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Neural Congealing: Aligning Images to a Joint Semantic Atlas | Dolev Ofri-Amar, Michal Geyer, Yoni Kasten, Tali Dekel | We present Neural Congealing -- a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harnesses the power of pre-trained DINO-ViT features to learn: (i) a joint semantic atlas -- a 2D grid that captures the mode of DINO-ViT features in the input set, and (ii) dense mappings from the unified atlas to each of the input images. We derive a new robust self-supervised framework that optimizes the atlas representation and mappings per image set, requiring only a few real-world images as input without any additional input information (e.g., segmentation masks). Notably, we design our losses and training paradigm to account only for the shared content under severe variations in appearance, pose, background clutter or other distracting objects. We demonstrate results on a plethora of challenging image sets including sets of mixed domains (e.g., aligning images depicting sculpture and artwork of cats), sets depicting related yet different object categories (e.g., dogs and tigers), or domains for which large-scale training data is scarce (e.g., coffee mugs). We thoroughly evaluate our method and show that our test-time optimization approach performs favorably compared to a state-of-the-art method that requires extensive training on large-scale datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ofri-Amar_Neural_Congealing_Aligning_Images_to_a_Joint_Semantic_Atlas_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ofri-Amar_Neural_Congealing_Aligning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.03956 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ofri-Amar_Neural_Congealing_Aligning_Images_to_a_Joint_Semantic_Atlas_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ofri-Amar_Neural_Congealing_Aligning_Images_to_a_Joint_Semantic_Atlas_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Spot-Guided Transformer for Consistent Local Feature Matching | Jiahuan Yu, Jiahao Chang, Jianfeng He, Tianzhu Zhang, Jiyang Yu, Feng Wu | Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation. Second, we design an adaptive scaling module to adjust the size of grids according to the calculated depth information at fine stage. Extensive experimental results on five standard benchmarks demonstrate that our ASTR performs favorably against state-of-the-art methods.Our code will be released on https://astr2023.github.io. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Adaptive_Spot-Guided_Transformer_for_Consistent_Local_Feature_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Adaptive_Spot-Guided_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16624 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Adaptive_Spot-Guided_Transformer_for_Consistent_Local_Feature_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Adaptive_Spot-Guided_Transformer_for_Consistent_Local_Feature_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Wide-Angle Rectification via Content-Aware Conformal Mapping | Qi Zhang, Hongdong Li, Qing Wang | Despite the proliferation of ultra wide-angle lenses on smartphone cameras, such lenses often come with severe image distortion (e.g. curved linear structure, unnaturally skewed faces). Most existing rectification methods adopt a global warping transformation to undistort the input wide-angle image, yet their performances are not entirely satisfactory, leaving many unwanted residue distortions uncorrected or at the sacrifice of the intended wide FoV (field-of-view). This paper proposes a new method to tackle these challenges. Specifically, we derive a locally-adaptive polar-domain conformal mapping to rectify a wide-angle image. Parameters of the mapping are found automatically by analyzing image contents via deep neural networks. Experiments on large number of photos have confirmed the superior performance of the proposed method compared with all available previous methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Wide-Angle_Rectification_via_Content-Aware_Conformal_Mapping_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Wide-Angle_Rectification_via_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Wide-Angle_Rectification_via_Content-Aware_Conformal_Mapping_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Wide-Angle_Rectification_via_Content-Aware_Conformal_Mapping_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Stable Human Pose Estimation via Cross-View Fusion and Foot Stabilization | Li’an Zhuo, Jian Cao, Qi Wang, Bang Zhang, Liefeng Bo | Towards stable human pose estimation from monocular images, there remain two main dilemmas. On the one hand, the different perspectives, i.e., front view, side view, and top view, appear the inconsistent performances due to the depth ambiguity. On the other hand, foot posture plays a significant role in complicated human pose estimation, i.e., dance and sports, and foot-ground interaction, but unfortunately, it is omitted in most general approaches and datasets. In this paper, we first propose the Cross-View Fusion (CVF) module to catch up with better 3D intermediate representation and alleviate the view inconsistency based on the vision transformer encoder. Then the optimization-based method is introduced to reconstruct the foot pose and foot-ground contact for the general multi-view datasets including AIST++ and Human3.6M. Besides, the reversible kinematic topology strategy is innovated to utilize the contact information into the full-body with foot pose regressor. Extensive experiments on the popular benchmarks demonstrate that our method outperforms the state-of-the-art approaches by achieving 40.1mm PA-MPJPE on the 3DPW test set and 43.8mm on the AIST++ test set. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhuo_Towards_Stable_Human_Pose_Estimation_via_Cross-View_Fusion_and_Foot_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhuo_Towards_Stable_Human_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhuo_Towards_Stable_Human_Pose_Estimation_via_Cross-View_Fusion_and_Foot_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhuo_Towards_Stable_Human_Pose_Estimation_via_Cross-View_Fusion_and_Foot_CVPR_2023_paper.html | CVPR 2023 | null |
