the best-matching token or in a stochastic manner by sampling from a predicted distribution. However, deterministic quantization suffers from severe codebook collapse and misaligned inference stage while stochastic quantization suffers from low codebook utilization and perturbed reconstruction objective. This paper presents a regularized vector quantization framework that allows to mitigate above issues effectively by applying regularization from two perspectives. The first is a prior distribution regularization which measures the discrepancy between a prior token distribution and predicted token distribution to avoid codebook collapse and low codebook utilization. The second is a stochastic mask regularization that introduces stochasticity during quantization to strike a good balance between inference stage misalignment and unperturbed reconstruction objective. In addition, we design a probabilistic contrastive loss which serves as a calibrated metric to further mitigate the perturbed reconstruction objective. Extensive experiments show that the proposed quantization framework outperforms prevailing vector quantizers consistently across different generative models including auto-regressive models and diffusion models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Regularized_Vector_Quantization_for_Tokenized_Image_Synthesis_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.06424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Regularized_Vector_Quantization_for_Tokenized_Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Regularized_Vector_Quantization_for_Tokenized_Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Semantic Scene Completion With Cleaner Self | Fengyun Wang, Dong Zhang, Hanwang Zhang, Jinhui Tang, Qianru Sun | Semantic Scene Completion (SSC) transforms an image of single-view depth and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a well-known ill-posed problem as the prediction model has to "imagine" what is behind the visible surface, which is usually represented by Truncated Signed Distance Function (TSDF). Due to the sensory imperfection of the depth camera, most existing methods based on the noisy TSDF estimated from depth values suffer from 1) incomplete volumetric predictions and 2) confused semantic labels. To this end, we use the ground-truth 3D voxels to generate a perfect visible surface, called TSDF-CAD, and then train a "cleaner" SSC model. As the model is noise-free, it is expected to focus more on the "imagination" of unseen voxels. Then, we propose to distill the intermediate "cleaner" knowledge into another model with noisy TSDF input. In particular, we use the 3D occupancy feature and the semantic relations of the "cleaner self" to supervise the counterparts of the "noisy self" to respectively address the above two incorrect predictions. Experimental results validate that the proposed method improves the noisy counterparts with 3.1% IoU and 2.2% mIoU for measuring scene completion and SSC, and also achieves new state-of-the-art accuracy on the popular NYU dataset. The code is available at https://github.com/fereenwong/CleanerS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Semantic_Scene_Completion_With_Cleaner_Self_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Semantic_Scene_Completion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09977 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Semantic_Scene_Completion_With_Cleaner_Self_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Semantic_Scene_Completion_With_Cleaner_Self_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Image Recognition by Retrieving From Web-Scale Image-Text Data | Ahmet Iscen, Alireza Fathi, Cordelia Schmid | Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We also thoroughly study various ways of constructing the memory dataset. Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, and show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Iscen_Improving_Image_Recognition_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05173 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Factorized Metric Learning | Chengkun Wang, Wenzhao Zheng, Junlong Li, Jie Zhou, Jiwen Lu | Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks. While existing methods approach this goal by learning an ensemble of embeddings with diverse objectives, the backbone network still receives a mix of all the training signals. Differently, we propose a deep factorized metric learning method (DFML) to factorize the training signal and employ different samples to train various components of the backbone network. We factorize the network to different sub-blocks and devise a learnable router to adaptively allocate the training samples to each sub-block with the objective to capture the most information. The metric model trained by DFML captures different characteristics with different sub-blocks and constitutes a generalizable metric when using all the sub-blocks. The proposed DFML achieves state-of-the-art performance on all three benchmarks for deep metric learning including CUB-200-2011, Cars196, and Stanford Online Products. We also generalize DFML to the image classification task on ImageNet-1K and observe consistent improvement in accuracy/computation trade-off. Specifically, we improve the performance of ViT-B on ImageNet (+0.2% accuracy) with less computation load (-24% FLOPs). | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity 3D Face Generation From Natural Language Descriptions | Menghua Wu, Hao Zhu, Linjia Huang, Yiyu Zhuang, Yuanxun Lu, Xun Cao | Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descriptive text annotation, and 2) the complex mapping relationship between descriptive language space and shape/appearance space. To solve these problems, we build DESCRIBE3D dataset, the first large-scale dataset with fine-grained text descriptions for text-to-3D face generation task. Then we propose a two-stage framework to first generate a 3D face that matches the concrete descriptions, then optimize the parameters in the 3D shape and texture space with abstract description to refine the 3D face model. Extensive experimental results show that our method can produce a faithful 3D face that conforms to the input descriptions with higher accuracy and quality than previous methods. The code and DESCRIBE3D dataset are released at https://github.com/zhuhao-nju/describe3d. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_High-Fidelity_3D_Face_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.03302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.html | CVPR 2023 | null |
A Generalized Framework for Video Instance Segmentation | Miran Heo, Sukjun Hwang, Jeongseok Hyun, Hanjung Kim, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim | The handling of long videos with complex and occluded sequences has recently emerged as a new challenge in the video instance segmentation (VIS) community. However, existing methods have limitations in addressing this challenge. We argue that the biggest bottleneck in current approaches is the discrepancy between training and inference. To effectively bridge this gap, we propose a Generalized framework for VIS, namely GenVIS, that achieves state-of-the-art performance on challenging benchmarks without designing complicated architectures or requiring extra post-processing. The key contribution of GenVIS is the learning strategy, which includes a query-based training pipeline for sequential learning with a novel target label assignment. Additionally, we introduce a memory that effectively acquires information from previous states. Thanks to the new perspective, which focuses on building relationships between separate frames or clips, GenVIS can be flexibly executed in both online and semi-online manner. We evaluate our approach on popular VIS benchmarks, achieving state-of-the-art results on YouTube-VIS 2019/2021/2022 and Occluded VIS (OVIS). Notably, we greatly outperform the state-of-the-art on the long VIS benchmark (OVIS), improving 5.6 AP with ResNet-50 backbone. Code is available at https://github.com/miranheo/GenVIS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Heo_A_Generalized_Framework_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.08834 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Level Logit Distillation | Ying Jin, Jiaqi Wang, Dahua Lin | Knowledge Distillation (KD) aims at distilling the knowledge from the large teacher model to a lightweight student model. Mainstream KD methods can be divided into two categories, logit distillation, and feature distillation. The former is easy to implement, but inferior in performance, while the latter is not applicable to some practical circumstances due to concerns such as privacy and safety. Towards this dilemma, in this paper, we explore a stronger logit distillation method via making better utilization of logit outputs. Concretely, we propose a simple yet effective approach to logit distillation via multi-level prediction alignment. Through this framework, the prediction alignment is not only conducted at the instance level, but also at the batch and class level, through which the student model learns instance prediction, input correlation, and category correlation simultaneously. In addition, a prediction augmentation mechanism based on model calibration further boosts the performance. Extensive experiment results validate that our method enjoys consistently higher performance than previous logit distillation methods, and even reaches competitive performance with mainstream feature distillation methods. We promise to release our code and models to ensure reproducibility. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Multi-Level_Logit_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Multi-Level_Logit_Distillation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Multi-Level_Logit_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Multi-Level_Logit_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
