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SCPNet: Semantic Scene Completion on Point Cloud | Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao | Training deep models for semantic scene completion is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inherent label noise for moving objects. To address the above-mentioned problems, we propose the following three solutions: 1) Redesigning the completion network. We design a novel completion network, which consists of several Multi-Path Blocks (MPBs) to aggregate multi-scale features and is free from the lossy downsampling operations. 2) Distilling rich knowledge from the multi-frame model. We design a novel knowledge distillation objective, dubbed Dense-to-Sparse Knowledge Distillation (DSKD). It transfers the dense, relation-based semantic knowledge from the multi-frame teacher to the single-frame student, significantly improving the representation learning of the single-frame model. 3) Completion label rectification. We propose a simple yet effective label rectification strategy, which uses off-the-shelf panoptic segmentation labels to remove the traces of dynamic objects in completion labels, greatly improving the performance of deep models especially for those moving objects. Extensive experiments are conducted in two public semantic scene completion benchmarks, i.e., SemanticKITTI and SemanticPOSS. Our SCPNet ranks 1st on SemanticKITTI semantic scene completion challenge and surpasses the competitive S3CNet by 7.2 mIoU. SCPNet also outperforms previous completion algorithms on the SemanticPOSS dataset. Besides, our method also achieves competitive results on SemanticKITTI semantic segmentation tasks, showing that knowledge learned in the scene completion is beneficial to the segmentation task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xia_SCPNet_Semantic_Scene_Completion_on_Point_Cloud_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xia_SCPNet_Semantic_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06884 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_SCPNet_Semantic_Scene_Completion_on_Point_Cloud_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_SCPNet_Semantic_Scene_Completion_on_Point_Cloud_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Prototypical Network for Cross Domain Few-Shot Learning | Fei Zhou, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang | Prototypical Network is a popular few-shot solver that aims at establishing a feature metric generalizable to novel few-shot classification (FSC) tasks using deep neural networks. However, its performance drops dramatically when generalizing to the FSC tasks in new domains. In this study, we revisit this problem and argue that the devil lies in the simplicity bias pitfall in neural networks. In specific, the network tends to focus on some biased shortcut features (e.g., color, shape, etc.) that are exclusively sufficient to distinguish very few classes in the meta-training tasks within a pre-defined domain, but fail to generalize across domains as some desirable semantic features. To mitigate this problem, we propose a Local-global Distillation Prototypical Network (LDP-net). Different from the standard Prototypical Network, we establish a two-branch network to classify the query image and its random local crops, respectively. Then, knowledge distillation is conducted among these two branches to enforce their class affiliation consistency. The rationale behind is that since such global-local semantic relationship is expected to hold regardless of data domains, the local-global distillation is beneficial to exploit some cross-domain transferable semantic features for feature metric establishment. Moreover, such local-global semantic consistency is further enforced among different images of the same class to reduce the intra-class semantic variation of the resultant feature. In addition, we propose to update the local branch as Exponential Moving Average (EMA) over training episodes, which makes it possible to better distill cross-episode knowledge and further enhance the generalization performance. Experiments on eight cross-domain FSC benchmarks empirically clarify our argument and show the state-of-the-art results of LDP-net. Code is available in https://github.com/NWPUZhoufei/LDP-Net | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Revisiting_Prototypical_Network_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation | Sicheng Yang, Zhiyong Wu, Minglei Li, Zhensong Zhang, Lei Hao, Weihong Bao, Haolin Zhuang | Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion matching framework. Specifically, we first present a gesture VQ-VAE module to learn a codebook to summarize meaningful gesture units. With each code representing a unique gesture, random jittering problems are alleviated effectively. We then use Levenshtein distance to align diverse gestures with different speech. Levenshtein distance based on audio quantization as a similarity metric of corresponding speech of gestures helps match more appropriate gestures with speech, and solves the alignment problem of speech and gestures well. Moreover, we introduce phase to guide the optimal gesture matching based on the semantics of context or rhythm of audio. Phase guides when text-based or speech-based gestures should be performed to make the generated gestures more natural. Extensive experiments show that our method outperforms recent approaches on speech-driven gesture generation. Our code, database, pre-trained models and demos are available at https://github.com/YoungSeng/QPGesture. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_QPGesture_Quantization-Based_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.html | CVPR 2023 | null |
Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis | Kang Han, Wei Xiang | Rendering novel views from captured multi-view images has made considerable progress since the emergence of the neural radiance field. This paper aims to further advance the quality of view rendering by proposing a novel approach dubbed the neural radiance feature field (NRFF). We first propose a multiscale tensor decomposition scheme to organize learnable features so as to represent scenes from coarse to fine scales. We demonstrate many benefits of the proposed multiscale representation, including more accurate scene shape and appearance reconstruction, and faster convergence compared with the single-scale representation. Instead of encoding view directions to model view-dependent effects, we further propose to encode the rendering equation in the feature space by employing the anisotropic spherical Gaussian mixture predicted from the proposed multiscale representation. The proposed NRFF improves state-of-the-art rendering results by over 1 dB in PSNR on both the NeRF and NSVF synthetic datasets. A significant improvement has also been observed on the real-world Tanks & Temples dataset. Code can be found at https://github.com/imkanghan/nrff. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_Multiscale_Tensor_Decomposition_and_Rendering_Equation_Encoding_for_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_Multiscale_Tensor_Decomposition_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Multiscale_Tensor_Decomposition_and_Rendering_Equation_Encoding_for_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Multiscale_Tensor_Decomposition_and_Rendering_Equation_Encoding_for_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations | Joy Hsu, Jiayuan Mao, Jiajun Wu | Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipulation. However, the variability of the 3D domain induces two fundamental challenges: 1) the expense of labeling and 2) the complexity of 3D grounded language. Hence, essential desiderata for models are to be data-efficient, generalize to different data distributions and tasks with unseen semantic forms, as well as ground complex language semantics (e.g., view-point anchoring and multi-object reference). To address these challenges, we propose NS3D, a neuro-symbolic framework for 3D grounding. NS3D translates language into programs with hierarchical structures by leveraging large language-to-code models. Different functional modules in the programs are implemented as neural networks. Notably, NS3D extends prior neuro-symbolic visual reasoning methods by introducing functional modules that effectively reason about high-arity relations (i.e., relations among more than two objects), key in disambiguating objects in complex 3D scenes. Modular and compositional architecture enables NS3D to achieve state-of-the-art results on the ReferIt3D view-dependence task, a 3D referring expression comprehension benchmark. Importantly, NS3D shows significantly improved performance on settings of data-efficiency and generalization, and demonstrate zero-shot transfer to an unseen 3D question-answering task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hsu_NS3D_Neuro-Symbolic_Grounding_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13483 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging | Tianyu Huang, Haoang Li, Kejing He, Congying Sui, Bin Li, Yun-Hui Liu | Shape from Polarization (SfP) aims to recover surface normal using the polarization cues of light. The accuracy of existing SfP methods is affected by two main problems. First, the ambiguity of polarization cues partially results in false normal estimation. Second, the widely-used assumption about orthographic projection is too ideal. To solve these problems, we propose the first approach that combines deep learning and stereo polarization information to recover not only normal but also disparity. Specifically, for the ambiguity problem, we design a Shape Consistency-based Mask Prediction (SCMP) module. It exploits the inherent consistency between normal and disparity to identify the areas with false normal estimation. We replace the unreliable features enclosed by these areas with new features extracted by global attention mechanism. As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy. This strategy is based on the unique pixel-viewing direction encoding, and thus enables our neural network to handle the non-orthographic projection. In addition, we establish a real-world stereo SfP dataset that contains various object categories and illumination conditions. Experiments showed that compared with existing SfP methods, our approach is more accurate. Moreover, our approach shows higher robustness to light variation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Learning_Accurate_3D_Shape_Based_on_Stereo_Polarimetric_Imaging_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Learning_Accurate_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_Accurate_3D_Shape_Based_on_Stereo_Polarimetric_Imaging_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_Accurate_3D_Shape_Based_on_Stereo_Polarimetric_Imaging_CVPR_2023_paper.html | CVPR 2023 | null |
VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking | Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, Yu Qiao | Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video foundation models with billions of parameters. This paper shows that video masked autoencoder (VideoMAE) is a scalable and general self-supervised pre-trainer for building video foundation models. We scale the VideoMAE in both model and data with a core design. Specifically, we present a dual masking strategy for efficient pre-training, with an encoder operating on a subset of video tokens and a decoder processing another subset of video tokens. Although VideoMAE is very efficient due to high masking ratio in encoder, masking decoder can still further reduce the overall computational cost. This enables the efficient pre-training of billion-level models in video. We also introduce a progressive training paradigm that involves initial pre-training on the diverse multi-sourced unlabeled dataset, followed by fine-tuning on a mixed labeled dataset. Finally, we successfully train a video ViT model with a billion parameters, which achieves a new state-of-the-art performance on the datasets of Kinetics (90.0% on K400 and 89.9% on K600) and Something-Something (68.7% on V1 and 77.0% on V2). In addition, we extensively verify the pre-trained video ViT models on a variety of downstream tasks, demonstrating its effectiveness as a general video representation learner. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_VideoMAE_V2_Scaling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16727 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.html | CVPR 2023 | null |
GANmouflage: 3D Object Nondetection With Texture Fields | Rui Guo, Jasmine Collins, Oscar de Lima, Andrew Owens | We propose a method that learns to camouflage 3D objects within scenes. Given an object's shape and a distribution of viewpoints from which it will be seen, we estimate a texture that will make it difficult to detect. Successfully solving this task requires a model that can accurately reproduce textures from the scene, while simultaneously dealing with the highly conflicting constraints imposed by each viewpoint. We address these challenges with a model based on texture fields and adversarial learning. Our model learns to camouflage a variety of object shapes from randomly sampled locations and viewpoints within the input scene, and is the first to address the problem of hiding complex object shapes. Using a human visual search study, we find that our estimated textures conceal objects significantly better than previous methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_GANmouflage_3D_Object_Nondetection_With_Texture_Fields_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2201.07202 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_GANmouflage_3D_Object_Nondetection_With_Texture_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_GANmouflage_3D_Object_Nondetection_With_Texture_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Perception and Semantic Aware Regularization for Sequential Confidence Calibration | Zhenghua Peng, Yu Luo, Tianshui Chen, Keke Xu, Shuangping Huang | Deep sequence recognition (DSR) models receive increasing attention due to their superior application to various applications. Most DSR models use merely the target sequences as supervision without considering other related sequences, leading to over-confidence in their predictions. The DSR models trained with label smoothing regularize labels by equally and independently smoothing each token, reallocating a small value to other tokens for mitigating overconfidence. However, they do not consider tokens/sequences correlations that may provide more effective information to regularize training and thus lead to sub-optimal performance. In this work, we find tokens/sequences with high perception and semantic correlations with the target ones contain more correlated and effective information and thus facilitate more effective regularization. To this end, we propose a Perception and Semantic aware Sequence Regularization framework, which explore perceptively and semantically correlated tokens/sequences as regularization. Specifically, we introduce a semantic context-free recognition and a language model to acquire similar sequences with high perceptive similarities and semantic correlation, respectively. Moreover, over-confidence degree varies across samples according to their difficulties. Thus, we further design an adaptive calibration intensity module to compute a difficulty score for each samples to obtain finer-grained regularization. Extensive experiments on canonical sequence recognition tasks, including scene text and speech recognition, demonstrate that our method sets novel state-of-the-art results. Code is available at https://github.com/husterpzh/PSSR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_Perception_and_Semantic_Aware_Regularization_for_Sequential_Confidence_Calibration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Peng_Perception_and_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Perception_and_Semantic_Aware_Regularization_for_Sequential_Confidence_Calibration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Perception_and_Semantic_Aware_Regularization_for_Sequential_Confidence_Calibration_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Residual Networks for Adversarial Robustness | Shihua Huang, Zhichao Lu, Kalyanmoy Deb, Vishnu Naresh Boddeti | Efforts to improve the adversarial robustness of convolutional neural networks have primarily focused on developing more effective adversarial training methods. In contrast, little attention was devoted to analyzing the role of architectural elements (e.g., topology, depth, and width) on adversarial robustness. This paper seeks to bridge this gap and present a holistic study on the impact of architectural design on adversarial robustness. We focus on residual networks and consider architecture design at the block level as well as at the network scaling level. In both cases, we first derive insights through systematic experiments. Then we design a robust residual block, dubbed RobustResBlock, and a compound scaling rule, dubbed RobustScaling, to distribute depth and width at the desired FLOP count. Finally, we combine RobustResBlock and RobustScaling and present a portfolio of adversarially robust residual networks, RobustResNets, spanning a broad spectrum of model capacities. Experimental validation across multiple datasets and adversarial attacks demonstrate that RobustResNets consistently outperform both the standard WRNs and other existing robust architectures, achieving state-of-the-art AutoAttack robust accuracy 63.7% with 500K external data while being 2x more compact in terms of parameters. The code is available at https://github.com/zhichao-lu/robust-residual-network. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Revisiting_Residual_Networks_for_Adversarial_Robustness_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Revisiting_Residual_Networks_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.11005 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Revisiting_Residual_Networks_for_Adversarial_Robustness_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Revisiting_Residual_Networks_for_Adversarial_Robustness_CVPR_2023_paper.html | CVPR 2023 | null |