Few-Shot Non-Line-of-Sight Imaging With Signal-Surface Collaborative Regularization | Xintong Liu, Jianyu Wang, Leping Xiao, Xing Fu, Lingyun Qiu, Zuoqiang Shi | The non-line-of-sight imaging technique aims to reconstruct targets from multiply reflected light. For most existing methods, dense points on the relay surface are raster scanned to obtain high-quality reconstructions, which requires a long acquisition time. In this work, we propose a signal-surface collaborative regularization (SSCR) framework that provides noise-robust reconstructions with a minimal number of measurements. Using Bayesian inference, we design joint regularizations of the estimated signal, the 3D voxel-based representation of the objects, and the 2D surface-based description of the targets. To our best knowledge, this is the first work that combines regularizations in mixed dimensions for hidden targets. Experiments on synthetic and experimental datasets illustrated the efficiency of the proposed method under both confocal and non-confocal settings. We report the reconstruction of the hidden targets with complex geometric structures with only 5 x 5 confocal measurements from public datasets, indicating an acceleration of the conventional measurement process by a factor of 10,000. Besides, the proposed method enjoys low time and memory complexity with sparse measurements. Our approach has great potential in real-time non-line-of-sight imaging applications such as rescue operations and autonomous driving. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Few-Shot_Non-Line-of-Sight_Imaging_With_Signal-Surface_Collaborative_Regularization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Few-Shot_Non-Line-of-Sight_Imaging_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.15367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Few-Shot_Non-Line-of-Sight_Imaging_With_Signal-Surface_Collaborative_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Few-Shot_Non-Line-of-Sight_Imaging_With_Signal-Surface_Collaborative_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
SINE: SINgle Image Editing With Text-to-Image Diffusion Models | Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris N. Metaxas, Jian Ren | Recent works on diffusion models have demonstrated a strong capability for conditioning image generation, e.g., text-guided image synthesis. Such success inspires many efforts trying to use large-scale pre-trained diffusion models for tackling a challenging problem--real image editing. Works conducted in this area learn a unique textual token corresponding to several images containing the same object. However, under many circumstances, only one image is available, such as the painting of the Girl with a Pearl Earring. Using existing works on fine-tuning the pre-trained diffusion models with a single image causes severe overfitting issues. The information leakage from the pre-trained diffusion models makes editing can not keep the same content as the given image while creating new features depicted by the language guidance. This work aims to address the problem of single-image editing. We propose a novel model-based guidance built upon the classifier-free guidance so that the knowledge from the model trained on a single image can be distilled into the pre-trained diffusion model, enabling content creation even with one given image. Additionally, we propose a patch-based fine-tuning that can effectively help the model generate images of arbitrary resolution. We provide extensive experiments to validate the design choices of our approach and show promising editing capabilities, including changing style, content addition, and object manipulation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_SINE_SINgle_Image_Editing_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_SINE_SINgle_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04489 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_SINE_SINgle_Image_Editing_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_SINE_SINgle_Image_Editing_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Probabilistic Debiasing of Scene Graphs | Bashirul Azam Biswas, Qiang Ji | The quality of scene graphs generated by the state-of-the-art (SOTA) models is compromised due to the long-tail nature of the relationships and their parent object pairs. Training of the scene graphs is dominated by the majority relationships of the majority pairs and, therefore, the object-conditional distributions of relationship in the minority pairs are not preserved after the training is converged. Consequently, the biased model performs well on more frequent relationships in the marginal distribution of relationships such as 'on' and 'wearing', and performs poorly on the less frequent relationships such as 'eating' or 'hanging from'. In this work, we propose virtual evidence incorporated within-triplet Bayesian Network (BN) to preserve the object-conditional distribution of the relationship label and to eradicate the bias created by the marginal probability of the relationships. The insufficient number of relationships in the minority classes poses a significant problem in learning the within-triplet Bayesian network. We address this insufficiency by embedding-based augmentation of triplets where we borrow samples of the minority triplet classes from its neighboring triplets in the semantic space. We perform experiments on two different datasets and achieve a significant improvement in the mean recall of the relationships. We also achieve a better balance between recall and mean recall performance compared to the SOTA de-biasing techniques of scene graph models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Biswas_Probabilistic_Debiasing_of_Scene_Graphs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Biswas_Probabilistic_Debiasing_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.06444 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Biswas_Probabilistic_Debiasing_of_Scene_Graphs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Biswas_Probabilistic_Debiasing_of_Scene_Graphs_CVPR_2023_paper.html | CVPR 2023 | null |