On Distillation of Guided Diffusion Models | Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans | Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion frameworks including DALL*E 2, Stable Diffusion and Imagen. However, a downside of classifier-free guided diffusion models is that they are computationally expensive at inference time since they require evaluating two diffusion models, a class-conditional model and an unconditional model, tens to hundreds of times. To deal with this limitation, we propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from: Given a pre-trained classifier-free guided model, we first learn a single model to match the output of the combined conditional and unconditional models, and then we progressively distill that model to a diffusion model that requires much fewer sampling steps. For standard diffusion models trained on the pixel-space, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps on ImageNet 64x64 and CIFAR-10, achieving FID/IS scores comparable to that of the original model while being up to 256 times faster to sample from. For diffusion models trained on the latent-space (e.g., Stable Diffusion), our approach is able to generate high-fidelity images using as few as 1 to 4 denoising steps, accelerating inference by at least 10-fold compared to existing methods on ImageNet 256x256 and LAION datasets. We further demonstrate the effectiveness of our approach on text-guided image editing and inpainting, where our distilled model is able to generate high-quality results using as few as 2-4 denoising steps. | https://openaccess.thecvf.com/content/CVPR2023/papers/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Meng_On_Distillation_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.03142 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Dual-Path Adaptation From Image to Video Transformers | Jungin Park, Jiyoung Lee, Kwanghoon Sohn | In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters. Previous adaptation methods have simultaneously considered spatial and temporal modeling with a unified learnable module but still suffered from fully leveraging the representative capabilities of image transformers. We argue that the popular dual-path (two-stream) architecture in video models can mitigate this problem. We propose a novel DUALPATH adaptation separated into spatial and temporal adaptation paths, where a lightweight bottleneck adapter is employed in each transformer block. Especially for temporal dynamic modeling, we incorporate consecutive frames into a grid-like frameset to precisely imitate vision transformers' capability that extrapolates relationships between tokens. In addition, we extensively investigate the multiple baselines from a unified perspective in video understanding and compare them with DUALPATH. Experimental results on four action recognition benchmarks prove that pretrained image transformers with DUALPATH can be effectively generalized beyond the data domain. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Dual-Path_Adaptation_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09857 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Better Decision Forests: Forest Alternating Optimization | Miguel Á. Carreira-Perpiñán, Magzhan Gabidolla, Arman Zharmagambetov | Decision forests are among the most accurate models in machine learning. This is remarkable given that the way they are trained is highly heuristic: neither the individual trees nor the overall forest optimize any well-defined loss. While diversity mechanisms such as bagging or boosting have been until now critical in the success of forests, we think that a better optimization should lead to better forests---ideally eliminating any need for an ensembling heuristic. However, unlike for most other models, such as neural networks, optimizing forests or trees is not easy, because they define a non-differentiable function. We show, for the first time, that it is possible to learn a forest by optimizing a desirable loss and regularization jointly over all its trees and parameters. Our algorithm, Forest Alternating Optimization, is based on defining a forest as a parametric model with a fixed number of trees and structure (rather than adding trees indefinitely as in bagging or boosting). It then iteratively updates each tree in alternation so that the objective function decreases monotonically. The algorithm is so effective at optimizing that it easily overfits, but this can be corrected by averaging. The result is a forest that consistently exceeds the accuracy of the state-of-the-art while using fewer, smaller trees. | https://openaccess.thecvf.com/content/CVPR2023/papers/Carreira-Perpinan_Towards_Better_Decision_Forests_Forest_Alternating_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Carreira-Perpinan_Towards_Better_Decision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Carreira-Perpinan_Towards_Better_Decision_Forests_Forest_Alternating_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Carreira-Perpinan_Towards_Better_Decision_Forests_Forest_Alternating_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
DA Wand: Distortion-Aware Selection Using Neural Mesh Parameterization | Richard Liu, Noam Aigerman, Vladimir G. Kim, Rana Hanocka | We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The motivation for our framework is driven by interactive workflows used for decaling, texturing, or painting on surfaces. Our key idea to to learn a local parameterization in a data-driven manner, using a novel differentiable parameterization layer within a neural network framework. We train a segmentation network to select 3D regions that are parameterized into 2D and penalized by the resulting distortion, giving rise to segmentations which are distortion-aware. Following training, a user can use our system to interactively select a point on the mesh and obtain a large, meaningful region around the selection which induces a low-distortion parameterization. Our code and project page are publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_DA_Wand_Distortion-Aware_Selection_Using_Neural_Mesh_Parameterization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_DA_Wand_Distortion-Aware_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06344 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DA_Wand_Distortion-Aware_Selection_Using_Neural_Mesh_Parameterization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DA_Wand_Distortion-Aware_Selection_Using_Neural_Mesh_Parameterization_CVPR_2023_paper.html | CVPR 2023 | null |
Disentangled Representation Learning for Unsupervised Neural Quantization | Haechan Noh, Sangeek Hyun, Woojin Jeong, Hanshin Lim, Jae-Pil Heo | The inverted index is a widely used data structure to avoid the infeasible exhaustive search. It accelerates retrieval significantly by splitting the database into multiple disjoint sets and restricts distance computation to a small fraction of the database. Moreover, it even improves search quality by allowing quantizers to exploit the compact distribution of residual vector space. However, we firstly point out a problem that an existing deep learning-based quantizer hardly benefits from the residual vector space, unlike conventional shallow quantizers. To cope with this problem, we introduce a novel disentangled representation learning for unsupervised neural quantization. Similar to the concept of residual vector space, the proposed method enables more compact latent space by disentangling information of the inverted index from the vectors. Experimental results on large-scale datasets confirm that our method outperforms the state-of-the-art retrieval systems by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Noh_Disentangled_Representation_Learning_for_Unsupervised_Neural_Quantization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Noh_Disentangled_Representation_Learning_for_Unsupervised_Neural_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Noh_Disentangled_Representation_Learning_for_Unsupervised_Neural_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding | Chaolei Tan, Zihang Lin, Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai | Video Paragraph Grounding (VPG) is an essential yet challenging task in vision-language understanding, which aims to jointly localize multiple events from an untrimmed video with a paragraph query description. One of the critical challenges in addressing this problem is to comprehend the complex semantic relations between visual and textual modalities. Previous methods focus on modeling the contextual information between the video and text from a single-level perspective (i.e., the sentence level), ignoring rich visual-textual correspondence relations at different semantic levels, e.g., the video-word and video-paragraph correspondence. To this end, we propose a novel Hierarchical Semantic Correspondence Network (HSCNet), which explores multi-level visual-textual correspondence by learning hierarchical semantic alignment and utilizes dense supervision by grounding diverse levels of queries. Specifically, we develop a hierarchical encoder that encodes the multi-modal inputs into semantics-aligned representations at different levels. To exploit the hierarchical semantic correspondence learned in the encoder for multi-level supervision, we further design a hierarchical decoder that progressively performs finer grounding for lower-level queries conditioned on higher-level semantics. Extensive experiments demonstrate the effectiveness of HSCNet and our method significantly outstrips the state-of-the-arts on two challenging benchmarks, i.e., ActivityNet-Captions and TACoS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Hierarchical_Semantic_Correspondence_Networks_for_Video_Paragraph_Grounding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tan_Hierarchical_Semantic_Correspondence_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Hierarchical_Semantic_Correspondence_Networks_for_Video_Paragraph_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Hierarchical_Semantic_Correspondence_Networks_for_Video_Paragraph_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning | Cheng Tan, Zhangyang Gao, Lirong Wu, Yongjie Xu, Jun Xia, Siyuan Li, Stan Z. Li | Spatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework of spatiotemporal predictive learning, in which the spatial encoder and decoder capture intra-frame features and the middle temporal module catches inter-frame correlations. While the mainstream methods employ recurrent units to capture long-term temporal dependencies, they suffer from low computational efficiency due to their unparallelizable architectures. To parallelize the temporal module, we propose the Temporal Attention Unit (TAU), which decomposes temporal attention into intra-frame statical attention and inter-frame dynamical attention. Moreover, while the mean squared error loss focuses on intra-frame errors, we introduce a novel differential divergence regularization to take inter-frame variations into account. Extensive experiments demonstrate that the proposed method enables the derived model to achieve competitive performance on various spatiotemporal prediction benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Temporal_Attention_Unit_Towards_Efficient_Spatiotemporal_Predictive_Learning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2206.12126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Temporal_Attention_Unit_Towards_Efficient_Spatiotemporal_Predictive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Temporal_Attention_Unit_Towards_Efficient_Spatiotemporal_Predictive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Pose Transfer for Unrigged Stylized 3D Characters | Jiashun Wang, Xueting Li, Sifei Liu, Shalini De Mello, Orazio Gallo, Xiaolong Wang, Jan Kautz | Transferring the pose of a reference avatar to stylized 3D characters of various shapes is a fundamental task in computer graphics. Existing methods either require the stylized characters to be rigged, or they use the stylized character in the desired pose as ground truth at training. We present a zero-shot approach that requires only the widely available deformed non-stylized avatars in training, and deforms stylized characters of significantly different shapes at inference. Classical methods achieve strong generalization by deforming the mesh at the triangle level, but this requires labelled correspondences. We leverage the power of local deformation, but without requiring explicit correspondence labels. We introduce a semi-supervised shape-understanding module to bypass the need for explicit correspondences at test time, and an implicit pose deformation module that deforms individual surface points to match the target pose. Furthermore, to encourage realistic and accurate deformation of stylized characters, we introduce an efficient volume-based test-time training procedure. Because it does not need rigging, nor the deformed stylized character at training time, our model generalizes to categories with scarce annotation, such as stylized quadrupeds. Extensive experiments demonstrate the effectiveness of the proposed method compared to the state-of-the-art approaches trained with comparable or more supervision. Our project page is available at https://jiashunwang.github.io/ZPT | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Zero-Shot_Pose_Transfer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.html | CVPR 2023 | null |
Listening Human Behavior: 3D Human Pose Estimation With Acoustic Signals | Yuto Shibata, Yutaka Kawashima, Mariko Isogawa, Go Irie, Akisato Kimura, Yoshimitsu Aoki | Given only acoustic signals without any high-level information, such as voices or sounds of scenes/actions, how much can we infer about the behavior of humans? Unlike existing methods, which suffer from privacy issues because they use signals that include human speech or the sounds of specific actions, we explore how low-level acoustic signals can provide enough clues to estimate 3D human poses by active acoustic sensing with a single pair of microphones and loudspeakers (see Fig. 1). This is a challenging task since sound is much more diffractive than other signals and therefore covers up the shape of objects in a scene. Accordingly, we introduce a framework that encodes multichannel audio features into 3D human poses. Aiming to capture subtle sound changes to reveal detailed pose information, we explicitly extract phase features from the acoustic signals together with typical spectrum features and feed them into our human pose estimation network. Also, we show that reflected or diffracted sounds are easily influenced by subjects' physique differences e.g., height and muscularity, which deteriorates prediction accuracy. We reduce these gaps by using a subject discriminator to improve accuracy. Our experiments suggest that with the use of only low-dimensional acoustic information, our method outperforms baseline methods. The datasets and codes used in this project will be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shibata_Listening_Human_Behavior_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.html | CVPR 2023 | null |
Meta-Learning With a Geometry-Adaptive Preconditioner | Suhyun Kang, Duhun Hwang, Moonjung Eo, Taesup Kim, Wonjong Rhee | Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared initialization and the inner-loop process optimizes task-specific weights. Although MAML relies on the standard gradient descent in the inner-loop, recent studies have shown that controlling the inner-loop's gradient descent with a meta-learned preconditioner can be beneficial. Existing preconditioners, however, cannot simultaneously adapt in a task-specific and path-dependent way. Additionally, they do not satisfy the Riemannian metric condition, which can enable the steepest descent learning with preconditioned gradient. In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a preconditioner that is dependent on task-specific parameters, and its preconditioner can be shown to be a Riemannian metric. Thanks to the two properties, the geometry-adaptive preconditioner is effective for improving the inner-loop optimization. Experiment results show that GAP outperforms the state-of-the-art MAML family and preconditioned gradient descent-MAML (PGD-MAML) family in a variety of few-shot learning tasks. Code is available at: https://github.com/Suhyun777/CVPR23-GAP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Meta-Learning_With_a_Geometry-Adaptive_Preconditioner_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Meta-Learning_With_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Meta-Learning_With_a_Geometry-Adaptive_Preconditioner_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Meta-Learning_With_a_Geometry-Adaptive_Preconditioner_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Graph Enhanced Contrastive Learning for Chest X-Ray Report Generation | Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, Xiaojun Chang | Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate chest X-ray report generation with Contrastive Learning, named DCL. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Then we explore specific knowledge extracted from the retrieved reports to add additional nodes or redefine their relations in a bottom-up manner. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation. Finally, this paper introduces Image-Report Contrastive and Image-Report Matching losses to better represent visual features and textual information. Evaluated on IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art models on these two benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Dynamic_Graph_Enhanced_Contrastive_Learning_for_Chest_X-Ray_Report_Generation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Dynamic_Graph_Enhanced_Contrastive_Learning_for_Chest_X-Ray_Report_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Dynamic_Graph_Enhanced_Contrastive_Learning_for_Chest_X-Ray_Report_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