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training | Chen-Wei Xie, Siyang Sun, Xiong Xiong, Yun Zheng, Deli Zhao, Jingren Zhou | Contrastive Language-Image Pre-training (CLIP) is attracting increasing attention for its impressive zero-shot recognition performance on different down-stream tasks. However, training CLIP is data-hungry and requires lots of image-text pairs to memorize various semantic concepts. In this paper, we propose a novel and efficient framework: Retrieval Augmented Contrastive Language-Image Pre-training (RA-CLIP) to augment embeddings by online retrieval. Specifically, we sample part of image-text data as a hold-out reference set. Given an input image, relevant image-text pairs are retrieved from the reference set to enrich the representation of input image. This process can be considered as an open-book exam: with the reference set as a cheat sheet, the proposed method doesn't need to memorize all visual concepts in the training data. It explores how to recognize visual concepts by exploiting correspondence between images and texts in the cheat sheet. The proposed RA-CLIP implements this idea and comprehensive experiments are conducted to show how RA-CLIP works. Performances on 10 image classification datasets and 2 object detection datasets show that RA-CLIP outperforms vanilla CLIP baseline by a large margin on zero-shot image classification task (+12.7%), linear probe image classification task (+6.9%) and zero-shot ROI classification task (+2.8%). | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_RA-CLIP_Retrieval_Augmented_Contrastive_Language-Image_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_RA-CLIP_Retrieval_Augmented_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_RA-CLIP_Retrieval_Augmented_Contrastive_Language-Image_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_RA-CLIP_Retrieval_Augmented_Contrastive_Language-Image_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
PosterLayout: A New Benchmark and Approach for Content-Aware Visual-Textual Presentation Layout | Hsiao Yuan Hsu, Xiangteng He, Yuxin Peng, Hao Kong, Qing Zhang | Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and both inter-element relationships as well as inter-layer relationships should be concerned when generating a proper layout. A few recent works deal with them simultaneously, but they still suffer from poor graphic performance, such as a lack of layout variety or spatial non-alignment. Since content-aware visual-textual presentation layout is a novel task, we first construct a new dataset named PKU PosterLayout, which consists of 9,974 poster-layout pairs and 905 images, i.e., non-empty canvases. It is more challenging and useful for greater layout variety, domain diversity, and content diversity. Then, we propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers, and a novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts. Specifically, the discriminator is design-sequence-aware and will supervise the "design" process of the generator. Experimental results verify the usefulness of the new benchmark and the effectiveness of the proposed approach, which achieves the best performance by generating suitable layouts for diverse canvases. The dataset and the source code are available at https://github.com/PKU-ICST-MIPL/PosterLayout-CVPR2023. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.html | CVPR 2023 | null |
A Practical Upper Bound for the Worst-Case Attribution Deviations | Fan Wang, Adams Wai-Kin Kong | Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks; however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both L2 and Linf-norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_A_Practical_Upper_Bound_for_the_Worst-Case_Attribution_Deviations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_A_Practical_Upper_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Upper_Bound_for_the_Worst-Case_Attribution_Deviations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Upper_Bound_for_the_Worst-Case_Attribution_Deviations_CVPR_2023_paper.html | CVPR 2023 | null |
A General Regret Bound of Preconditioned Gradient Method for DNN Training | Hongwei Yong, Ying Sun, Lei Zhang | While adaptive learning rate methods, such as Adam, have achieved remarkable improvement in optimizing Deep Neural Networks (DNNs), they consider only the diagonal elements of the full preconditioned matrix. Though the full-matrix preconditioned gradient methods theoretically have a lower regret bound, they are impractical for use to train DNNs because of the high complexity. In this paper, we present a general regret bound with a constrained full-matrix preconditioned gradient and show that the updating formula of the preconditioner can be derived by solving a cone-constrained optimization problem. With the block-diagonal and Kronecker-factorized constraints, a specific guide function can be obtained. By minimizing the upper bound of the guide function, we develop a new DNN optimizer, termed AdaBK. A series of techniques, including statistics updating, dampening, efficient matrix inverse root computation, and gradient amplitude preservation, are developed to make AdaBK effective and efficient to implement. The proposed AdaBK can be readily embedded into many existing DNN optimizers, e.g., SGDM and AdamW, and the corresponding SGDM_BK and AdamW_BK algorithms demonstrate significant improvements over existing DNN optimizers on benchmark vision tasks, including image classification, object detection and segmentation. The source code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yong_A_General_Regret_Bound_of_Preconditioned_Gradient_Method_for_DNN_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yong_A_General_Regret_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yong_A_General_Regret_Bound_of_Preconditioned_Gradient_Method_for_DNN_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yong_A_General_Regret_Bound_of_Preconditioned_Gradient_Method_for_DNN_CVPR_2023_paper.html | CVPR 2023 | null |
Teacher-Generated Spatial-Attention Labels Boost Robustness and Accuracy of Contrastive Models | Yushi Yao, Chang Ye, Junfeng He, Gamaleldin F. Elsayed | Human spatial attention conveys information about theregions of visual scenes that are important for perform-ing visual tasks. Prior work has shown that the informa-tion about human attention can be leveraged to benefit var-ious supervised vision tasks. Might providing this weakform of supervision be useful for self-supervised represen-tation learning? Addressing this question requires collect-ing large datasets with human attention labels. Yet, col-lecting such large scale data is very expensive. To addressthis challenge, we construct an auxiliary teacher model topredict human attention, trained on a relatively small la-beled dataset. This teacher model allows us to generate im-age (pseudo) attention labels for ImageNet. We then traina model with a primary contrastive objective; to this stan-dard configuration, we add a simple output head trained topredict the attentional map for each image, guided by thepseudo labels from teacher model. We measure the qual-ity of learned representations by evaluating classificationperformance from the frozen learned embeddings as wellas performance on image retrieval tasks. We find that thespatial-attention maps predicted from the contrastive modeltrained with teacher guidance aligns better with human at-tention compared to vanilla contrastive models. Moreover,we find that our approach improves classification accuracyand robustness of the contrastive models on ImageNet andImageNet-C. Further, we find that model representationsbecome more useful for image retrieval task as measuredby precision-recall performance on ImageNet, ImageNet-C,CIFAR10, and CIFAR10-C datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Teacher-Generated_Spatial-Attention_Labels_Boost_Robustness_and_Accuracy_of_Contrastive_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Teacher-Generated_Spatial-Attention_Labels_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Teacher-Generated_Spatial-Attention_Labels_Boost_Robustness_and_Accuracy_of_Contrastive_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Teacher-Generated_Spatial-Attention_Labels_Boost_Robustness_and_Accuracy_of_Contrastive_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification | Mengyao Xie, Zongbo Han, Changqing Zhang, Yichen Bai, Qinghua Hu | Classifying incomplete multi-view data is inevitable since arbitrary view missing widely exists in real-world applications. Although great progress has been achieved, existing incomplete multi-view methods are still difficult to obtain a trustworthy prediction due to the relatively high uncertainty nature of missing views. First, the missing view is of high uncertainty, and thus it is not reasonable to provide a single deterministic imputation. Second, the quality of the imputed data itself is of high uncertainty. To explore and exploit the uncertainty, we propose an Uncertainty-induced Incomplete Multi-View Data Classification (UIMC) model to classify the incomplete multi-view data under a stable and reliable framework. We construct a distribution and sample multiple times to characterize the uncertainty of missing views, and adaptively utilize them according to the sampling quality. Accordingly, the proposed method realizes more perceivable imputation and controllable fusion. Specifically, we model each missing data with a distribution conditioning on the available views and thus introducing uncertainty. Then an evidence-based fusion strategy is employed to guarantee the trustworthy integration of the imputed views. Extensive experiments are conducted on multiple benchmark data sets and our method establishes a state-of-the-art performance in terms of both performance and trustworthiness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Exploring_and_Exploiting_Uncertainty_for_Incomplete_Multi-View_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Exploring_and_Exploiting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05165 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Exploring_and_Exploiting_Uncertainty_for_Incomplete_Multi-View_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Exploring_and_Exploiting_Uncertainty_for_Incomplete_Multi-View_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning | Antoine Yang, Arsha Nagrani, Paul Hongsuck Seo, Antoine Miech, Jordi Pont-Tuset, Ivan Laptev, Josef Sivic, Cordelia Schmid | In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the same output sequence. Such a unified model requires large-scale training data, which is not available in current annotated datasets. We show that it is possible to leverage unlabeled narrated videos for dense video captioning, by reformulating sentence boundaries of transcribed speech as pseudo event boundaries, and using the transcribed speech sentences as pseudo event captions. The resulting Vid2Seq model pretrained on the YT-Temporal-1B dataset improves the state of the art on a variety of dense video captioning benchmarks including YouCook2, ViTT and ActivityNet Captions. Vid2Seq also generalizes well to the tasks of video paragraph captioning and video clip captioning, and to few-shot settings. Our code is publicly available at https://antoyang.github.io/vid2seq.html. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Vid2Seq_Large-Scale_Pretraining_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14115 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.html | CVPR 2023 | null |
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection | Xiaolin Song, Binghui Chen, Pengyu Li, Jun-Yan He, Biao Wang, Yifeng Geng, Xuansong Xie, Honggang Zhang | End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industrial applications. In this paper, we intend to bridge this gap and propose an Optimal Proposal Learning (OPL) framework for deployable end-to-end pedestrian detection. Specifically, we achieve this goal by using CNN-based light detector and introducing two novel modules, including a Coarse-to-Fine (C2F) learning strategy for proposing precise positive proposals for the Ground-Truth (GT) instances by reducing the ambiguity of sample assignment/output in training/testing respectively, and a Completed Proposal Network (CPN) for producing extra information compensation to further recall the hard pedestrian samples. Extensive experiments are conducted on CrowdHuman, TJU-Ped and Caltech, and the results show that our proposed OPL method significantly outperforms the competing methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering | Chuanqi Zang, Hanqing Wang, Mingtao Pei, Wei Liang | Video Question Answering (VideoQA) is challenging as it requires capturing accurate correlations between modalities from redundant information. Recent methods focus on the explicit challenges of the task, e.g. multimodal feature extraction, video-text alignment and fusion. Their frameworks reason the answer relying on statistical evidence causes, which ignores potential bias in the multimodal data. In our work, we investigate relational structure from a causal representation perspective on multimodal data and propose a novel inference framework. For visual data, question-irrelevant objects may establish simple matching associations with the answer. For textual data, the model prefers the local phrase semantics which may deviate from the global semantics in long sentences. Therefore, to enhance the generalization of the model, we discover the real association by explicitly capturing visual features that are causally related to the question semantics and weakening the impact of local language semantics on question answering. The experimental results on two large causal VideoQA datasets verify that our proposed framework 1) improves the accuracy of the existing VideoQA backbone, 2) demonstrates robustness on complex scenes and questions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zang_Discovering_the_Real_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.html | CVPR 2023 | null |
Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields | Sungheon Park, Minjung Son, Seokhwan Jang, Young Chun Ahn, Ji-Yeon Kim, Nahyup Kang | Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feature vectors. Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames. The proposed multi-level feature interpolation network effectively captures features of both short-term and long-term time ranges. In the grid representation, space-time features are learned via four-dimensional hash grids, which remarkably reduces training time. The grid representation shows more than 100 times faster training speed than the previous neural-net-based methods while maintaining the rendering quality. Concatenating static and dynamic features and adding a simple smoothness term further improve the performance of our proposed models. Despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Temporal_Interpolation_Is_All_You_Need_for_Dynamic_Neural_Radiance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Temporal_Interpolation_Is_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.09311 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Temporal_Interpolation_Is_All_You_Need_for_Dynamic_Neural_Radiance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Temporal_Interpolation_Is_All_You_Need_for_Dynamic_Neural_Radiance_CVPR_2023_paper.html | CVPR 2023 | null |
Graph Transformer GANs for Graph-Constrained House Generation | Hao Tang, Zhenyu Zhang, Humphrey Shi, Bo Li, Ling Shao, Nicu Sebe, Radu Timofte, Luc Van Gool | We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively. The proposed graph modeling block (GMB) aims to exploit local vertex interactions based on a house layout topology. Moreover, we propose a new node classification-based discriminator to preserve the high-level semantic and discriminative node features for different house components. Finally, we propose a novel graph-based cycle-consistency loss that aims at maintaining the relative spatial relationships between ground truth and predicted graphs. Experiments on two challenging graph-constrained house generation tasks (i.e., house layout and roof generation) with two public datasets demonstrate the effectiveness of GTGAN in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on both tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
On the Benefits of 3D Pose and Tracking for Human Action Recognition | Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Christoph Feichtenhofer, Jitendra Malik | In this work we study the benefits of using tracking and 3D poses for action recognition. To achieve this, we take the Lagrangian view on analysing actions over a trajectory of human motion rather than at a fixed point in space. Taking this stand allows us to use the tracklets of people to predict their actions. In this spirit, first we show the benefits of using 3D pose to infer actions, and study person-person interactions. Subsequently, we propose a Lagrangian Action Recognition model by fusing 3D pose and contextualized appearance over tracklets. To this end, our method achieves state-of-the-art performance on the AVA v2.2 dataset on both pose only settings and on standard benchmark settings. When reasoning about the action using only pose cues, our pose model achieves +10.0 mAP gain over the corresponding state-of-the-art while our fused model has a gain of +2.8 mAP over the best state-of-the-art model. Code and results are available at: https://brjathu.github.io/LART | https://openaccess.thecvf.com/content/CVPR2023/papers/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rajasegaran_On_the_Benefits_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01199 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.html | CVPR 2023 | null |