OSAN: A One-Stage Alignment Network To Unify Multimodal Alignment and Unsupervised Domain Adaptation | Ye Liu, Lingfeng Qiao, Changchong Lu, Di Yin, Chen Lin, Haoyuan Peng, Bo Ren | Extending from unimodal to multimodal is a critical challenge for unsupervised domain adaptation (UDA). Two major problems emerge in unsupervised multimodal domain adaptation: domain adaptation and modality alignment. An intuitive way to handle these two problems is to fulfill these tasks in two separate stages: aligning modalities followed by domain adaptation, or vice versa. However, domains and modalities are not associated in most existing two-stage studies, and the relationship between them is not leveraged which can provide complementary information to each other. In this paper, we unify these two stages into one to align domains and modalities simultaneously. In our model, a tensor-based alignment module (TAL) is presented to explore the relationship between domains and modalities. By this means, domains and modalities can interact sufficiently and guide them to utilize complementary information for better results. Furthermore, to establish a bridge between domains, a dynamic domain generator (DDG) module is proposed to build transitional samples by mixing the shared information of two domains in a self-supervised manner, which helps our model learn a domain-invariant common representation space. Extensive experiments prove that our method can achieve superior performance in two real-world applications. The code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_OSAN_A_One-Stage_Alignment_Network_To_Unify_Multimodal_Alignment_and_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_OSAN_A_One-Stage_Alignment_Network_To_Unify_Multimodal_Alignment_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_OSAN_A_One-Stage_Alignment_Network_To_Unify_Multimodal_Alignment_and_CVPR_2023_paper.html | CVPR 2023 | null |
Token Turing Machines | Michael S. Ryoo, Keerthana Gopalakrishnan, Kumara Kahatapitiya, Ted Xiao, Kanishka Rao, Austin Stone, Yao Lu, Julian Ibarz, Anurag Arnab | We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history (i.e., frames). This memory is efficiently addressed, read and written using a Transformer as the processing unit/controller at each step. The model's memory module ensures that a new observation will only be processed with the contents of the memory (and not the entire history), meaning that it can efficiently process long sequences with a bounded computational cost at each step. We show that TTM outperforms other alternatives, such as other Transformer models designed for long sequences and recurrent neural networks, on two real-world sequential visual understanding tasks: online temporal activity detection from videos and vision-based robot action policy learning. Code is publicly available at: https://github.com/google-research/scenic/tree/main/scenic/projects/token_turing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ryoo_Token_Turing_Machines_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.09119 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ryoo_Token_Turing_Machines_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ryoo_Token_Turing_Machines_CVPR_2023_paper.html | CVPR 2023 | null |
Solving 3D Inverse Problems Using Pre-Trained 2D Diffusion Models | Hyungjin Chung, Dohoon Ryu, Michael T. McCann, Marc L. Klasky, Jong Chul Ye | Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibility. They have also been shown to be effective inverse problem solvers, acting as the prior of the distribution, while the information of the forward model can be granted at the sampling stage. Nonetheless, as the generative process remains in the same high dimensional (i.e. identical to data dimension) space, the models have not been extended to 3D inverse problems due to the extremely high memory and computational cost. In this paper, we combine the ideas from the conventional model-based iterative reconstruction with the modern diffusion models, which leads to a highly effective method for solving 3D medical image reconstruction tasks such as sparse-view tomography, limited angle tomography, compressed sensing MRI from pre-trained 2D diffusion models. In essence, we propose to augment the 2D diffusion prior with a model-based prior in the remaining direction at test time, such that one can achieve coherent reconstructions across all dimensions. Our method can be run in a single commodity GPU, and establishes the new state-of-the-art, showing that the proposed method can perform reconstructions of high fidelity and accuracy even in the most extreme cases (e.g. 2-view 3D tomography). We further reveal that the generalization capacity of the proposed method is surprisingly high, and can be used to reconstruct volumes that are entirely different from the training dataset. Code available: https://github.com/HJ-harry/DiffusionMBIR | https://openaccess.thecvf.com/content/CVPR2023/papers/Chung_Solving_3D_Inverse_Problems_Using_Pre-Trained_2D_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chung_Solving_3D_Inverse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10655 