BiCro: Noisy Correspondence Rectification for Multi-Modality Data via Bi-Directional Cross-Modal Similarity Consistency | Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu | As one of the most fundamental techniques in multimodal learning, cross-modal matching aims to project various sensory modalities into a shared feature space. To achieve this, massive and correctly aligned data pairs are required for model training. However, unlike unimodal datasets, multimodal datasets are extremely harder to collect and annotate precisely. As an alternative, the co-occurred data pairs (e.g., image-text pairs) collected from the Internet have been widely exploited in the area. Unfortunately, the cheaply collected dataset unavoidably contains many mismatched data pairs, which have been proven to be harmful to the model's performance. To address this, we propose a general framework called BiCro (Bidirectional Cross-modal similarity consistency), which can be easily integrated into existing cross-modal matching models and improve their robustness against noisy data. Specifically, BiCro aims to estimate soft labels for noisy data pairs to reflect their true correspondence degree. The basic idea of BiCro is motivated by that -- taking image-text matching as an example -- similar images should have similar textual descriptions and vice versa. Then the consistency of these two similarities can be recast as the estimated soft labels to train the matching model. The experiments on three popular cross-modal matching datasets demonstrate that our method significantly improves the noise-robustness of various matching models, and surpass the state-of-the-art by a clear margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_BiCro_Noisy_Correspondence_Rectification_for_Multi-Modality_Data_via_Bi-Directional_Cross-Modal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_BiCro_Noisy_Correspondence_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12419 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BiCro_Noisy_Correspondence_Rectification_for_Multi-Modality_Data_via_Bi-Directional_Cross-Modal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BiCro_Noisy_Correspondence_Rectification_for_Multi-Modality_Data_via_Bi-Directional_Cross-Modal_CVPR_2023_paper.html | CVPR 2023 | null |
Transfer Knowledge From Head to Tail: Uncertainty Calibration Under Long-Tailed Distribution | Jiahao Chen, Bing Su | How to estimate the uncertainty of a given model is a crucial problem. Current calibration techniques treat different classes equally and thus implicitly assume that the distribution of training data is balanced, but ignore the fact that real-world data often follows a long-tailed distribution. In this paper, we explore the problem of calibrating the model trained from a long-tailed distribution. Due to the difference between the imbalanced training distribution and balanced test distribution, existing calibration methods such as temperature scaling can not generalize well to this problem. Specific calibration methods for domain adaptation are also not applicable because they rely on unlabeled target domain instances which are not available. Models trained from a long-tailed distribution tend to be more overconfident to head classes. To this end, we propose a novel knowledge-transferring-based calibration method by estimating the importance weights for samples of tail classes to realize long-tailed calibration. Our method models the distribution of each class as a Gaussian distribution and views the source statistics of head classes as a prior to calibrate the target distributions of tail classes. We adaptively transfer knowledge from head classes to get the target probability density of tail classes. The importance weight is estimated by the ratio of the target probability density over the source probability density. Extensive experiments on CIFAR-10-LT, MNIST-LT, CIFAR-100-LT, and ImageNet-LT datasets demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Transfer_Knowledge_From_Head_to_Tail_Uncertainty_Calibration_Under_Long-Tailed_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Transfer_Knowledge_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Transfer_Knowledge_From_Head_to_Tail_Uncertainty_Calibration_Under_Long-Tailed_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Transfer_Knowledge_From_Head_to_Tail_Uncertainty_Calibration_Under_Long-Tailed_CVPR_2023_paper.html | CVPR 2023 | null |
FrustumFormer: Adaptive Instance-Aware Resampling for Multi-View 3D Detection | Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang | The transformation of features from 2D perspective space to 3D space is essential to multi-view 3D object detection. Recent approaches mainly focus on the design of view transformation, either pixel-wisely lifting perspective view features into 3D space with estimated depth or grid-wisely constructing BEV features via 3D projection, treating all pixels or grids equally. However, choosing what to transform is also important but has rarely been discussed before. The pixels of a moving car are more informative than the pixels of the sky. To fully utilize the information contained in images, the view transformation should be able to adapt to different image regions according to their contents. In this paper, we propose a novel framework named FrustumFormer, which pays more attention to the features in instance regions via adaptive instance-aware resampling. Specifically, the model obtains instance frustums on the bird's eye view by leveraging image view object proposals. An adaptive occupancy mask within the instance frustum is learned to refine the instance location. Moreover, the temporal frustum intersection could further reduce the localization uncertainty of objects. Comprehensive experiments on the nuScenes dataset demonstrate the effectiveness of FrustumFormer, and we achieve a new state-of-the-art performance on the benchmark. Codes and models will be made available at https://github.com/Robertwyq/Frustum. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_FrustumFormer_Adaptive_Instance-Aware_Resampling_for_Multi-View_3D_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.04467 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FrustumFormer_Adaptive_Instance-Aware_Resampling_for_Multi-View_3D_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FrustumFormer_Adaptive_Instance-Aware_Resampling_for_Multi-View_3D_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Global Vision Transformer Pruning With Hessian-Aware Saliency | Huanrui Yang, Hongxu Yin, Maying Shen, Pavlo Molchanov, Hai Li, Jan Kautz | Transformers yield state-of-the-art results across many tasks. However, their heuristically designed architecture impose huge computational costs during inference. This work aims on challenging the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage, where we redistribute the parameters both across transformer blocks and between different structures within the block via the first systematic attempt on global structural pruning. Dealing with diverse ViT structural components, we derive a novel Hessian-based structural pruning criteria comparable across all layers and structures, with latency-aware regularization for direct latency reduction. Performing iterative pruning on the DeiT-Base model leads to a new architecture family called NViT (Novel ViT), with a novel parameter redistribution that utilizes parameters more efficiently. On ImageNet-1K, NViT-Base achieves a 2.6x FLOPs reduction, 5.1x parameter reduction, and 1.9x run-time speedup over the DeiT-Base model in a near lossless manner. Smaller NViT variants achieve more than 1% accuracy gain at the same throughput of the DeiT Small/Tiny variants, as well as a lossless 3.3x parameter reduction over the SWIN-Small model. These results outperform prior art by a large margin. Further analysis is provided on the parameter redistribution insight of NViT, where we show the high prunability of ViT models, distinct sensitivity within ViT block, and unique parameter distribution trend across stacked ViT blocks. Our insights provide viability for a simple yet effective parameter redistribution rule towards more efficient ViTs for off-the-shelf performance boost. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Global_Vision_Transformer_Pruning_With_Hessian-Aware_Saliency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Global_Vision_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2110.04869 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Global_Vision_Transformer_Pruning_With_Hessian-Aware_Saliency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Global_Vision_Transformer_Pruning_With_Hessian-Aware_Saliency_CVPR_2023_paper.html | CVPR 2023 | null |
Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition | Zhipeng Zhou, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Wei Gong | It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better. However, such property is under-explored in deep long-tailed recognition (DLTR), a practical problem where the model is required to generalize equally well across all classes when trained on highly imbalanced label distribution. In this paper, through empirical observations, we argue that sharp minima are in fact prevalent in deep longtailed models, whereas naive integration of existing flattening operations into long-tailed learning algorithms brings little improvement. Instead, we propose an effective twostage sharpness-aware optimization approach based on the decoupling paradigm in DLTR. In the first stage, both the feature extractor and classifier are trained under parameter perturbations at a class-conditioned scale, which is theoretically motivated by the characteristic radius of flat minima under the PAC-Bayesian framework. In the second stage, we generate adversarial features with classbalanced sampling to further robustify the classifier with the backbone frozen. Extensive experiments on multiple longtailed visual recognition benchmarks show that, our proposed Class-Conditional Sharpness-Aware Minimization (CC-SAM), achieves competitive performance compared to the state-of-the-arts. Code is available at https:// github.com/zzpustc/CC-SAM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Class-Conditional_Sharpness-Aware_Minimization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
ScarceNet: Animal Pose Estimation With Scarce Annotations | Chen Li, Gim Hee Lee | Animal pose estimation is an important but under-explored task due to the lack of labeled data. In this paper, we tackle the task of animal pose estimation with scarce annotations, where only a small set of labeled data and unlabeled images are available. At the core of the solution to this problem setting is the use of the unlabeled data to compensate for the lack of well-labeled animal pose data. To this end, we propose the ScarceNet, a pseudo label-based approach to generate artificial labels for the unlabeled images. The pseudo labels, which are generated with a model trained with the small set of labeled images, are generally noisy and can hurt the performance when directly used for training. To solve this problem, we first use a small-loss trick to select reliable pseudo labels. Although effective, the selection process is improvident since numerous high-loss samples are left unused. We further propose to identify reusable samples from the high-loss samples based on an agreement check. Pseudo labels are re-generated to provide supervision for those reusable samples. Lastly, we introduce a student-teacher framework to enforce a consistency constraint since there are still samples that are neither reliable nor reusable. By combining the reliable pseudo label selection with the reusable sample re-labeling and the consistency constraint, we can make full use of the unlabeled data. We evaluate our approach on the challenging AP-10K dataset, where our approach outperforms existing semi-supervised approaches by a large margin. We also test on the TigDog dataset, where our approach can achieve better performance than domain adaptation based approaches when only very few annotations are available. Our code is available at the project website. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ScarceNet_Animal_Pose_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15023 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.html | CVPR 2023 | null |
OmniCity: Omnipotent City Understanding With Multi-Level and Multi-View Images | Weijia Li, Yawen Lai, Linning Xu, Yuanbo Xiangli, Jinhua Yu, Conghui He, Gui-Song Xia, Dahua Lin | This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, OmniCity contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations in New York City. To alleviate the substantial pixel-wise annotation efforts, we propose an efficient street-view image annotation pipeline that leverages the existing label maps of satellite view and the transformation relations between different views (satellite, panorama, and mono-view). With the new OmniCity dataset, we provide benchmarks for a variety of tasks including building footprint extraction, height estimation, and building plane/instance/fine-grained segmentation. Compared with existing multi-level and multi-view benchmarks, OmniCity contains a larger number of images with richer annotation types and more views, provides more benchmark results of state-of-the-art models, and introduces a new task for fine-grained building instance segmentation on street-level panorama images. Moreover, OmniCity provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation. The OmniCity dataset as well as the benchmarks will be released at https://city-super.github.io/omnicity/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_OmniCity_Omnipotent_City_Understanding_With_Multi-Level_and_Multi-View_Images_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.00928 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_OmniCity_Omnipotent_City_Understanding_With_Multi-Level_and_Multi-View_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_OmniCity_Omnipotent_City_Understanding_With_Multi-Level_and_Multi-View_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient On-Device Training via Gradient Filtering | Yuedong Yang, Guihong Li, Radu Marculescu | Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19x speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy; over 20x speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Efficient_On-Device_Training_via_Gradient_Filtering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Efficient_On-Device_Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.00330 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Efficient_On-Device_Training_via_Gradient_Filtering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Efficient_On-Device_Training_via_Gradient_Filtering_CVPR_2023_paper.html | CVPR 2023 | null |
SViTT: Temporal Learning of Sparse Video-Text Transformers | Yi Li, Kyle Min, Subarna Tripathi, Nuno Vasconcelos | Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size; the curse of dimensionality for multi-frame modeling; and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://svcl.ucsd.edu/projects/svitt. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_SViTT_Temporal_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08809 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions | Juze Zhang, Haimin Luo, Hongdi Yang, Xinru Xu, Qianyang Wu, Ye Shi, Jingyi Yu, Lan Xu, Jingya Wang | Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that captures free-viewpoint interactions. We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HODome, that consists of 71M frames on 10 subjects interacting with 23 objects. To process the HODome dataset, we develop NeuralDome, a layer-wise neural processing pipeline tailored for multi-view video inputs to conduct accurate tracking, geometry reconstruction and free-view rendering, for both human subjects and objects. Extensive experiments on the HODome dataset demonstrate the effectiveness of NeuralDome on a variety of inference, modeling, and rendering tasks. Both the dataset and the NeuralDome tools will be disseminated to the community for further development, which can be found at https://juzezhang.github.io/NeuralDome | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_NeuralDome_A_Neural_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.07626 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.html | CVPR 2023 | null |
3D Human Mesh Estimation From Virtual Markers | Xiaoxuan Ma, Jiajun Su, Chunyu Wang, Wentao Zhu, Yizhou Wang | Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://github.com/ShirleyMaxx/VirtualMarker. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_3D_Human_Mesh_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11726 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.html | CVPR 2023 | null |