How to Backdoor Diffusion Models? | Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho | Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via progressive noise-addition and denoising. To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks. Specifically, we propose BadDiffusion, a novel attack framework that engineers compromised diffusion processes during model training for backdoor implantation. At the inference stage, the backdoored diffusion model will behave just like an untampered generator for regular data inputs, while falsely generating some targeted outcome designed by the bad actor upon receiving the implanted trigger signal. Such a critical risk can be dreadful for downstream tasks and applications built upon the problematic model. Our extensive experiments on various backdoor attack settings show that BadDiffusion can consistently lead to compromised diffusion models with high utility and target specificity. Even worse, BadDiffusion can be made cost-effective by simply finetuning a clean pre-trained diffusion model to implant backdoors. We also explore some possible countermeasures for risk mitigation. Our results call attention to potential risks and possible misuse of diffusion models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chou_How_to_Backdoor_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chou_How_to_Backdoor_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05400 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chou_How_to_Backdoor_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chou_How_to_Backdoor_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model With Knowledge-Enhanced Mixture-of-Denoising-Experts | Zhida Feng, Zhenyu Zhang, Xintong Yu, Yewei Fang, Lanxin Li, Xuyi Chen, Yuxiang Lu, Jiaxiang Liu, Weichong Yin, Shikun Feng, Yu Sun, Li Chen, Hao Tian, Hua Wu, Haifeng Wang | Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 not only achieves a new state-of-the-art on MS-COCO with zero-shot FID-30k score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_ERNIE-ViLG_2.0_Improving_Text-to-Image_Diffusion_Model_With_Knowledge-Enhanced_Mixture-of-Denoising-Experts_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_ERNIE-ViLG_2.0_Improving_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_ERNIE-ViLG_2.0_Improving_Text-to-Image_Diffusion_Model_With_Knowledge-Enhanced_Mixture-of-Denoising-Experts_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_ERNIE-ViLG_2.0_Improving_Text-to-Image_Diffusion_Model_With_Knowledge-Enhanced_Mixture-of-Denoising-Experts_CVPR_2023_paper.html | CVPR 2023 | null |
PACO: Parts and Attributes of Common Objects | Vignesh Ramanathan, Anmol Kalia, Vladan Petrovic, Yi Wen, Baixue Zheng, Baishan Guo, Rui Wang, Aaron Marquez, Rama Kovvuri, Abhishek Kadian, Amir Mousavi, Yiwen Song, Abhimanyu Dubey, Dhruv Mahajan | Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets. We provide 641K part masks annotated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/facebookresearch/paco. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ramanathan_PACO_Parts_and_Attributes_of_Common_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ramanathan_PACO_Parts_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01795 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ramanathan_PACO_Parts_and_Attributes_of_Common_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ramanathan_PACO_Parts_and_Attributes_of_Common_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Transformations To Reduce the Geometric Shift in Object Detection | Vidit Vidit, Martin Engilberge, Mathieu Salzmann | The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in the image capture process, or due to the constraints of the environment causing differences in the apparent geometry of the content itself. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts without leveraging any labeled data in the new domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change. Our results evidence that learning geometric transformations helps detectors to perform better in the target domains. | https://openaccess.thecvf.com/content/CVPR2023/papers/Vidit_Learning_Transformations_To_Reduce_the_Geometric_Shift_in_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vidit_Learning_Transformations_To_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05496 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_Learning_Transformations_To_Reduce_the_Geometric_Shift_in_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_Learning_Transformations_To_Reduce_the_Geometric_Shift_in_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
OReX: Object Reconstruction From Planar Cross-Sections Using Neural Fields | Haim Sawdayee, Amir Vaxman, Amit H. Bermano | Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing the training process to focus on high frequencies at later stages. In addition, we identify and analyze a ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries during network training, tackling the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sawdayee_OReX_Object_Reconstruction_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting With Neural Radiance Fields | Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis, Jonathan Kelly, Marcus A. Brubaker, Igor Gilitschenski, Alex Levinshtein | Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the replaced region is visually plausible and consistent with its context. We refer to this task as 3D inpainting. In 3D, solutions must be both consistent across multiple views and geometrically valid. In this paper, we propose a novel 3D inpainting method that addresses these challenges. Given a small set of posed images and sparse annotations in a single input image, our framework first rapidly obtains a 3D segmentation mask for a target object. Using the mask, a perceptual optimization-based approach is then introduced that leverages learned 2D image inpainters, distilling their information into 3D space, while ensuring view consistency. We also address the lack of a diverse benchmark for evaluating 3D scene inpainting methods by introducing a dataset comprised of challenging real-world scenes. In particular, our dataset contains views of the same scene with and without a target object, enabling more principled benchmarking of the 3D inpainting task. We first demonstrate the superiority of our approach on multiview segmentation, comparing to NeRF-based methods and 2D segmentation approaches. We then evaluate on the task of 3D inpainting, establishing state-of-the-art performance against other NeRF manipulation algorithms, as well as a strong 2D image inpainter baseline. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mirzaei_SPIn-NeRF_Multiview_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting the Stack-Based Inverse Tone Mapping | Ning Zhang, Yuyao Ye, Yang Zhao, Ronggang Wang | Current stack-based inverse tone mapping (ITM) methods can recover high dynamic range (HDR) radiance by predicting a set of multi-exposure images from a single low dynamic range image. However, there are still some limitations. On the one hand, these methods estimate a fixed number of images (e.g., three exposure-up and three exposure-down), which may introduce unnecessary computational cost or reconstruct incorrect results. On the other hand, they neglect the connections between the up-exposure and down-exposure models and thus fail to fully excavate effective features. In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images. At first, we design the exposure adaptive block that can adaptively adjust the exposure based on the luminance distribution of the input image. Secondly, we devise the cross-model attention block to connect the exposure adjustment models. Thirdly, we propose an end-to-end ITM pipeline by incorporating the multi-exposure fusion model. Furthermore, we propose and open a multi-exposure dataset that indicates the optimal exposure-up/down levels. Experimental results show that the proposed method outperforms some state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Revisiting_the_Stack-Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Rotation Averaging: Uncertainties and Robust Losses | Ganlin Zhang, Viktor Larsson, Daniel Barath | In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries. We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. Such uncertainties are obtained for free by considering the Jacobians of two-view refinements. Moreover, we explore integrating a variant of the MAGSAC loss into the rotation averaging problem, instead of using classical robust losses employed in current frameworks. The proposed method leads to results superior to baselines, in terms of accuracy, on large-scale public benchmarks. The code is public. https://github.com/zhangganlin/GlobalSfMpy | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Revisiting_Rotation_Averaging_Uncertainties_and_Robust_Losses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Revisiting_Rotation_Averaging_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_Rotation_Averaging_Uncertainties_and_Robust_Losses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_Rotation_Averaging_Uncertainties_and_Robust_Losses_CVPR_2023_paper.html | CVPR 2023 | null |
Continuous Sign Language Recognition With Correlation Network | Lianyu Hu, Liqing Gao, Zekang Liu, Wei Feng | Human body trajectories are a salient cue to identify actions in video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition(CSLR) usually process frames independently to capture frame-wise features, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly leverage body trajectories across frames to identify signs. In specific, an identification module is first presented to emphasize informative regions in each frame that are beneficial in expressing a sign. A correlation module is then proposed to dynamically compute correlation maps between current frame and adjacent neighbors to capture cross-frame trajectories. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison between CorrNet and previous spatial-temporal reasoning methods verifies its effectiveness. Visualizations are given to demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.03202 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.html | CVPR 2023 | null |
A Simple Framework for Text-Supervised Semantic Segmentation | Muyang Yi, Quan Cui, Hao Wu, Cheng Yang, Osamu Yoshie, Hongtao Lu | Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pre-training (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at github.com/muyangyi/SimSeg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yi_A_Simple_Framework_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection | Chen Zhang, Guorong Li, Yuankai Qi, Shuhui Wang, Laiyun Qing, Qingming Huang, Ming-Hsuan Yang | Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved significant improvements by self-generating pseudo labels and self-refining anomaly scores with these labels. As the pseudo labels play a crucial role, we propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training. Specifically, we first design a multi-head classification module (each head serves as a classifier) with a diversity loss to maximize the distribution differences of predicted pseudo labels across heads. This encourages the generated pseudo labels to cover as many abnormal events as possible. We then devise an iterative uncertainty pseudo label refinement strategy, which improves not only the initial pseudo labels but also the updated ones obtained by the desired classifier in the second stage. Extensive experimental results demonstrate the proposed method performs favorably against state-of-the-art approaches on the UCF-Crime, TAD, and XD-Violence benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Exploiting_Completeness_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04090 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.html | CVPR 2023 | null |
PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast Training and Rendering | Han Yan, Celong Liu, Chao Ma, Xing Mei | In this paper, we present a new representation for neural radiance fields that accelerates both the training and the inference processes with VDB, a hierarchical data structure for sparse volumes. VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being a promising data structure for NeRF data interpolation and ray marching. Our method, Plenoptic VDB (PlenVDB), directly learns the VDB data structure from a set of posed images by means of a novel training strategy and then uses it for real-time rendering. Experimental results demonstrate the effectiveness and the efficiency of our method over previous arts: First, it converges faster in the training process. Second, it delivers a more compact data format for NeRF data presentation. Finally, it renders more efficiently on commodity graphics hardware. Our mobile PlenVDB demo achieves 30+ FPS, 1280x720 resolution on an iPhone12 mobile phone. Check plenvdb.github.io for details. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_PlenVDB_Memory_Efficient_VDB-Based_Radiance_Fields_for_Fast_Training_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_PlenVDB_Memory_Efficient_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_PlenVDB_Memory_Efficient_VDB-Based_Radiance_Fields_for_Fast_Training_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_PlenVDB_Memory_Efficient_VDB-Based_Radiance_Fields_for_Fast_Training_and_CVPR_2023_paper.html | CVPR 2023 | null |
Patch-Based 3D Natural Scene Generation From a Single Example | Weiyu Li, Xuelin Chen, Jue Wang, Baoquan Chen | We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, renders existing setups intractable. Inspired by classical patch-based image models, we advocate for synthesizing 3D scenes at the patch level, given a single example. At the core of this work lies important algorithmic designs w.r.t the scene representation and generative patch nearest-neighbor module, that address unique challenges arising from lifting classical 2D patch-based framework to 3D generation. These design choices, on a collective level, contribute to a robust, effective, and efficient model that can generate high-quality general natural scenes with both realistic geometric structure and visual appearance, in large quantities and varieties, as demonstrated upon a variety of exemplar scenes. Data and code can be found at http://wyysf-98.github.io/Sin3DGen. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Patch-Based_3D_Natural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.html | CVPR 2023 | null |
Full or Weak Annotations? An Adaptive Strategy for Budget-Constrained Annotation Campaigns | Javier Gamazo Tejero, Martin S. Zinkernagel, Sebastian Wolf, Raphael Sznitman, Pablo Márquez-Neila | Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly. For segmentation applications, the burden is particularly high as manual delineations of relevant image content are often extremely expensive or can only be done by experts with domain-specific knowledge. Thanks to developments in transfer learning and training with weak supervision, segmentation models can now also greatly benefit from annotations of different kinds. However, for any new domain application looking to use weak supervision, the dataset builder still needs to define a strategy to distribute full segmentation and other weak annotations. Doing so is challenging, however, as it is a priori unknown how to distribute an annotation budget for a given new dataset. To this end, we propose a novel approach to determine annotation strategies for segmentation datasets, whereby estimating what proportion of segmentation and classification annotations should be collected given a fixed budget. To do so, our method sequentially determines proportions of segmentation and classification annotations to collect for budget-fractions by modeling the expected improvement of the final segmentation model. We show in our experiments that our approach yields annotations that perform very close to the optimal for a number of different annotation budgets and datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tejero_Full_or_Weak_Annotations_An_Adaptive_Strategy_for_Budget-Constrained_Annotation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tejero_Full_or_Weak_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11678 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tejero_Full_or_Weak_Annotations_An_Adaptive_Strategy_for_Budget-Constrained_Annotation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tejero_Full_or_Weak_Annotations_An_Adaptive_Strategy_for_Budget-Constrained_Annotation_CVPR_2023_paper.html | CVPR 2023 | null |