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chung_Solving_3D_Inverse_Problems_Using_Pre-Trained_2D_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chung_Solving_3D_Inverse_Problems_Using_Pre-Trained_2D_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Heat Diffusion Based Multi-Scale and Geometric Structure-Aware Transformer for Mesh Segmentation | Chi-Chong Wong | Triangle mesh segmentation is an important task in 3D shape analysis, especially in applications such as digital humans and AR/VR. Transformer model is inherently permutation-invariant to input, which makes it a suitable candidate model for 3D mesh processing. However, two main challenges involved in adapting Transformer from natural languages to 3D mesh are yet to be solved, such as i) extracting the multi-scale information of mesh data in an adaptive manner; ii) capturing geometric structures of mesh data as the discriminative characteristics of the shape. Current point based Transformer models fail to tackle such challenges and thus provide inferior performance for discretized surface segmentation. In this work, heat diffusion based method is exploited to tackle these problems. A novel Transformer model called MeshFormer is proposed, which i) integrates Heat Diffusion method into Multi-head Self-Attention operation (HDMSA) to adaptively capture the features from local neighborhood to global contexts; ii) applies a novel Heat Kernel Signature based Structure Encoding (HKSSE) to embed the intrinsic geometric structures of mesh instances into Transformer for structure-aware processing. Extensive experiments on triangle mesh segmentation validate the effectiveness of the proposed MeshFormer model and show significant improvements over current state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wong_Heat_Diffusion_Based_Multi-Scale_and_Geometric_Structure-Aware_Transformer_for_Mesh_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wong_Heat_Diffusion_Based_Multi-Scale_and_Geometric_Structure-Aware_Transformer_for_Mesh_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wong_Heat_Diffusion_Based_Multi-Scale_and_Geometric_Structure-Aware_Transformer_for_Mesh_CVPR_2023_paper.html | CVPR 2023 | null |
DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular Automata | Ehsan Pajouheshgar, Yitao Xu, Tong Zhang, Sabine Süsstrunk | Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis process. We propose Dynamic Neural Cellular Automata (DyNCA), a framework for real-time and controllable dynamic texture synthesis. Our method is built upon the recently introduced NCA models and can synthesize infinitely long and arbitrary-size realistic video textures in real-time. We quantitatively and qualitatively evaluate our model and show that our synthesized videos appear more realistic than the existing results. We improve the SOTA DyTS performance by 2 4 orders of magnitude. Moreover, our model offers several real-time video controls including motion speed, motion direction, and an editing brush tool. We exhibit our trained models in an online interactive demo that runs on local hardware and is accessible on personal computers and smartphones. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pajouheshgar_DyNCA_Real-Time_Dynamic_Texture_Synthesis_Using_Neural_Cellular_Automata_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pajouheshgar_DyNCA_Real-Time_Dynamic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11417 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pajouheshgar_DyNCA_Real-Time_Dynamic_Texture_Synthesis_Using_Neural_Cellular_Automata_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pajouheshgar_DyNCA_Real-Time_Dynamic_Texture_Synthesis_Using_Neural_Cellular_Automata_CVPR_2023_paper.html | CVPR 2023 | null |
Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation | Shuting He, Henghui Ding, Wei Jiang | Zero-shot instance segmentation aims to detect and precisely segment objects of unseen categories without any training samples. Since the model is trained on seen categories, there is a strong bias that the model tends to classify all the objects into seen categories. Besides, there is a natural confusion between background and novel objects that have never shown up in training. These two challenges make novel objects hard to be raised in the final instance segmentation results. It is desired to rescue novel objects from background and dominated seen categories. To this end, we propose D^2Zero with Semantic-Promoted Debiasing and Background Disambiguation to enhance the performance of Zero-shot instance segmentation. Semantic-promoted debiasing utilizes inter-class semantic relationships to involve unseen categories in visual feature training and learns an input-conditional classifier to conduct dynamical classification based on the input image. Background disambiguation produces image-adaptive background representation to avoid mistaking novel objects for background. Extensive experiments show that we significantly outperform previous state-of-the-art methods by a large margin, e.g., 16.86% improvement on COCO. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Semantic-Promoted_Debiasing_and_Background_Disambiguation_for_Zero-Shot_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Semantic-Promoted_Debiasing_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Semantic-Promoted_Debiasing_and_Background_Disambiguation_for_Zero-Shot_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Semantic-Promoted_Debiasing_and_Background_Disambiguation_for_Zero-Shot_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