CUDA: Convolution-Based Unlearnable Datasets | Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi | Large-scale training of modern deep learning models heavily relies on publicly available data on the web. This potentially unauthorized usage of online data leads to concerns regarding data privacy. Recent works aim to make unlearnable data for deep learning models by adding small, specially designed noises to tackle this issue. However, these methods are vulnerable to adversarial training (AT) and/or are computationally heavy. In this work, we propose a novel, model-free, Convolution-based Unlearnable DAtaset (CUDA) generation technique. CUDA is generated using controlled class-wise convolutions with filters that are randomly generated via a private key. CUDA encourages the network to learn the relation between filters and labels rather than informative features for classifying the clean data. We develop some theoretical analysis demonstrating that CUDA can successfully poison Gaussian mixture data by reducing the clean data performance of the optimal Bayes classifier. We also empirically demonstrate the effectiveness of CUDA with various datasets (CIFAR-10, CIFAR-100, ImageNet-100, and Tiny-ImageNet), and architectures (ResNet-18, VGG-16, Wide ResNet-34-10, DenseNet-121, DeIT, EfficientNetV2-S, and MobileNetV2). Our experiments show that CUDA is robust to various data augmentations and training approaches such as smoothing, AT with different budgets, transfer learning, and fine-tuning. For instance, training a ResNet-18 on ImageNet-100 CUDA achieves only 8.96%, 40.08%, and 20.58% clean test accuracies with empirical risk minimization (ERM), L_infinity AT, and L_2 AT, respectively. Here, ERM on the clean training data achieves a clean test accuracy of 80.66%. CUDA exhibits unlearnability effect with ERM even when only a fraction of the training dataset is perturbed. Furthermore, we also show that CUDA is robust to adaptive defenses designed specifically to break it. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sadasivan_CUDA_Convolution-Based_Unlearnable_Datasets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sadasivan_CUDA_Convolution-Based_Unlearnable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.04278 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sadasivan_CUDA_Convolution-Based_Unlearnable_Datasets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sadasivan_CUDA_Convolution-Based_Unlearnable_Datasets_CVPR_2023_paper.html | CVPR 2023 | null |
No One Left Behind: Improving the Worst Categories in Long-Tailed Learning | Yingxiao Du, Jianxin Wu | Unlike the case when using a balanced training dataset, the per-class recall (i.e., accuracy) of neural networks trained with an imbalanced dataset are known to vary a lot from category to category. The convention in long-tailed recognition is to manually split all categories into three subsets and report the average accuracy within each subset. We argue that under such an evaluation setting, some categories are inevitably sacrificed. On one hand, focusing on the average accuracy on a balanced test set incurs little penalty even if some worst performing categories have zero accuracy. On the other hand, classes in the "Few" subset do not necessarily perform worse than those in the "Many" or "Medium" subsets. We therefore advocate to focus more on improving the lowest recall among all categories and the harmonic mean of all recall values. Specifically, we propose a simple plug-in method that is applicable to a wide range of methods. By simply re-training the classifier of an existing pre-trained model with our proposed loss function and using an optional ensemble trick that combines the predictions of the two classifiers, we achieve a more uniform distribution of recall values across categories, which leads to a higher harmonic mean accuracy while the (arithmetic) average accuracy is still high. The effectiveness of our method is justified on widely used benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_No_One_Left_Behind_Improving_the_Worst_Categories_in_Long-Tailed_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_No_One_Left_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03630 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_No_One_Left_Behind_Improving_the_Worst_Categories_in_Long-Tailed_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_No_One_Left_Behind_Improving_the_Worst_Categories_in_Long-Tailed_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric | Pengxin Zeng, Yunfan Li, Peng Hu, Dezhong Peng, Jiancheng Lv, Xi Peng | Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (e.g., gender, race, RNA sequencing technique) from dominating the clustering. Although a number of works have been conducted and achieved huge success recently, most of them are heuristical, and there lacks a unified theory for algorithm design. In this work, we fill this blank by developing a mutual information theory for deep fair clustering and accordingly designing a novel algorithm, dubbed FCMI. In brief, through maximizing and minimizing mutual information, FCMI is designed to achieve four characteristics highly expected by deep fair clustering, i.e., compact, balanced, and fair clusters, as well as informative features. Besides the contributions to theory and algorithm, another contribution of this work is proposing a novel fair clustering metric built upon information theory as well. Unlike existing evaluation metrics, our metric measures the clustering quality and fairness as a whole instead of separate manner. To verify the effectiveness of the proposed FCMI, we conduct experiments on six benchmarks including a single-cell RNA-seq atlas compared with 11 state-of-the-art methods in terms of five metrics. The code could be accessed from https://pengxi.me. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_Deep_Fair_Clustering_via_Maximizing_and_Minimizing_Mutual_Information_Theory_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_Deep_Fair_Clustering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.12396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Deep_Fair_Clustering_via_Maximizing_and_Minimizing_Mutual_Information_Theory_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Deep_Fair_Clustering_via_Maximizing_and_Minimizing_Mutual_Information_Theory_CVPR_2023_paper.html | CVPR 2023 | null |
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation | Yong Yang, Qiong Chen, Yuan Feng, Tianlin Huang | Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at github.com/Aldrich2y/MIANet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_MIANet_Aggregating_Unbiased_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.html | CVPR 2023 | null |
High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition | Tianyu Luan, Yuanhao Zhai, Jingjing Meng, Zhong Li, Zhang Chen, Yi Xu, Junsong Yuan | Despite the impressive performance obtained by recent single-image hand modeling techniques, they lack the capability to capture sufficient details of the 3D hand mesh. This deficiency greatly limits their applications when high fidelity hand modeling is required, e.g., personalized hand modeling. To address this problem, we design a frequency split network to generate 3D hand mesh using different frequency bands in a coarse-to-fine manner. To capture high-frequency personalized details, we transform the 3D mesh into the frequency domain, and propose a novel frequency decomposition loss to supervise each frequency component. By leveraging such a coarse-to-fine scheme, hand details that correspond to the higher frequency domain can be preserved. In addition, the proposed network is scalable, and can stop the inference at any resolution level to accommodate different hardwares with varying computational powers. To quantitatively evaluate the performance of our method in terms of recovering personalized shape details, we introduce a new evaluation metric named Mean Signal-to-Noise Ratio (MSNR) to measure the signal-to-noise ratio of each mesh frequency component. Extensive experiments demonstrate that our approach generates fine-grained details for high fidelity 3D hand reconstruction, and our evaluation metric is more effective for measuring mesh details compared with traditional metrics. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luan_High_Fidelity_3D_Hand_Shape_Reconstruction_via_Scalable_Graph_Frequency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luan_High_Fidelity_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luan_High_Fidelity_3D_Hand_Shape_Reconstruction_via_Scalable_Graph_Frequency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luan_High_Fidelity_3D_Hand_Shape_Reconstruction_via_Scalable_Graph_Frequency_CVPR_2023_paper.html | CVPR 2023 | null |
COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport | Yang Liu, Zhipeng Zhou, Baigui Sun | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge transfer, aligning the distribution between source and target domain from a global perspective is widely adopted in UDA. Recent researchers further point out the importance of local-level alignment and propose to construct instance-pair alignment by leveraging on Optimal Transport (OT) theory. However, existing OT-based UDA approaches are limited to handling class imbalance challenges and introduce a heavy computation overhead when considering a large-scale training situation. To cope with two aforementioned issues, we propose a Clustering-based Optimal Transport (COT) algorithm, which formulates the alignment procedure as an Optimal Transport problem and constructs a mapping between clustering centers in the source and target domain via an end-to-end manner. With this alignment on clustering centers, our COT eliminates the negative effect caused by class imbalance and reduces the computation cost simultaneously. Empirically, our COT achieves state-of-the-art performance on several authoritative benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.html | CVPR 2023 | null |
Target-Referenced Reactive Grasping for Dynamic Objects | Jirong Liu, Ruo Zhang, Hao-Shu Fang, Minghao Gou, Hongjie Fang, Chenxi Wang, Sheng Xu, Hengxu Yan, Cewu Lu | Reactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics. Current methods mainly focus on the temporal smoothness of the predicted grasp poses but few consider their semantic consistency. Consequently, the predicted grasps are not guaranteed to fall on the same part of the same object, especially in cluttered scenes. In this paper, we propose to solve reactive grasping in a target-referenced setting by tracking through generated grasp spaces. Given a targeted grasp pose on an object and detected grasp poses in a new observation, our method is composed of two stages: 1) discovering grasp pose correspondences through an attentional graph neural network and selecting the one with the highest similarity with respect to the target pose; 2) refining the selected grasp poses based on target and historical information. We evaluate our method on a large-scale benchmark GraspNet-1Billion. We also collect 30 scenes of dynamic objects for testing. The results suggest that our method outperforms other representative methods. Furthermore, our real robot experiments achieve an average success rate of over 80 percent. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Target-Referenced_Reactive_Grasping_for_Dynamic_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Target-Referenced_Reactive_Grasping_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Target-Referenced_Reactive_Grasping_for_Dynamic_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Target-Referenced_Reactive_Grasping_for_Dynamic_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization | Haina Qin, Longfei Han, Weihua Xiong, Juan Wang, Wentao Ma, Bing Li, Weiming Hu | The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_Learning_To_Exploit_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.html | CVPR 2023 | null |
Complexity-Guided Slimmable Decoder for Efficient Deep Video Compression | Zhihao Hu, Dong Xu | In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression. Specifically, given the target complexity constraints, in our cgSlimDecoder, we introduce a set of new channel width selection modules to automatically decide the optimal channel width of each slimmable convolution layer. By optimizing the complexity-rate-distortion related objective function to directly learn the parameters of the newly introduced channel width selection modules and other modules in the decoder, our cgSlimDecoder can automatically allocate the optimal numbers of parameters for different types of modules (e.g., motion/residual decoder and the motion compensation network) and simultaneously support multiple complexity levels by using a single learnt decoder instead of multiple decoders. In addition, our proposed SaEC can further accelerate the entropy decoding procedure in both motion and residual decoders by simply skipping the entropy coding process for the elements in the encoded feature maps that are already well-predicted by the hyperprior network. As demonstrated in our comprehensive experiments, our newly proposed methods cgSlimDecoder and SaEC are general and can be readily incorporated into three widely used deep video codecs (i.e., DVC, FVC and DCVC) to significantly improve their coding efficiency with negligible performance drop. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Complexity-Guided_Slimmable_Decoder_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.html | CVPR 2023 | null |
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation | Ning Zhang, Francesco Nex, George Vosselman, Norman Kerle | Self-supervised monocular depth estimation that does not require ground truth for training has attracted attention in recent years. It is of high interest to design lightweight but effective models so that they can be deployed on edge devices. Many existing architectures benefit from using heavier backbones at the expense of model sizes. This paper achieves comparable results with a lightweight architecture. Specifically, the efficient combination of CNNs and Transformers is investigated, and a hybrid architecture called Lite-Mono is presented. A Consecutive Dilated Convolutions (CDC) module and a Local-Global Features Interaction (LGFI) module are proposed. The former is used to extract rich multi-scale local features, and the latter takes advantage of the self-attention mechanism to encode long-range global information into the features. Experiments demonstrate that Lite-Mono outperforms Monodepth2 by a large margin in accuracy, with about 80% fewer trainable parameters. Our codes and models are available at https://github.com/noahzn/Lite-Mono. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Lite-Mono_A_Lightweight_CNN_and_Transformer_Architecture_for_Self-Supervised_Monocular_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Lite-Mono_A_Lightweight_CNN_and_Transformer_Architecture_for_Self-Supervised_Monocular_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Lite-Mono_A_Lightweight_CNN_and_Transformer_Architecture_for_Self-Supervised_Monocular_CVPR_2023_paper.html | CVPR 2023 | null |
MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins | Tiberiu Sosea, Cornelia Caragea | We introduce MarginMatch, a new SSL approach combining consistency regularization and pseudo-labeling, with its main novelty arising from the use of unlabeled data training dynamics to measure pseudo-label quality. Instead of using only the model's confidence on an unlabeled example at an arbitrary iteration to decide if the example should be masked or not, MarginMatch also analyzes the behavior of the model on the pseudo-labeled examples as the training progresses, ensuring low fluctuations in the model's predictions from one iteration to another. MarginMatch brings substantial improvements on four vision benchmarks in low data regimes and on two large-scale datasets, emphasizing the importance of enforcing high-quality pseudo-labels. Notably, we obtain an improvement in error rate over the state-of-the-art of 3.25% on CIFAR-100 with only 25 examples per class and of 4.19% on STL-10 using as few as 4 examples per class. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sosea_MarginMatch_Improving_Semi-Supervised_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Scene Chronology | Haotong Lin, Qianqian Wang, Ruojin Cai, Sida Peng, Hadar Averbuch-Elor, Xiaowei Zhou, Noah Snavely | In this work, we aim to reconstruct a time-varying 3D model, capable of rendering photo-realistic renderings with independent control of viewpoint, illumination, and time, from Internet photos of large-scale landmarks. The core challenges are twofold. First, different types of temporal changes, such as illumination and changes to the underlying scene itself (such as replacing one graffiti artwork with another) are entangled together in the imagery. Second, scene-level temporal changes are often discrete and sporadic over time, rather than continuous. To tackle these problems, we propose a new scene representation equipped with a novel temporal step function encoding method that can model discrete scene-level content changes as piece-wise constant functions over time. Specifically, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions. To facilitate our task of chronology reconstruction from Internet imagery, we also collect a new dataset of four scenes that exhibit various changes over time. We demonstrate that our method exhibits state-of-the-art view synthesis results on this dataset, while achieving independent control of viewpoint, time, and illumination. Code and data are available at https://zju3dv.github.io/NeuSC/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Neural_Scene_Chronology_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Neural_Scene_Chronology_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Neural_Scene_Chronology_CVPR_2023_paper.html | CVPR 2023 | null |