Leveraging Hidden Positives for Unsupervised Semantic Segmentation | Hyun Seok Seong, WonJun Moon, SuBeen Lee, Jae-Pil Heo | Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we leverage contrastive learning by excavating hidden positives to learn rich semantic relationships and ensure semantic consistency in local regions. Specifically, we first discover two types of global hidden positives, task-agnostic and task-specific ones for each anchor based on the feature similarities defined by a fixed pre-trained backbone and a segmentation head-in-training, respectively. A gradual increase in the contribution of the latter induces the model to capture task-specific semantic features. In addition, we introduce a gradient propagation strategy to learn semantic consistency between adjacent patches, under the inherent premise that nearby patches are highly likely to possess the same semantics. Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets. Our code is available at: https://github.com/hynnsk/HP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Backdoor Defense via Deconfounded Representation Learning | Zaixi Zhang, Qi Liu, Zhicai Wang, Zepu Lu, Qingyong Hu | Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations between the input images and target labels, making the model predictions less reliable. Inspired by the causal understanding, we propose the Causality-inspired Backdoor Defense (CBD), to learn deconfounded representations by employing the front-door adjustment. Specifically, a backdoored model is intentionally trained to capture the confounding effects. The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme. Extensive experiments on multiple benchmark datasets against 6 state-of-the-art attacks verify that our proposed defense method is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples. Further analysis shows that CBD can also resist potential adaptive attacks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Backdoor_Defense_via_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising | Zichun Wang, Ying Fu, Ji Liu, Yulun Zhang | Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blind-spot networks (BSNs). For recent methods targeting real-world denoising, they either suffer from ignoring this spatial correlation, or are limited by the destruction of fine textures for under-considering the correlation. In this paper, we present a novel method called LG-BPN for self-supervised real-world denoising, which takes the spatial correlation statistic into our network design for local detail restoration, and also brings the long-range dependencies modeling ability to previously CNN-based BSN methods. First, based on the correlation statistic, we propose a densely-sampled patch-masked convolution module. By taking more neighbor pixels with low noise correlation into account, we enable a denser local receptive field, preserving more useful information for enhanced fine structure recovery. Second, we propose a dilated Transformer block to allow distant context exploitation in BSN. This global perception addresses the intrinsic deficiency of BSN, whose receptive field is constrained by the blind spot requirement, which can not be fully resolved by the previous CNN-based BSNs. These two designs enable LG-BPN to fully exploit both the detailed structure and the global interaction in a blind manner. Extensive results on real-world datasets demonstrate the superior performance of our method. https://github.com/Wang-XIaoDingdd/LGBPN | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_LG-BPN_Local_and_Global_Blind-Patch_Network_for_Self-Supervised_Real-World_Denoising_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LG-BPN_Local_and_Global_Blind-Patch_Network_for_Self-Supervised_Real-World_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LG-BPN_Local_and_Global_Blind-Patch_Network_for_Self-Supervised_Real-World_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient View Synthesis and 3D-Based Multi-Frame Denoising With Multiplane Feature Representations | Thomas Tanay, Aleš Leonardis, Matteo Maggioni | While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the way for a new paradigm relying on volumetric scene representations. In this work, we introduce the first 3D-based multi-frame denoising method that significantly outperforms its 2D-based counterparts with lower computational requirements. Our method extends the multiplane image (MPI) framework for novel view synthesis by introducing a learnable encoder-renderer pair manipulating multiplane representations in feature space. The encoder fuses information across views and operates in a depth-wise manner while the renderer fuses information across depths and operates in a view-wise manner. The two modules are trained end-to-end and learn to separate depths in an unsupervised way, giving rise to Multiplane Feature (MPF) representations. Experiments on the Spaces and Real Forward-Facing datasets as well as on raw burst data validate our approach for view synthesis, multi-frame denoising, and view synthesis under noisy conditions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tanay_Efficient_View_Synthesis_and_3D-Based_Multi-Frame_Denoising_With_Multiplane_Feature_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tanay_Efficient_View_Synthesis_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18139 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tanay_Efficient_View_Synthesis_and_3D-Based_Multi-Frame_Denoising_With_Multiplane_Feature_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tanay_Efficient_View_Synthesis_and_3D-Based_Multi-Frame_Denoising_With_Multiplane_Feature_CVPR_2023_paper.html | CVPR 2023 | null |
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity | Zhao Xie, Tian Gao, Kewei Wu, Jiao Chang | The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence of some actors (cause actors) on other actors (effect actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficient to deal with the causality relation with asynchronous temporal features. In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. First, given a centric actor and correlative actor, we analyze their influences to detect causality relation. We estimate the self influence of the centric actor with self regression. We estimate the correlative influence from the correlative actor to the centric actor with correlative regression, which uses asynchronous features at different timestamps. Second, we synchronize the two action features by estimating the temporal delay between the cause action and the effect action. The synchronized features are used to enhance the feature of the effect action with a channel-wise fusion. Third, we describe the nodes (actors) with causality features and learn the edges by fusing the causality relation with the appearance relation and distance relation. The causality relation graph inference provides crucial features of effect action, which are complementary to the base model using synchronous relation inference. The two relation inferences are aggregated to enhance group relation learning. Extensive experiments show that our method achieves state-of-the-art performance on the Volleyball dataset and Collective Activity dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_An_Actor-Centric_Causality_Graph_for_Asynchronous_Temporal_Inference_in_Group_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_An_Actor-Centric_Causality_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_An_Actor-Centric_Causality_Graph_for_Asynchronous_Temporal_Inference_in_Group_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_An_Actor-Centric_Causality_Graph_for_Asynchronous_Temporal_Inference_in_Group_CVPR_2023_paper.html | CVPR 2023 | null |
Color Backdoor: A Robust Poisoning Attack in Color Space | Wenbo Jiang, Hongwei Li, Guowen Xu, Tianwei Zhang | Backdoor attacks against neural networks have been intensively investigated, where the adversary compromises the integrity of the victim model, causing it to make wrong predictions for inference samples containing a specific trigger. To make the trigger more imperceptible and human-unnoticeable, a variety of stealthy backdoor attacks have been proposed, some works employ imperceptible perturbations as the backdoor triggers, which restrict the pixel differences of the triggered image and clean image. Some works use special image styles (e.g., reflection, Instagram filter) as the backdoor triggers. However, these attacks sacrifice the robustness, and can be easily defeated by common preprocessing-based defenses. This paper presents a novel color backdoor attack, which can exhibit robustness and stealthiness at the same time. The key insight of our attack is to apply a uniform color space shift for all pixels as the trigger. This global feature is robust to image transformation operations and the triggered samples maintain natural-looking. To find the optimal trigger, we first define naturalness restrictions through the metrics of PSNR, SSIM and LPIPS. Then we employ the Particle Swarm Optimization (PSO) algorithm to search for the optimal trigger that can achieve high attack effectiveness and robustness while satisfying the restrictions. Extensive experiments demonstrate the superiority of PSO and the robustness of color backdoor against different mainstream backdoor defenses. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Color_Backdoor_A_Robust_Poisoning_Attack_in_Color_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_Color_Backdoor_A_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Color_Backdoor_A_Robust_Poisoning_Attack_in_Color_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Color_Backdoor_A_Robust_Poisoning_Attack_in_Color_Space_CVPR_2023_paper.html | CVPR 2023 | null |
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling | Yujian Zheng, Zirong Jin, Moran Li, Haibin Huang, Chongyang Ma, Shuguang Cui, Xiaoguang Han | In this work, we tackle the challenging problem of learning-based single-view 3D hair modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using synthetic data to provide prior knowledge for real domain becomes a leading solution. This unfortunately introduces the challenge of domain gap. Due to the inherent difficulty of realistic hair rendering, existing methods typically use orientation maps instead of hair images as input to bridge the gap. We firmly think an intermediate representation is essential, but we argue that orientation map using the dominant filtering-based methods is sensitive to uncertain noise and far from a competent representation. Thus, we first raise this issue up and propose a novel intermediate representation, termed as HairStep, which consists of a strand map and a depth map. It is found that HairStep not only provides sufficient information for accurate 3D hair modeling, but also is feasible to be inferred from real images. Specifically, we collect a dataset of 1,250 portrait images with two types of annotations. A learning framework is further designed to transfer real images to the strand map and depth map. It is noted that, an extra bonus of our new dataset is the first quantitative metric for 3D hair modeling. Our experiments show that HairStep narrows the domain gap between synthetic and real and achieves state-of-the-art performance on single-view 3D hair reconstruction. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_HairStep_Transfer_Synthetic_to_Real_Using_Strand_and_Depth_Maps_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_HairStep_Transfer_Synthetic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02700 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_HairStep_Transfer_Synthetic_to_Real_Using_Strand_and_Depth_Maps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_HairStep_Transfer_Synthetic_to_Real_Using_Strand_and_Depth_Maps_CVPR_2023_paper.html | CVPR 2023 | null |
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences | Yingwei Li, Charles R. Qi, Yin Zhou, Chenxi Liu, Dragomir Anguelov | Occluded and long-range objects are ubiquitous and challenging for 3D object detection. Point cloud sequence data provide unique opportunities to improve such cases, as an occluded or distant object can be observed from different viewpoints or gets better visibility over time. However, the efficiency and effectiveness in encoding long-term sequence data can still be improved. In this work, we propose MoDAR, using motion forecasting outputs as a type of virtual modality, to augment LiDAR point clouds. The MoDAR modality propagates object information from temporal contexts to a target frame, represented as a set of virtual points, one for each object from a waypoint on a forecasted trajectory. A fused point cloud of both raw sensor points and the virtual points can then be fed to any off-the-shelf point-cloud based 3D object detector. Evaluated on the Waymo Open Dataset, our method significantly improves prior art detectors by using motion forecasting from extra-long sequences (e.g. 18 seconds), achieving new state of the arts, while not adding much computation overhead. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_MoDAR_Using_Motion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.html | CVPR 2023 | null |
How You Feelin'? Learning Emotions and Mental States in Movie Scenes | Dhruv Srivastava, Aditya Kumar Singh, Makarand Tapaswi | Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset, we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted state-of-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx's self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues. | https://openaccess.thecvf.com/content/CVPR2023/papers/Srivastava_How_You_Feelin_Learning_Emotions_and_Mental_States_in_Movie_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Srivastava_How_You_Feelin_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Srivastava_How_You_Feelin_Learning_Emotions_and_Mental_States_in_Movie_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Srivastava_How_You_Feelin_Learning_Emotions_and_Mental_States_in_Movie_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Inference With Grounding Based Vision and Language Models | Burak Uzkent, Amanmeet Garg, Wentao Zhu, Keval Doshi, Jingru Yi, Xiaolong Wang, Mohamed Omar | Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other hand, there exists a large amount of computational redundancy in these large models which skips their run-time efficiency. To address this problem, we propose dynamic inference for grounding based vision and language models conditioned on the input image-text pair. We first design an approach to dynamically skip multihead self-attention and feed forward network layers across two backbones and multimodal network. Additionally, we propose dynamic token pruning and fusion for two backbones. In particular, we remove redundant tokens at different levels of the backbones and fuse the image tokens with the language tokens in an adaptive manner. To learn policies for dynamic inference, we train agents using reinforcement learning. In this direction, we replace the CNN backbone in a recent grounding-based vision and language model, MDETR, with a vision transformer and call it ViTMDETR. Then, we apply our dynamic inference method to ViTMDETR, called D-ViTDMETR, and perform experiments on image-language tasks. Our results show that we can improve the run-time efficiency of the state-of-the-art models MDETR and GLIP by up to 50% on Referring Expression Comprehension and Segmentation, and VQA with only maximum 0.3% accuracy drop. | https://openaccess.thecvf.com/content/CVPR2023/papers/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Uzkent_Dynamic_Inference_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ALSO: Automotive Lidar Self-Supervision by Occupancy Estimation | Alexandre Boulch, Corentin Sautier, Björn Michele, Gilles Puy, Renaud Marlet | We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled, and to use the underlying latent vectors as input to the perception head. The intuition is that if the network is able to reconstruct the scene surface, given only sparse input points, then it probably also captures some fragments of semantic information, that can be used to boost an actual perception task. This principle has a very simple formulation, which makes it both easy to implement and widely applicable to a large range of 3D sensors and deep networks performing semantic segmentation or object detection. In fact, it supports a single-stream pipeline, as opposed to most contrastive learning approaches, allowing training on limited resources. We conducted extensive experiments on various autonomous driving datasets, involving very different kinds of lidars, for both semantic segmentation and object detection. The results show the effectiveness of our method to learn useful representations without any annotation, compared to existing approaches. The code is available at github.com/valeoai/ALSO | https://openaccess.thecvf.com/content/CVPR2023/papers/Boulch_ALSO_Automotive_Lidar_Self-Supervision_by_Occupancy_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Boulch_ALSO_Automotive_Lidar_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05867 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Boulch_ALSO_Automotive_Lidar_Self-Supervision_by_Occupancy_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Boulch_ALSO_Automotive_Lidar_Self-Supervision_by_Occupancy_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Connecting Vision and Language With Video Localized Narratives | Paul Voigtlaender, Soravit Changpinyo, Jordi Pont-Tuset, Radu Soricut, Vittorio Ferrari | We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives, annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new protocol empowers annotators to tell the story of a video with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. We annotated 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. Based on this data, we also construct new benchmarks for the video narrative grounding and video question answering tasks, and provide reference results from strong baseline models. Our annotations are available at https://google.github.io/video-localized-narratives/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Voigtlaender_Connecting_Vision_and_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.11217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.html | CVPR 2023 | null |
Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-Identification | Yukang Zhang, Hanzi Wang | For the visible-infrared person re-identification (VIReID) task, one of the major challenges is the modality gaps between visible (VIS) and infrared (IR) images. However, the training samples are usually limited, while the modality gaps are too large, which leads that the existing methods cannot effectively mine diverse cross-modality clues. To handle this limitation, we propose a novel augmentation network in the embedding space, called diverse embedding expansion network (DEEN). The proposed DEEN can effectively generate diverse embeddings to learn the informative feature representations and reduce the modality discrepancy between the VIS and IR images. Moreover, the VIReID model may be seriously affected by drastic illumination changes, while all the existing VIReID datasets are captured under sufficient illumination without significant light changes. Thus, we provide a low-light cross-modality (LLCM) dataset, which contains 46,767 bounding boxes of 1,064 identities captured by 9 RGB/IR cameras. Extensive experiments on the SYSU-MM01, RegDB and LLCM datasets show the superiority of the proposed DEEN over several other state-of-the-art methods. The code and dataset are released at: https://github.com/ZYK100/LLCM | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.14481 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.html | CVPR 2023 | null |
Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection | Lianyu Wang, Meng Wang, Daoqiang Zhang, Huazhu Fu | As the scientific and technological achievements produced by human intellectual labor and computation cost, model intellectual property (IP) protection, which refers to preventing the usage of the well-trained model on an unauthorized domain, deserves further attention, so as to effectively mobilize the enthusiasm of model owners and creators. To this end, we propose a novel compact un-transferable isolation domain (CUTI-domain), which acts as a model barrier to block illegal transferring from the authorized domain to the unauthorized domain. Specifically, CUTI-domain is investigated to block cross-domain transferring by highlighting private style features of the authorized domain and lead to the failure of recognition on unauthorized domains that contain irrelative private style features. Furthermore, depending on whether the unauthorized domain is known or not, two solutions of using CUTI-domain are provided: target-specified CUTI-domain and target-free CUTI-domain. Comprehensive experimental results on four digit datasets, CIFAR10 & STL10, and VisDA-2017 dataset, demonstrate that our CUTI-domain can be easily implemented with different backbones as a plug-and-play module and provides an efficient solution for model IP protection. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Model_Barrier_A_Compact_Un-Transferable_Isolation_Domain_for_Model_Intellectual_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.11078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Model_Barrier_A_Compact_Un-Transferable_Isolation_Domain_for_Model_Intellectual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Model_Barrier_A_Compact_Un-Transferable_Isolation_Domain_for_Model_Intellectual_CVPR_2023_paper.html | CVPR 2023 | null |
Object Detection With Self-Supervised Scene Adaptation | Zekun Zhang, Minh Hoai | This paper proposes a novel method to improve the performance of a trained object detector on scenes with fixed camera perspectives based on self-supervised adaptation. Given a specific scene, the trained detector is adapted using pseudo-ground truth labels generated by the detector itself and an object tracker in a cross-teaching manner. When the camera perspective is fixed, our method can utilize the background equivariance by proposing artifact-free object mixup as a means of data augmentation, and utilize accurate background extraction as an additional input modality. We also introduce a large-scale and diverse dataset for the development and evaluation of scene-adaptive object detection. Experiments on this dataset show that our method can improve the average precision of the original detector, outperforming the previous state-of-the-art self-supervised domain adaptive object detection methods by a large margin. Our dataset and code are published at https://github.com/cvlab-stonybrook/scenes100. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Object_Detection_With_Self-Supervised_Scene_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Object_Detection_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Object_Detection_With_Self-Supervised_Scene_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Object_Detection_With_Self-Supervised_Scene_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Visual-Language Prompt Tuning With Knowledge-Guided Context Optimization | Hantao Yao, Rui Zhang, Changsheng Xu | Prompt tuning is an effective way to adapt the pretrained visual-language model (VLM) to the downstream task using task-related textual tokens. Representative CoOp-based works combine the learnable textual tokens with the class tokens to obtain specific textual knowledge. However, the specific textual knowledge has worse generalizable to the unseen classes because it forgets the essential general textual knowledge having a strong generalization ability. To tackle this issue, we introduce a novel Knowledge-guided Context Optimization (KgCoOp) to enhance the generalization ability of the learnable prompt for unseen classes. To remember the essential general knowledge, KgCoOp constructs a regularization term to ensure that the essential general textual knowledge can be embedded into the special textual knowledge generated by the learnable prompt. Especially, KgCoOp minimizes the discrepancy between the textual embeddings generated by learned prompts and the hand-crafted prompts. Finally, adding the KgCoOp upon the contrastive loss can make a discriminative prompt for both seen and unseen tasks. Extensive evaluation of several benchmarks demonstrates that the proposed Knowledge-guided Context Optimization is an efficient method for prompt tuning, i.e., achieves better performance with less training time. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Visual-Language_Prompt_Tuning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13283 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Video Representation Learning With Unaligned Text for Sequential Videos | Sixun Dong, Huazhang Hu, Dongze Lian, Weixin Luo, Yicheng Qian, Shenghua Gao | Sequential video understanding, as an emerging video understanding task, has driven lots of researchers' attention because of its goal-oriented nature. This paper studies weakly supervised sequential video understanding where the accurate time-stamp level text-video alignment is not provided. We solve this task by borrowing ideas from CLIP. Specifically, we use a transformer to aggregate frame-level features for video representation and use a pre-trained text encoder to encode the texts corresponding to each action and the whole video, respectively. To model the correspondence between text and video, we propose a multiple granularity loss, where the video-paragraph contrastive loss enforces matching between the whole video and the complete script, and a fine-grained frame-sentence contrastive loss enforces the matching between each action and its description. As the frame-sentence correspondence is not available, we propose to use the fact that video actions happen sequentially in the temporal domain to generate pseudo frame-sentence correspondence and supervise the network training with the pseudo labels. Extensive experiments on video sequence verification and text-to-video matching show that our method outperforms baselines by a large margin, which validates the effectiveness of our proposed approach. Code is available at https://github.com/svip-lab/WeakSVR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Weakly_Supervised_Video_Representation_Learning_With_Unaligned_Text_for_Sequential_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Weakly_Supervised_Video_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Weakly_Supervised_Video_Representation_Learning_With_Unaligned_Text_for_Sequential_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Weakly_Supervised_Video_Representation_Learning_With_Unaligned_Text_for_Sequential_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Positioning Point-Based Transformer for Point Cloud Understanding | Jinyoung Park, Sanghyeok Lee, Sihyeon Kim, Yunyang Xiong, Hyunwoo J. Kim | Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity. Specifically, this architecture consists of local self- attention and self-positioning point-based global cross-attention. The self-positioning points, adaptively located based on the input shape, consider both spatial and semantic information with disentangled attention to improve expressive power. With the self-positioning points, we propose a novel global cross-attention mechanism for point clouds, which improves the scalability of global self-attention by allowing the attention module to compute attention weights with only a small set of self-positioning points. Experiments show the effectiveness of SPoTr on three point cloud tasks such as shape classification, part segmentation, and scene segmentation. In particular, our proposed model achieves an accuracy gain of 2.6% over the previous best models on shape classification with ScanObjectNN. We also provide qualitative analyses to demonstrate the interpretability of self-positioning points. The code of SPoTr is available at https://github.com/mlvlab/SPoTr. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Self-Positioning_Point-Based_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16450 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery | Muli Yang, Liancheng Wang, Cheng Deng, Hanwang Zhang | Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes. Existing works hold an impractical assumption that the novel class distribution prior is uniform, yet neglect the imbalanced nature of real-world data. In this paper, we relax this assumption by proposing a new challenging task: distribution-agnostic NCD, which allows data drawn from arbitrary unknown class distributions and thus renders existing methods useless or even harmful. We tackle this challenge by proposing a new method, dubbed "Bootstrapping Your Own Prior (BYOP)", which iteratively estimates the class prior based on the model prediction itself. At each iteration, we devise a dynamic temperature technique that better estimates the class prior by encouraging sharper predictions for less-confident samples. Thus, BYOP obtains more accurate pseudo-labels for the novel samples, which are beneficial for the next training iteration. Extensive experiments show that existing methods suffer from imbalanced class distributions, while BYOP outperforms them by clear margins, demonstrating its effectiveness across various distribution scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Bootstrap_Your_Own_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Generate Image Embeddings With User-Level Differential Privacy | Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan | Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of epsilon < 2 while controlling the utility drop within 5%, when millions of users can participate in training. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_To_Generate_Image_Embeddings_With_User-Level_Differential_Privacy_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_To_Generate_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10844 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_To_Generate_Image_Embeddings_With_User-Level_Differential_Privacy_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_To_Generate_Image_Embeddings_With_User-Level_Differential_Privacy_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Vocabulary Panoptic Segmentation With Text-to-Image Diffusion Models | Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, Shalini De Mello | We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. This demonstrates that their internal representation space is highly correlated with open concepts in the real world. Text-image discriminative models like CLIP, on the other hand, are good at classifying images into open-vocabulary labels. We leverage the frozen internal representations of both these models to perform panoptic segmentation of any category in the wild. Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks. In particular, with COCO training only, our method achieves 23.4 PQ and 30.0 mIoU on the ADE20K dataset, with 8.3 PQ and 7.9 mIoU absolute improvement over the previous state of the art. We open-source our code and models at https://github.com/NVlabs/ODISE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Open-Vocabulary_Panoptic_Segmentation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.04803 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision | Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Yi Wang, Yu Qiao, Weidi Xie | In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, and aligns the group tokens to the corresponding caption embedding. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given the group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, which encourages the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on three benchmark datasets, PASCAL VOC 2012, PASCAL Context, and COCO Object. Our model achieves superior segmentation results over the state-of-the-art method by using only 3% data (4M vs 134M) for pre-training. Code and pre-trained models will be released for future research. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_Open-Vocabulary_Semantic_Segmentation_Models_From_Natural_Language_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_Open-Vocabulary_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.09121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Open-Vocabulary_Semantic_Segmentation_Models_From_Natural_Language_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Open-Vocabulary_Semantic_Segmentation_Models_From_Natural_Language_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Dynamic Style Kernels for Artistic Style Transfer | Wenju Xu, Chengjiang Long, Yongwei Nie | Arbitrary style transfer has been demonstrated to be efficient in artistic image generation. Previous methods either globally modulate the content feature ignoring local details, or overly focus on the local structure details leading to style leakage. In contrast to the literature, we propose a new scheme "style kernel" that learns spatially adaptive kernel for per-pixel stylization, where the convolutional kernels are dynamically generated from the global style-content aligned feature and then the learned kernels are applied to modulate the content feature at each spatial position. This new scheme allows flexible both global and local interactions between the content and style features such that the wanted styles can be easily transferred to the content image while at the same time the content structure can be easily preserved. To further enhance the flexibility of our style transfer method, we propose a Style Alignment Encoding (SAE) module complemented with a Content-based Gating Modulation (CGM) module for learning the dynamic style kernels in focusing regions. Extensive experiments strongly demonstrate that our proposed method outperforms state-of-the-art methods and exhibits superior performance in terms of visual quality and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_Dynamic_Style_Kernels_for_Artistic_Style_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_Dynamic_Style_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00414 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Dynamic_Style_Kernels_for_Artistic_Style_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Dynamic_Style_Kernels_for_Artistic_Style_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
DeepLSD: Line Segment Detection and Refinement With Deep Image Gradients | Rémi Pautrat, Daniel Barath, Viktor Larsson, Martin R. Oswald, Marc Pollefeys | Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://github.com/cvg/DeepLSD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pautrat_DeepLSD_Line_Segment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07766 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.html | CVPR 2023 | null |
OcTr: Octree-Based Transformer for 3D Object Detection | Chao Zhou, Yanan Zhang, Jiaxin Chen, Di Huang | A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this paper, we propose an Octree-based Transformer, named OcTr, to address this issue. It first constructs a dynamic octree on the hierarchical feature pyramid through conducting self-attention on the top level and then recursively propagates to the level below restricted by the octants, which captures rich global context in a coarse-to-fine manner while maintaining the computational complexity under control. Furthermore, for enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask, to fully exploit semantic and geometry clues. Extensive experiments are conducted on the Waymo Open Dataset and KITTI Dataset, and OcTr reaches newly state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_OcTr_Octree-Based_Transformer_for_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_OcTr_Octree-Based_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12621 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_OcTr_Octree-Based_Transformer_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_OcTr_Octree-Based_Transformer_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Chat2Map: Efficient Scene Mapping From Multi-Ego Conversations | Sagnik Majumder, Hao Jiang, Pierre Moulon, Ethan Henderson, Paul Calamia, Kristen Grauman, Vamsi Krishna Ithapu | Can conversational videos captured from multiple egocentric viewpoints reveal the map of a scene in a cost-efficient way? We seek to answer this question by proposing a new problem: efficiently building the map of a previously unseen 3D environment by exploiting shared information in the egocentric audio-visual observations of participants in a natural conversation. Our hypothesis is that as multiple people ("egos") move in a scene and talk among themselves, they receive rich audio-visual cues that can help uncover the unseen areas of the scene. Given the high cost of continuously processing egocentric visual streams, we further explore how to actively coordinate the sampling of visual information, so as to minimize redundancy and reduce power use. To that end, we present an audio-visual deep reinforcement learning approach that works with our shared scene mapper to selectively turn on the camera to efficiently chart out the space. We evaluate the approach using a state-of-the-art audio-visual simulator for 3D scenes as well as real-world video. Our model outperforms previous state-of-the-art mapping methods, and achieves an excellent cost-accuracy tradeoff. Project: https://vision.cs.utexas.edu/projects/chat2map. | https://openaccess.thecvf.com/content/CVPR2023/papers/Majumder_Chat2Map_Efficient_Scene_Mapping_From_Multi-Ego_Conversations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Majumder_Chat2Map_Efficient_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02184 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Majumder_Chat2Map_Efficient_Scene_Mapping_From_Multi-Ego_Conversations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Majumder_Chat2Map_Efficient_Scene_Mapping_From_Multi-Ego_Conversations_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Distortion Invariant Representation for Image Restoration From a Causality Perspective | Xin Li, Bingchen Li, Xin Jin, Cuiling Lan, Zhibo Chen | In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtual model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the generalization capability of our DIL on unseen distortion types and degrees. Our code will be available at https://github.com/lixinustc/Causal-IR-DIL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Learning_Distortion_Invariant_Representation_for_Image_Restoration_From_a_Causality_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Learning_Distortion_Invariant_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Distortion_Invariant_Representation_for_Image_Restoration_From_a_Causality_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Distortion_Invariant_Representation_for_Image_Restoration_From_a_Causality_CVPR_2023_paper.html | CVPR 2023 | null |
MOT: Masked Optimal Transport for Partial Domain Adaptation | You-Wei Luo, Chuan-Xian Ren | As an important methodology to measure distribution discrepancy, optimal transport (OT) has been successfully applied to learn generalizable visual models under changing environments. However, there are still limitations, including strict prior assumption and implicit alignment, for current OT modeling in challenging real-world scenarios like partial domain adaptation, where the learned transport plan may be biased and negative transfer is inevitable. Thus, it is necessary to explore a more feasible OT methodology for real-world applications. In this work, we focus on the rigorous OT modeling for conditional distribution matching and label shift correction. A novel masked OT (MOT) methodology on conditional distributions is proposed by defining a mask operation with label information. Further, a relaxed and reweighting formulation is proposed to improve the robustness of OT in extreme scenarios. We prove the theoretical equivalence between conditional OT and MOT, which implies the well-defined MOT serves as a computation-friendly proxy. Extensive experiments validate the effectiveness of theoretical results and proposed model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_MOT_Masked_Optimal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Executing Your Commands via Motion Diffusion in Latent Space | Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao Chen, Gang Yu | We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors. Since human motions are highly diverse and have a property of quite different distribution from conditional modalities, such as textual descriptors in natural languages, it is hard to learn a probabilistic mapping from the desired conditional modality to the human motion sequences. Besides, the raw motion data from the motion capture system might be redundant in sequences and contain noises; directly modeling the joint distribution over the raw motion sequences and conditional modalities would need a heavy computational overhead and might result in artifacts introduced by the captured noises. To learn a better representation of the various human motion sequences, we first design a powerful Variational AutoEncoder (VAE) and arrive at a representative and low-dimensional latent code for a human motion sequence. Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space. Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Executing_Your_Commands_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04048 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.html | CVPR 2023 | null |
GeoMAE: Masked Geometric Target Prediction for Self-Supervised Point Cloud Pre-Training | Xiaoyu Tian, Haoxi Ran, Yue Wang, Hang Zhao | This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a point cloud representation learning framework, based on geometric feature reconstruction. In contrast to recent papers that directly adopt masked autoencoder (MAE) and only predict original coordinates or occupancy from masked point clouds, our method revisits differences between images and point clouds and identifies three self-supervised learning objectives peculiar to point clouds, namely centroid prediction, normal estimation, and curvature prediction. Combined, these three objectives yield an nontrivial self-supervised learning task and mutually facilitate models to better reason fine-grained geometry of point clouds. Our pipeline is conceptually simple and it consists of two major steps: first, it randomly masks out groups of points, followed by a Transformer-based point cloud encoder; second, a lightweight Transformer decoder predicts centroid, normal, and curvature for points in each voxel. We transfer the pre-trained Transformer encoder to a downstream peception model. On the nuScene Datset, our model achieves 3.38 mAP improvment for object detection, 2.1 mIoU gain for segmentation, and 1.7 AMOTA gain for multi-object tracking. We also conduct experiments on the Waymo Open Dataset and achieve significant performance improvements over baselines as well. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_GeoMAE_Masked_Geometric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.08808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Conditional Attributes for Compositional Zero-Shot Learning | Qingsheng Wang, Lingqiao Liu, Chenchen Jing, Hao Chen, Guoqiang Liang, Peng Wang, Chunhua Shen | Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute "wet" in "wet apple" and "wet cat" is different. As a solution, we provide analysis and argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings by a proposed attribute learning framework containing an attribute hyper learner and an attribute base learner. By encoding conditional attributes, our model enables to generate flexible attribute embeddings for generalization from seen to unseen compositions. Experiments on CZSL benchmarks, including the more challenging C-GQA dataset, demonstrate better performances compared with other state-of-the-art approaches and validate the importance of learning conditional attributes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Complete 3D Human Reconstruction From a Single Incomplete Image | Junying Wang, Jae Shin Yoon, Tuanfeng Y. Wang, Krishna Kumar Singh, Ulrich Neumann | This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e.g., a torso. The core challenge arises from the occlusion: there exists no pixel to reconstruct where many existing single-view human reconstruction methods are not designed to handle such invisible parts, leading to missing data in 3D. To address this challenge, we introduce a novel coarse-to-fine human reconstruction framework. For coarse reconstruction, explicit volumetric features are learned to generate a complete human geometry with 3D convolutional neural networks conditioned by a 3D body model and the style features from visible parts. An implicit network combines the learned 3D features with the high-quality surface normals enhanced from multiview to produce fine local details, e.g., high-frequency wrinkles. Finally, we perform progressive texture inpainting to reconstruct a complete appearance of the person in a view-consistent way, which is not possible without the reconstruction of a complete geometry. In experiments, we demonstrate that our method can reconstruct high-quality 3D humans, which is robust to occlusion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Complete_3D_Human_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.html | CVPR 2023 | null |
PVT-SSD: Single-Stage 3D Object Detector With Point-Voxel Transformer | Honghui Yang, Wenxiao Wang, Minghao Chen, Binbin Lin, Tong He, Hua Chen, Xiaofei He, Wanli Ouyang | Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. However, the former requires time-consuming sampling while the latter introduces quantization errors. In this paper, we present a novel Point-Voxel Transformer for single-stage 3D detection (PVT-SSD) that takes advantage of these two representations. Specifically, we first use voxel-based sparse convolutions for efficient feature encoding. Then, we propose a Point-Voxel Transformer (PVT) module that obtains long-range contexts in a cheap manner from voxels while attaining accurate positions from points. The key to associating the two different representations is our introduced input-dependent Query Initialization module, which could efficiently generate reference points and content queries. Then, PVT adaptively fuses long-range contextual and local geometric information around reference points into content queries. Further, to quickly find the neighboring points of reference points, we design the Virtual Range Image module, which generalizes the native range image to multi-sensor and multi-frame. The experiments on several autonomous driving benchmarks verify the effectiveness and efficiency of the proposed method. Code will be available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_PVT-SSD_Single-Stage_3D_Object_Detector_With_Point-Voxel_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_PVT-SSD_Single-Stage_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_PVT-SSD_Single-Stage_3D_Object_Detector_With_Point-Voxel_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_PVT-SSD_Single-Stage_3D_Object_Detector_With_Point-Voxel_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Human Matting for Dynamic Videos | Chung-Ching Lin, Jiang Wang, Kun Luo, Kevin Lin, Linjie Li, Lijuan Wang, Zicheng Liu | The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with highly diverse and unstructured videos. We address this limitation by introducing Adaptive Matting for Dynamic Videos, termed AdaM, which is a framework designed for simultaneously differentiating foregrounds from backgrounds and capturing alpha matte details of human subjects in the foreground. Two interconnected network designs are employed to achieve this goal: (1) an encoder-decoder network that produces alpha mattes and intermediate masks which are used to guide the transformer in adaptively decoding foregrounds and backgrounds, and (2) a transformer network in which long- and short-term attention combine to retain spatial and temporal contexts, facilitating the decoding of foreground details. We benchmark and study our methods on recently introduced datasets, showing that our model notably improves matting realism and temporal coherence in complex real-world videos and achieves new best-in-class generalizability. Further details and examples are available at https://github.com/microsoft/AdaM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Adaptive_Human_Matting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06018 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems | Yangyang Shu, Anton van den Hengel, Lingqiao Liu | Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shu_Learning_Common_Rationale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01669 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
Reconstructing Animatable Categories From Videos | Gengshan Yang, Chaoyang Wang, N. Dinesh Reddy, Deva Ramanan | Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging. Recently, differentiable rendering provides a pathway to obtain high-quality 3D models from monocular videos, but these are limited to rigid categories or single instances. We present RAC, a method to build category-level 3D models from monocular videos, disentangling variations over instances and motion over time. Three key ideas are introduced to solve this problem: (1) specializing a category-level skeleton to instances, (2) a method for latent space regularization that encourages shared structure across a category while maintaining instance details, and (3) using 3D background models to disentangle objects from the background. We build 3D models for humans, cats, and dogs given monocular videos. Project page: gengshan-y.github.io/rac-www/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Reconstructing_Animatable_Categories_From_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Reconstructing_Animatable_Categories_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.06351 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Reconstructing_Animatable_Categories_From_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Reconstructing_Animatable_Categories_From_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
UDE: A Unified Driving Engine for Human Motion Generation | Zixiang Zhou, Baoyuan Wang | Generating controllable and editable human motion sequences is a key challenge in 3D Avatar generation. It has been labor-intensive to generate and animate human motion for a long time until learning-based approaches have been developed and applied recently. However, these approaches are still task-specific or modality-specific. In this paper, we propose "UDE", the first unified driving engine that enables generating human motion sequences from natural language or audio sequences (see Fig. 1). Specifically, UDE consists of the following key components: 1) a motion quantization module based on VQVAE that represents continuous motion sequence as discrete latent code, 2) a modality-agnostic transformer encoder that learns to map modality-aware driving signals to a joint space, and 3) a unified token transformer (GPT-like) network to predict the quantized latent code index in an auto-regressive manner. 4) a diffusion motion decoder that takes as input the motion tokens and decodes them into motion sequences with high diversity. We evaluate our method on HumanML3D and AIST++ benchmarks, and the experiment results demonstrate our method achieves state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_UDE_A_Unified_Driving_Engine_for_Human_Motion_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_UDE_A_Unified_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16016 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_UDE_A_Unified_Driving_Engine_for_Human_Motion_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_UDE_A_Unified_Driving_Engine_for_Human_Motion_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity 3D Human Digitization From Single 2K Resolution Images | Sang-Hun Han, Min-Gyu Park, Ju Hong Yoon, Ju-Mi Kang, Young-Jae Park, Hae-Gon Jeon | High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The proposed method separately recovers the global shape of a human and its details. The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution depth network merges the global 3D shape and the detailed structures to infer the high-resolution front and back side depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at https://github.com/SangHunHan92/2K2K. In addition, we also provide 2,050 3D human models, including texture maps, 3D joints, and SMPL parameters for research purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_High-Fidelity_3D_Human_Digitization_From_Single_2K_Resolution_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_High-Fidelity_3D_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15108 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_High-Fidelity_3D_Human_Digitization_From_Single_2K_Resolution_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_High-Fidelity_3D_Human_Digitization_From_Single_2K_Resolution_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking | Yang Wu, Huihui Song, Bo Liu, Kaihua Zhang, Dong Liu | The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. Existing CoSOD models by default adopt the group consensus assumption. This brings about model robustness defect under the condition of irrelevant images in the testing image group, which hinders the use of CoSOD models in real-world applications. To address this issue, this paper presents a group exchange-masking (GEM) strategy for robust CoSOD model learning. With two group of image containing different types of salient object as input, the GEM first selects a set of images from each group by the proposed learning based strategy, then these images are exchanged. The proposed feature extraction module considers both the uncertainty caused by the irrelevant images and group consensus in the remaining relevant images. We design a latent variable generator branch which is made of conditional variational autoencoder to generate uncertainly-based global stochastic features. A CoSOD transformer branch is devised to capture the correlation-based local features that contain the group consistency information. At last, the output of two branches are concatenated and fed into a transformer-based decoder, producing robust co-saliency prediction. Extensive evaluations on co-saliency detection with and without irrelevant images demonstrate the superiority of our method over a variety of state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Co-Salient_Object_Detection_With_Uncertainty-Aware_Group_Exchange-Masking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Co-Salient_Object_Detection_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Co-Salient_Object_Detection_With_Uncertainty-Aware_Group_Exchange-Masking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Co-Salient_Object_Detection_With_Uncertainty-Aware_Group_Exchange-Masking_CVPR_2023_paper.html | CVPR 2023 | null |
Tangentially Elongated Gaussian Belief Propagation for Event-Based Incremental Optical Flow Estimation | Jun Nagata, Yusuke Sekikawa | Optical flow estimation is a fundamental functionality in computer vision. An event-based camera, which asynchronously detects sparse intensity changes, is an ideal device for realizing low-latency estimation of the optical flow owing to its low-latency sensing mechanism. An existing method using local plane fitting of events could utilize the sparsity to realize incremental updates for low-latency estimation; however, its output is merely a normal component of the full optical flow. An alternative approach using a frame-based deep neural network could estimate the full flow; however, its intensive non-incremental dense operation prohibits the low-latency estimation. We propose tangentially elongated Gaussian (TEG) belief propagation (BP) that realizes incremental full-flow estimation. We model the probability of full flow as the joint distribution of TEGs from the normal flow measurements, such that the marginal of this distribution with correct prior equals the full flow. We formulate the marginalization using a message-passing based on the BP to realize efficient incremental updates using sparse measurements. In addition to the theoretical justification, we evaluate the effectiveness of the TEGBP in real-world datasets; it outperforms SOTA incremental quasi-full flow method by a large margin. The code will be open-sourced upon acceptance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nagata_Tangentially_Elongated_Gaussian_Belief_Propagation_for_Event-Based_Incremental_Optical_Flow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nagata_Tangentially_Elongated_Gaussian_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nagata_Tangentially_Elongated_Gaussian_Belief_Propagation_for_Event-Based_Incremental_Optical_Flow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nagata_Tangentially_Elongated_Gaussian_Belief_Propagation_for_Event-Based_Incremental_Optical_Flow_CVPR_2023_paper.html | CVPR 2023 | null |