Starting From Non-Parametric Networks for 3D Point Cloud Analysis | Renrui Zhang, Liuhui Wang, Yali Wang, Peng Gao, Hongsheng Li, Jianbo Shi | We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions. Surprisingly, it performs well on various 3D tasks, requiring no parameters or training, and even surpasses existing fully trained models. Starting from this basic non-parametric model, we propose two extensions. First, Point-NN can serve as a base architectural framework to construct Parametric Networks by simply inserting linear layers on top. Given the superior non-parametric foundation, the derived Point-PN exhibits a high performance-efficiency trade-off with only a few learnable parameters. Second, Point-NN can be regarded as a plug-and-play module for the already trained 3D models during inference. Point-NN captures the complementary geometric knowledge and enhances existing methods for different 3D benchmarks without re-training. We hope our work may cast a light on the community for understanding 3D point clouds with non-parametric methods. Code is available at https://github.com/ZrrSkywalker/Point-NN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Starting_From_Non-Parametric_Networks_for_3D_Point_Cloud_Analysis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Starting_From_Non-Parametric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08134 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Starting_From_Non-Parametric_Networks_for_3D_Point_Cloud_Analysis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Starting_From_Non-Parametric_Networks_for_3D_Point_Cloud_Analysis_CVPR_2023_paper.html | CVPR 2023 | null |
Light Source Separation and Intrinsic Image Decomposition Under AC Illumination | Yusaku Yoshida, Ryo Kawahara, Takahiro Okabe | Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this paper, we show that the flickers due to AC illumination is useful for intrinsic image decomposition (IID). Our proposed method conducts the light source separation (LSS) followed by the IID under AC illumination. In particular, we reveal the ambiguity in the blind LSS via matrix factorization and the ambiguity in the IID assuming the Lambert model, and then show why and how those ambiguities can be resolved. We experimentally confirmed that our method can recover the colors of the light sources, the diffuse reflectance values, and the diffuse and specular intensities (shadings) under each of the light sources, and that the IID under AC illumination is effective for application to auto white balancing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yoshida_Light_Source_Separation_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.html | CVPR 2023 | null |
TIPI: Test Time Adaptation With Transformation Invariance | A. Tuan Nguyen, Thanh Nguyen-Tang, Ser-Nam Lim, Philip H.S. Torr | When deploying a machine learning model to a new environment, we often encounter the distribution shift problem -- meaning the target data distribution is different from the model's training distribution. In this paper, we assume that labels are not provided for this new domain, and that we do not store the source data (e.g., for privacy reasons). It has been shown that even small shifts in the data distribution can affect the model's performance severely. Test Time Adaptation offers a means to combat this problem, as it allows the model to adapt during test time to the new data distribution, using only unlabeled test data batches. To achieve this, the predominant approach is to optimize a surrogate loss on the test-time unlabeled target data. In particular, minimizing the prediction's entropy on target samples has received much interest as it is task-agnostic and does not require altering the model's training phase (e.g., does not require adding a self-supervised task during training on the source domain). However, as the target data's batch size is often small in real-world scenarios (e.g., autonomous driving models process each few frames in real-time), we argue that this surrogate loss is not optimal since it often collapses with small batch sizes. To tackle this problem, in this paper, we propose to use an invariance regularizer as the surrogate loss during test-time adaptation, motivated by our theoretical results regarding the model's performance under input transformations. The resulting method (TIPI -- Test tIme adaPtation with transformation Invariance) is validated with extensive experiments in various benchmarks (Cifar10-C, Cifar100-C, ImageNet-C, DIGITS, and VisDA17). Remarkably, TIPI is robust against small batch sizes (as small as 2 in our experiments), and consistently outperforms TENT in all settings. Our code is released at https://github.com/atuannguyen/TIPI. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nguyen_TIPI_Test_Time_Adaptation_With_Transformation_Invariance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nguyen_TIPI_Test_Time_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_TIPI_Test_Time_Adaptation_With_Transformation_Invariance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_TIPI_Test_Time_Adaptation_With_Transformation_Invariance_CVPR_2023_paper.html | CVPR 2023 | null |
OTAvatar: One-Shot Talking Face Avatar With Controllable Tri-Plane Rendering | Zhiyuan Ma, Xiangyu Zhu, Guo-Jun Qi, Zhen Lei, Lei Zhang | Controllability, generalizability and efficiency are the major objectives of constructing face avatars represented by neural implicit field. However, existing methods have not managed to accommodate the three requirements simultaneously. They either focus on static portraits, restricting the representation ability to a specific subject, or suffer from substantial computational cost, limiting their flexibility. In this paper, we propose One-shot Talking face Avatar (OTAvatar), which constructs face avatars by a generalized controllable tri-plane rendering solution so that each personalized avatar can be constructed from only one portrait as the reference. Specifically, OTAvatar first inverts a portrait image to a motion-free identity code. Second, the identity code and a motion code are utilized to modulate an efficient CNN to generate a tri-plane formulated volume, which encodes the subject in the desired motion. Finally, volume rendering is employed to generate an image in any view. The core of our solution is a novel decoupling-by-inverting strategy that disentangles identity and motion in the latent code via optimization-based inversion. Benefiting from the efficient tri-plane representation, we achieve controllable rendering of generalized face avatar at 35 FPS on A100. Experiments show promising performance of cross-identity reenactment on subjects out of the training set and better 3D consistency. The code is available at https://github.com/theEricMa/OTAvatar. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_OTAvatar_One-Shot_Talking_Face_Avatar_With_Controllable_Tri-Plane_Rendering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_OTAvatar_One-Shot_Talking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14662 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_OTAvatar_One-Shot_Talking_Face_Avatar_With_Controllable_Tri-Plane_Rendering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_OTAvatar_One-Shot_Talking_Face_Avatar_With_Controllable_Tri-Plane_Rendering_CVPR_2023_paper.html | CVPR 2023 | null |
Beyond Appearance: A Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks | Weihua Chen, Xianzhe Xu, Jian Jia, Hao Luo, Yaohua Wang, Fan Wang, Rong Jin, Xiuyu Sun | Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric tasks to the maximum extent. We call this method SOLIDER, a Semantic cOntrollable seLf-supervIseD lEaRning framework. Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation. Meanwhile, we note that different downstream tasks always require different ratios of semantic information and appearance information. For example, human parsing requires more semantic information, while person re-identification needs more appearance information for identification purpose. So a single learned representation cannot fit for all requirements. To solve this problem, SOLIDER introduces a conditional network with a semantic controller. After the model is trained, users can send values to the controller to produce representations with different ratios of semantic information, which can fit different needs of downstream tasks. Finally, SOLIDER is verified on six downstream human-centric visual tasks. It outperforms state of the arts and builds new baselines for these tasks. The code is released in https://github.com/tinyvision/SOLIDER. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Beyond_Appearance_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17602 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.html | CVPR 2023 | null |
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