Extracting Class Activation Maps From Non-Discriminative Features As Well | Zhaozheng Chen, Qianru Sun | Extracting class activation maps (CAM) from a classification model often results in poor coverage on foreground objects, i.e., only the discriminative region (e.g., the "head" of "sheep") is recognized and the rest (e.g., the "leg" of "sheep") mistakenly as background. The crux behind is that the weight of the classifier (used to compute CAM) captures only the discriminative features of objects. We tackle this by introducing a new computation method for CAM that explicitly captures non-discriminative features as well, thereby expanding CAM to cover whole objects. Specifically, we omit the last pooling layer of the classification model, and perform clustering on all local features of an object class, where "local" means "at a spatial pixel position". We call the resultant K cluster centers local prototypes - represent local semantics like the "head", "leg", and "body" of "sheep". Given a new image of the class, we compare its unpooled features to every prototype, derive K similarity matrices, and then aggregate them into a heatmap (i.e., our CAM). Our CAM thus captures all local features of the class without discrimination. We evaluate it in the challenging tasks of weakly-supervised semantic segmentation (WSSS), and plug it in multiple state-of-the-art WSSS methods, such as MCTformer and AMN, by simply replacing their original CAM with ours. Our extensive experiments on standard WSSS benchmarks (PASCAL VOC and MS COCO) show the superiority of our method: consistent improvements with little computational overhead. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Extracting_Class_Activation_Maps_From_Non-Discriminative_Features_As_Well_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Extracting_Class_Activation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Extracting_Class_Activation_Maps_From_Non-Discriminative_Features_As_Well_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Extracting_Class_Activation_Maps_From_Non-Discriminative_Features_As_Well_CVPR_2023_paper.html | CVPR 2023 | null |
BlendFields: Few-Shot Example-Driven Facial Modeling | Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi, Virginia Estellers, Kwang Moo Yi, Julien Valentin, Tomasz Trzciński, Marek Kowalski | Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely on geometric face models that cannot represent fine-grained details in texture with a mesh discretization and linear deformation designed to model only a coarse face geometry. We introduce a method that bridges this gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. This blending is performed by measuring local volumetric changes in those expressions and locally reproducing their appearance whenever a similar expression is performed at test time. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kania_BlendFields_Few-Shot_Example-Driven_Facial_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kania_BlendFields_Few-Shot_Example-Driven_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.07514 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kania_BlendFields_Few-Shot_Example-Driven_Facial_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kania_BlendFields_Few-Shot_Example-Driven_Facial_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Sparse Pairwise Loss for Object Re-Identification | Xiao Zhou, Yujie Zhong, Zhen Cheng, Fan Liang, Lin Ma | Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong ReID network. Existing pairwise losses densely exploit each instance as an anchor and sample its triplets in a mini-batch. This dense sampling mechanism inevitably introduces positive pairs that share few visual similarities, which can be harmful to the training. To address this problem, we propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks. Based on the proposed loss framework, we propose an adaptive positive mining strategy that can dynamically adapt to diverse intra-class variations. Extensive experiments show that SP loss and its adaptive variant AdaSP loss outperform other pairwise losses, and achieve state-of-the-art performance across several ReID benchmarks. Code is available at https://github.com/Astaxanthin/AdaSP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Adaptive_Sparse_Pairwise_Loss_for_Object_Re-Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Adaptive_Sparse_Pairwise_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Adaptive_Sparse_Pairwise_Loss_for_Object_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Adaptive_Sparse_Pairwise_Loss_for_Object_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
NeFII: Inverse Rendering for Reflectance Decomposition With Near-Field Indirect Illumination | Haoqian Wu, Zhipeng Hu, Lincheng Li, Yongqiang Zhang, Changjie Fan, Xin Yu | Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images. In order to achieve better decomposition, recent approaches attempt to model indirect illuminations reflected from different materials via Spherical Gaussians (SG), which, however, tends to blur the high-frequency reflection details. In this paper, we propose an end-to-end inverse rendering pipeline that decomposes materials and illumination from multi-view images, while considering near-field indirect illumination. In a nutshell, we introduce the Monte Carlo sampling based path tracing and cache the indirect illumination as neural radiance, enabling a physics-faithful and easy-to-optimize inverse rendering method. To enhance efficiency and practicality, we leverage SG to represent the smooth environment illuminations and apply importance sampling techniques. To supervise indirect illuminations from unobserved directions, we develop a novel radiance consistency constraint between implicit neural radiance and path tracing results of unobserved rays along with the joint optimization of materials and illuminations, thus significantly improving the decomposition performance. Extensive experiments demonstrate that our method outperforms the state-of-the-art on multiple synthetic and real datasets, especially in terms of inter-reflection decomposition. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_NeFII_Inverse_Rendering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16617 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Professional Level Crowd Annotation of Expert Domain Data | Pei Wang, Nuno Vasconcelos | Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed. It is a human-in-the-loop SSL method, where crowd-source workers act as filters of pseudo-labels, replacing the unreliable confidence thresholding used by state-of-the-art SSL methods. To enable annotation by non-experts, classes are specified implicitly, via positive and negative sets of examples and augmented with deliberative explanations, which highlight regions of class ambiguity. In this way, SSL-HF leverages the strong low-shot learning and confidence estimation ability of humans to create an intuitive but effective labeling experience. Experiments show that SSL-HF significantly outperforms various alternative approaches in several benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Towards_Professional_Level_Crowd_Annotation_of_Expert_Domain_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Towards_Professional_Level_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Professional_Level_Crowd_Annotation_of_Expert_Domain_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Professional_Level_Crowd_Annotation_of_Expert_Domain_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Fully Self-Supervised Depth Estimation From Defocus Clue | Haozhe Si, Bin Zhao, Dong Wang, Yunpeng Gao, Mulin Chen, Zhigang Wang, Xuelong Li | Depth-from-defocus (DFD), modeling the relationship between depth and defocus pattern in images, has demonstrated promising performance in depth estimation. Recently, several self-supervised works try to overcome the difficulties in acquiring accurate depth ground-truth. However, they depend on the all-in-focus (AIF) images, which cannot be captured in real-world scenarios. Such limitation discourages the applications of DFD methods. To tackle this issue, we propose a completely self-supervised framework that estimates depth purely from a sparse focal stack. We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions, thus closing the gap between the theoretical success of DFD works and their applications in the real world. In particular, we propose (i) a more realistic setting for DFD tasks, where no depth or AIF image ground-truth is available; (ii) a novel self-supervision framework that provides reliable predictions of depth and AIF image under the the challenging setting. The proposed framework uses a neural model to predict the depth and AIF image, and utilizes an optical model to validate and refine the prediction. We verify our framework on three benchmark datasets with rendered focal stacks and real focal stacks. Qualitative and quantitative evaluations show that our method provides a strong baseline for self-supervised DFD tasks. The source code is publicly available at https://github.com/Ehzoahis/DEReD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Si_Fully_Self-Supervised_Depth_Estimation_From_Defocus_Clue_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Si_Fully_Self-Supervised_Depth_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10752 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Si_Fully_Self-Supervised_Depth_Estimation_From_Defocus_Clue_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Si_Fully_Self-Supervised_Depth_Estimation_From_Defocus_Clue_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Weakly Supervised Object Kinematic Motion Prediction | Gengxin Liu, Qian Sun, Haibin Huang, Chongyang Ma, Yulan Guo, Li Yi, Hui Huang, Ruizhen Hu | Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. Due to the large variations in both topological structure and geometric details of 3D objects, this remains a challenging task and the lack of large scale labeled data also constrain the performance of deep learning based approaches. In this paper, we tackle the task of object kinematic motion prediction problem in a semi-weakly supervised manner. Our key observations are two-fold. First, although 3D dataset with fully annotated motion labels is limited, there are existing datasets and methods for object part semantic segmentation at large scale. Second, semantic part segmentation and mobile part segmentation is not always consistent but it is possible to detect the mobile parts from the underlying 3D structure. Towards this end, we propose a graph neural network to learn the map between hierarchical part-level segmentation and mobile parts parameters, which are further refined based on geometric alignment. This network can be first trained on PartNet-Mobility dataset with fully labeled mobility information and then applied on PartNet dataset with fine-grained and hierarchical part-level segmentation. The network predictions yield a large scale of 3D objects with pseudo labeled mobility information and can further be used for weakly-supervised learning with pre-existing segmentation. Our experiments show there are significant performance boosts with the augmented data for previous method designed for kinematic motion prediction on 3D partial scans. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Semi-Weakly_Supervised_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17774 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances | Zhenqi Fu, Yan Yang, Xiaotong Tu, Yue Huang, Xinghao Ding, Kai-Kuang Ma | Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in low-light conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https://github.com/zhenqifu/PairLIE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Stereo Video Inpainting | Zhiliang Wu, Changchang Sun, Hanyu Xuan, Yan Yan | Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously. Compared with the single video inpainting that has achieved promising results using deep convolutional neural networks, inpainting the missing regions of stereo video has not been thoroughly explored. In essence, apart from the spatial and temporal consistency that single video inpainting needs to achieve, another key challenge for stereo video inpainting is to maintain the stereo consistency between left and right views and hence alleviate the 3D fatigue for viewers. In this paper, we propose a novel deep stereo video inpainting network named SVINet, which is the first attempt for stereo video inpainting task utilizing deep convolutional neural networks. SVINet first utilizes a self-supervised flow-guided deformable temporal alignment module to align the features on the left and right view branches, respectively. Then, the aligned features are fed into a shared adaptive feature aggregation module to generate missing contents of their respective branches. Finally, the parallax attention module (PAM) that uses the cross-view information to consider the significant stereo correlation is introduced to fuse the completed features of left and right views. Furthermore, we develop a stereo consistency loss to regularize the trained parameters, so that our model is able to yield high-quality stereo video inpainting results with better stereo consistency. Experimental results demonstrate that our SVINet outperforms state-of-the-art single video inpainting models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.html | CVPR 2023 | null |
Prompting Large Language Models With Answer Heuristics for Knowledge-Based Visual Question Answering | Zhenwei Shao, Zhou Yu, Meng Wang, Jun Yu | Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have sought to use a large language model (i.e., GPT-3) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of GPT-3 as the provided input information is insufficient. In this paper, we present Prophet---a conceptually simple framework designed to prompt GPT-3 with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61.1% and 55.7% accuracies on their testing sets, respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shao_Prompting_Large_Language_Models_With_Answer_Heuristics_for_Knowledge-Based_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shao_Prompting_Large_Language_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Prompting_Large_Language_Models_With_Answer_Heuristics_for_Knowledge-Based_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Prompting_Large_Language_Models_With_Answer_Heuristics_for_Knowledge-Based_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
IFSeg: Image-Free Semantic Segmentation via Vision-Language Model | Sukmin Yun, Seong Hyeon Park, Paul Hongsuck Seo, Jinwoo Shin | Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional training images or even segmentation annotations to adapt a VL model to downstream segmentation tasks. In this paper, we introduce a novel image-free segmentation task where the goal is to perform semantic segmentation given only a set of the target semantic categories, but without any task-specific images and annotations. To tackle this challenging task, our proposed method, coined IFSeg, generates VL-driven artificial image-segmentation pairs and updates a pre-trained VL model to a segmentation task. We construct this artificial training data by creating a 2D map of random semantic categories and another map of their corresponding word tokens. Given that a pre-trained VL model projects visual and text tokens into a common space where tokens that share the semantics are located closely, this artificially generated word map can replace the real image inputs for such a VL model. Through an extensive set of experiments, our model not only establishes an effective baseline for this novel task but also demonstrates strong performances compared to existing methods that rely on stronger supervision, such as task-specific images and segmentation masks. Code is available at https://github.com/alinlab/ifseg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yun_IFSeg_Image-Free_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Robustness of Semantic Segmentation to Motion-Blur Using Class-Centric Augmentation | Aakanksha, A. N. Rajagopalan | Semantic segmentation involves classifying each pixel into one of a pre-defined set of object/stuff classes. Such a fine-grained detection and localization of objects in the scene is challenging by itself. The complexity increases manifold in the presence of blur. With cameras becoming increasingly light-weight and compact, blur caused by motion during capture time has become unavoidable. Most research has focused on improving segmentation performance for sharp clean images and the few works that deal with degradations, consider motion-blur as one of many generic degradations. In this work, we focus exclusively on motion-blur and attempt to achieve robustness for semantic segmentation in its presence. Based on the observation that segmentation annotations can be used to generate synthetic space-variant blur, we propose a Class-Centric Motion-Blur Augmentation (CCMBA) strategy. Our approach involves randomly selecting a subset of semantic classes present in the image and using the segmentation map annotations to blur only the corresponding regions. This enables the network to simultaneously learn semantic segmentation for clean images, images with egomotion blur, as well as images with dynamic scene blur. We demonstrate the effectiveness of our approach for both CNN and Vision Transformer-based semantic segmentation networks on PASCAL VOC and Cityscapes datasets. We also illustrate the improved generalizability of our method to complex real-world blur by evaluating on the commonly used deblurring datasets GoPro and REDS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Aakanksha_Improving_Robustness_of_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Open Space Expansion for Open-Set Model Attribution | Tianyun Yang, Danding Wang, Fan Tang, Xinying Zhao, Juan Cao, Sheng Tang | Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting limits the application in real-world scenarios for handling contents generated by arbitrary models. In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones. Compared to existing open-set recognition (OSR) tasks focusing on semantic novelty, OSMA is more challenging as the distinction between images from known and unknown models may only lie in visually imperceptible traces. To this end, we propose a Progressive Open Space Expansion (POSE) solution, which simulates open-set samples that maintain the same semantics as closed-set samples but embedded with different imperceptible traces. Guided by a diversity constraint, the open space is simulated progressively by a set of lightweight augmentation models. We consider three real-world scenarios and construct an OSMA benchmark dataset, including unknown models trained with different random seeds, architectures, and datasets from known ones. Extensive experiments on the dataset demonstrate POSE is superior to both existing model attribution methods and off-the-shelf OSR methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Progressive_Open_Space_Expansion_for_Open-Set_Model_Attribution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Progressive_Open_Space_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06877 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Progressive_Open_Space_Expansion_for_Open-Set_Model_Attribution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Progressive_Open_Space_Expansion_for_Open-Set_Model_Attribution_CVPR_2023_paper.html | CVPR 2023 | null |
Backdoor Cleansing With Unlabeled Data | Lu Pang, Tao Sun, Haibin Ling, Chao Chen | Due to the increasing computational demand of Deep Neural Networks (DNNs), companies and organizations have begun to outsource the training process. However, the externally trained DNNs can potentially be backdoor attacked. It is crucial to defend against such attacks, i.e, to postprocess a suspicious model so that its backdoor behavior is mitigated while its normal prediction power on clean inputs remain uncompromised. To remove the abnormal backdoor behavior, existing methods mostly rely on additional labeled clean samples. However, such requirement may be unrealistic as the training data are often unavailable to end users. In this paper, we investigate the possibility of circumventing such barrier. We propose a novel defense method that does not require training labels. Through a carefully designed layer-wise weight re-initialization and knowledge distillation, our method can effectively cleanse backdoor behaviors of a suspicious network with negligible compromise in its normal behavior. In experiments, we show that our method, trained without labels, is on-par with state-of-the-art defense methods trained using labels. We also observe promising defense results even on out-of-distribution data. This makes our method very practical. Code is available at: https://github.com/luluppang/BCU. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pang_Backdoor_Cleansing_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12044 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding | Morris Alper, Michael Fiman, Hadar Averbuch-Elor | Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual reasoning, focusing primarily on zero-shot probing methods. We propose a suite of visual language understanding (VLU) tasks for probing the visual reasoning abilities of text encoder models, as well as various non-visual natural language understanding (NLU) tasks for comparison. We also contribute a novel zero-shot knowledge probing method, Stroop probing, for applying models such as CLIP to text-only tasks without needing a prediction head such as the masked language modelling head of models like BERT. We show that SOTA multimodally trained text encoders outperform unimodally trained text encoders on the VLU tasks while being underperformed by them on the NLU tasks, lending new context to previously mixed results regarding the NLU capabilities of multimodal models. We conclude that exposure to images during pretraining affords inherent visual reasoning knowledge that is reflected in language-only tasks that require implicit visual reasoning. Our findings bear importance in the broader context of multimodal learning, providing principled guidelines for the choice of text encoders used in such contexts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Alper_Is_BERT_Blind_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12513 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.html | CVPR 2023 | null |
PivoTAL: Prior-Driven Supervision for Weakly-Supervised Temporal Action Localization | Mamshad Nayeem Rizve, Gaurav Mittal, Ye Yu, Matthew Hall, Sandra Sajeev, Mubarak Shah, Mei Chen | Weakly-supervised Temporal Action Localization (WTAL) attempts to localize the actions in untrimmed videos using only video-level supervision. Most recent works approach WTAL from a localization-by-classification perspective where these methods try to classify each video frame followed by a manually-designed post-processing pipeline to aggregate these per-frame action predictions into action snippets. Due to this perspective, the model lacks any explicit understanding of action boundaries and tends to focus only on the most discriminative parts of the video resulting in incomplete action localization. To address this, we present PivoTAL, Prior-driven Supervision for Weakly-supervised Temporal Action Localization, to approach WTAL from a localization-by-localization perspective by learning to localize the action snippets directly. To this end, PivoTAL leverages the underlying spatio-temporal regularities in videos in the form of action-specific scene prior, action snippet generation prior, and learnable Gaussian prior to supervise the localization-based training. PivoTAL shows significant improvement (of at least 3% avg mAP) over all existing methods on the benchmark datasets, THUMOS-14 and ActivitNet-v1.3. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rizve_PivoTAL_Prior-Driven_Supervision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Harmonious Feature Learning for Interactive Hand-Object Pose Estimation | Zhifeng Lin, Changxing Ding, Huan Yao, Zengsheng Kuang, Shaoli Huang | Joint hand and object pose estimation from a single image is extremely challenging as serious occlusion often occurs when the hand and object interact. Existing approaches typically first extract coarse hand and object features from a single backbone, then further enhance them with reference to each other via interaction modules. However, these works usually ignore that the hand and object are competitive in feature learning, since the backbone takes both of them as foreground and they are usually mutually occluded. In this paper, we propose a novel Harmonious Feature Learning Network (HFL-Net). HFL-Net introduces a new framework that combines the advantages of single- and double-stream backbones: it shares the parameters of the low- and high-level convolutional layers of a common ResNet-50 model for the hand and object, leaving the middle-level layers unshared. This strategy enables the hand and the object to be extracted as the sole targets by the middle-level layers, avoiding their competition in feature learning. The shared high-level layers also force their features to be harmonious, thereby facilitating their mutual feature enhancement. In particular, we propose to enhance the feature of the hand via concatenation with the feature in the same location from the object stream. A subsequent self-attention layer is adopted to deeply fuse the concatenated feature. Experimental results show that our proposed approach consistently outperforms state-of-the-art methods on the popular HO3D and Dex-YCB databases. Notably, the performance of our model on hand pose estimation even surpasses that of existing works that only perform the single-hand pose estimation task. Code is available at https://github.com/lzfff12/HFL-Net. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Harmonious_Feature_Learning_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
3D GAN Inversion With Facial Symmetry Prior | Fei Yin, Yong Zhang, Xuan Wang, Tengfei Wang, Xiaoyu Li, Yuan Gong, Yanbo Fan, Xiaodong Cun, Ying Shan, Cengiz Oztireli, Yujiu Yang | Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion. Although with the facial prior preserved in pre-trained 3D GANs, reconstructing a 3D portrait with only one monocular image is still an ill-pose problem. The straightforward application of 2D GAN inversion methods focuses on texture similarity only while ignoring the correctness of 3D geometry shapes. It may raise geometry collapse effects, especially when reconstructing a side face under an extreme pose. Besides, the synthetic results in novel views are prone to be blurry. In this work, we propose a novel method to promote 3D GAN inversion by introducing facial symmetry prior. We design a pipeline and constraints to make full use of the pseudo auxiliary view obtained via image flipping, which helps obtain a view-consistent and well-structured geometry shape during the inversion process. To enhance texture fidelity in unobserved viewpoints, pseudo labels from depth-guided 3D warping can provide extra supervision. We design constraints aimed at filtering out conflict areas for optimization in asymmetric situations. Comprehensive quantitative and qualitative evaluations on image reconstruction and editing demonstrate the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_3D_GAN_Inversion_With_Facial_Symmetry_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yin_3D_GAN_Inversion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16927 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_3D_GAN_Inversion_With_Facial_Symmetry_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_3D_GAN_Inversion_With_Facial_Symmetry_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
CLOTH4D: A Dataset for Clothed Human Reconstruction | Xingxing Zou, Xintong Han, Waikeung Wong | Clothed human reconstruction is the cornerstone for creating the virtual world. To a great extent, the quality of recovered avatars decides whether the Metaverse is a passing fad. In this work, we introduce CLOTH4D, a clothed human dataset containing 1,000 subjects with varied appearances, 1,000 3D outfits, and over 100,000 clothed meshes with paired unclothed humans, to fill the gap in large-scale and high-quality 4D clothing data. It enjoys appealing characteristics: 1) Accurate and detailed clothing textured meshes---all clothing items are manually created and then simulated in professional software, strictly following the general standard in fashion design. 2) Separated textured clothing and under-clothing body meshes, closer to the physical world than single-layer raw scans. 3) Clothed human motion sequences simulated given a set of 289 actions, covering fundamental and complicated dynamics. Upon CLOTH4D, we novelly designed a series of temporally-aware metrics to evaluate the temporal stability of the generated 3D human meshes, which has been overlooked previously. Moreover, by assessing and retraining current state-of-the-art clothed human reconstruction methods, we reveal insights, present improved performance, and propose potential future research directions, confirming our dataset's advancement. The dataset is available at www.github.com/AemikaChow/AiDLab-fAshIon-Data | https://openaccess.thecvf.com/content/CVPR2023/papers/Zou_CLOTH4D_A_Dataset_for_Clothed_Human_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zou_CLOTH4D_A_Dataset_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zou_CLOTH4D_A_Dataset_for_Clothed_Human_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zou_CLOTH4D_A_Dataset_for_Clothed_Human_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation | Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander G. Schwing, Liang-Yan Gui | In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, texts, partially observed shapes and combinations of these, further allowing for adjusting the strength of each input. At the core of our approach is an encoder-decoder, compressing 3D shapes into a compact latent representation, upon which a diffusion model is learned. To enable a variety of multi-modal inputs, we employ task-specific encoders with dropout followed by a cross-attention mechanism. Due to its flexibility, our model naturally supports a variety of tasks outperforming prior works on shape completion, image-based 3D reconstruction, and text-to-3D. Most interestingly, our model can combine all these tasks into one swiss-army-knife tool, enabling the user to perform shape generation using incomplete shapes, images, and textual descriptions at the same time, providing the relative weights for each input and facilitating interactivity. Despite our approach being shape-only, we further show an efficient method to texture the generated using large-scale text-to-image models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cheng_SDFusion_Multimodal_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
SMAE: Few-Shot Learning for HDR Deghosting With Saturation-Aware Masked Autoencoders | Qingsen Yan, Song Zhang, Weiye Chen, Hao Tang, Yu Zhu, Jinqiu Sun, Luc Van Gool, Yanning Zhang | Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generate satisfactory images with limited data. However, it is difficult for modern DNNs to avoid overfitting when trained on only a few images. In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR. Unlikely previous methods, directly recovering content and removing ghosts simultaneously, which is hard to achieve optimum, we first generate content of saturated regions with a self-supervised mechanism and then address ghosts via an iterative semi-supervised learning framework. Concretely, considering that saturated regions can be regarded as masking Low Dynamic Range (LDR) input regions, we design a Saturated Mask AutoEncoder (SMAE) to learn a robust feature representation and reconstruct a non-saturated HDR image. We also propose an adaptive pseudo-label selection strategy to pick high-quality HDR pseudo-labels in the second stage to avoid the effect of mislabeled samples. Experiments demonstrate that SSHDR outperforms state-of-the-art methods quantitatively and qualitatively within and across different datasets, achieving appealing HDR visualization with few labeled samples. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.06914 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Generalization With Domain Convex Game | Fangrui Lv, Jian Liang, Shuang Li, Jinming Zhang, Di Liu | Domain generalization (DG) tends to alleviate the poor generalization capability of deep neural networks by learning model with multiple source domains. A classical solution to DG is domain augmentation, the common belief of which is that diversifying source domains will be conducive to the out-of-distribution generalization. However, these claims are understood intuitively, rather than mathematically. Our explorations empirically reveal that the correlation between model generalization and the diversity of domains may be not strictly positive, which limits the effectiveness of domain augmentation. This work therefore aim to guarantee and further enhance the validity of this strand. To this end, we propose a new perspective on DG that recasts it as a convex game between domains. We first encourage each diversified domain to enhance model generalization by elaborately designing a regularization term based on supermodularity. Meanwhile, a sample filter is constructed to eliminate low-quality samples, thereby avoiding the impact of potentially harmful information. Our framework presents a new avenue for the formal analysis of DG, heuristic analysis and extensive experiments demonstrate the rationality and effectiveness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lv_Improving_Generalization_With_Domain_Convex_Game_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lv_Improving_Generalization_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13297 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lv_Improving_Generalization_With_Domain_Convex_Game_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lv_Improving_Generalization_With_Domain_Convex_Game_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Render Novel Views From Wide-Baseline Stereo Pairs | Yilun Du, Cameron Smith, Ayush Tewari, Vincent Sitzmann | We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. In several ablation studies, we demonstrate that our contributions enable learning of powerful multi-view geometry priors while reducing both rendering time and memory footprint. We conduct extensive comparisons on held-out test scenes across two real-world datasets, significantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Learning_To_Render_Novel_Views_From_Wide-Baseline_Stereo_Pairs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Learning_To_Render_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.08463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Learning_To_Render_Novel_Views_From_Wide-Baseline_Stereo_Pairs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Learning_To_Render_Novel_Views_From_Wide-Baseline_Stereo_Pairs_CVPR_2023_paper.html | CVPR 2023 | null |
TryOnDiffusion: A Tale of Two UNets | Luyang Zhu, Dawei Yang, Tyler Zhu, Fitsum Reda, William Chan, Chitwan Saharia, Mohammad Norouzi, Ira Kemelmacher-Shlizerman | Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of the garment, while warping the garment to accommodate a significant body pose and shape change across the subjects. Previous methods either focus on garment detail preservation without effective pose and shape variation, or allow try-on with the desired shape and pose but lack garment details. In this paper, we propose a diffusion-based architecture that unifies two UNets (referred to as Parallel-UNet), which allows us to preserve garment details and warp the garment for significant pose and body change in a single network. The key ideas behind Parallel-UNet include: 1) garment is warped implicitly via a cross attention mechanism, 2) garment warp and person blend happen as part of a unified process as opposed to a sequence of two separate tasks. Experimental results indicate that TryOnDiffusion achieves state-of-the-art performance both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_TryOnDiffusion_A_Tale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.html | CVPR 2023 | null |
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