RelightableHands: Efficient Neural Relighting of Articulated Hand Models | Shun Iwase, Shunsuke Saito, Tomas Simon, Stephen Lombardi, Timur Bagautdinov, Rohan Joshi, Fabian Prada, Takaaki Shiratori, Yaser Sheikh, Jason Saragih | We present the first neural relighting approach for rendering high-fidelity personalized hands that can be animated in real-time under novel illumination. Our approach adopts a teacher-student framework, where the teacher learns appearance under a single point light from images captured in a light-stage, allowing us to synthesize hands in arbitrary illuminations but with heavy compute. Using images rendered by the teacher model as training data, an efficient student model directly predicts appearance under natural illuminations in real-time. To achieve generalization, we condition the student model with physics-inspired illumination features such as visibility, diffuse shading, and specular reflections computed on a coarse proxy geometry, maintaining a small computational overhead. Our key insight is that these features have strong correlation with subsequent global light transport effects, which proves sufficient as conditioning data for the neural relighting network. Moreover, in contrast to bottleneck illumination conditioning, these features are spatially aligned based on underlying geometry, leading to better generalization to unseen illuminations and poses. In our experiments, we demonstrate the efficacy of our illumination feature representations, outperforming baseline approaches. We also show that our approach can photorealistically relight two interacting hands at real-time speeds. | https://openaccess.thecvf.com/content/CVPR2023/papers/Iwase_RelightableHands_Efficient_Neural_Relighting_of_Articulated_Hand_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Iwase_RelightableHands_Efficient_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.04866 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Iwase_RelightableHands_Efficient_Neural_Relighting_of_Articulated_Hand_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Iwase_RelightableHands_Efficient_Neural_Relighting_of_Articulated_Hand_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Paired-Point Lifting for Enhanced Privacy-Preserving Visual Localization | Chunghwan Lee, Jaihoon Kim, Chanhyuk Yun, Je Hyeong Hong | Visual localization refers to the process of recovering camera pose from input image relative to a known scene, forming a cornerstone of numerous vision and robotics systems. While many algorithms utilize sparse 3D point cloud of the scene obtained via structure-from-motion (SfM) for localization, recent studies have raised privacy concerns by successfully revealing high-fidelity appearance of the scene from such sparse 3D representation. One prominent approach for bypassing this attack was to lift 3D points to randomly oriented 3D lines thereby hiding scene geometry, but latest work have shown such random line cloud has a critical statistical flaw that can be exploited to break through protection. In this work, we present an alternative lightweight strategy called Paired-Point Lifting (PPL) for constructing 3D line clouds. Instead of drawing one randomly oriented line per 3D point, PPL splits 3D points into pairs and joins each pair to form 3D lines. This seemingly simple strategy yields 3 benefits, i) new ambiguity in feature selection, ii) increased line cloud sparsity, and iii) non-trivial distribution of 3D lines, all of which contributes to enhanced protection against privacy attacks. Extensive experimental results demonstrate the strength of PPL in concealing scene details without compromising localization accuracy, unlocking the true potential of 3D line clouds. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Paired-Point_Lifting_for_Enhanced_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Paired-Point_Lifting_for_Enhanced_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Paired-Point_Lifting_for_Enhanced_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Depth Estimation From Camera Image and mmWave Radar Point Cloud | Akash Deep Singh, Yunhao Ba, Ankur Sarker, Howard Zhang, Achuta Kadambi, Stefano Soatto, Mani Srivastava, Alex Wong | We present a method for inferring dense depth from a camera image and a sparse noisy radar point cloud. We first describe the mechanics behind mmWave radar point cloud formation and the challenges that it poses, i.e. ambiguous elevation and noisy depth and azimuth components that yields incorrect positions when projected onto the image, and how existing works have overlooked these nuances in camera-radar fusion. Our approach is motivated by these mechanics, leading to the design of a network that maps each radar point to the possible surfaces that it may project onto in the image plane. Unlike existing works, we do not process the raw radar point cloud as an erroneous depth map, but query each raw point independently to associate it with likely pixels in the image -- yielding a semi-dense radar depth map. To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map. We test our method on the NuScenes benchmark and show a 10.3% improvement in mean absolute error and a 9.1% improvement in root-mean-square error over the best method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Singh_Depth_Estimation_From_Camera_Image_and_mmWave_Radar_Point_Cloud_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Singh_Depth_Estimation_From_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_Depth_Estimation_From_Camera_Image_and_mmWave_Radar_Point_Cloud_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_Depth_Estimation_From_Camera_Image_and_mmWave_Radar_Point_Cloud_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Event Guided High Dynamic Range Video Reconstruction | Yixin Yang, Jin Han, Jinxiu Liang, Imari Sato, Boxin Shi | Limited by the trade-off between frame rate and exposure time when capturing moving scenes with conventional cameras, frame based HDR video reconstruction suffers from scene-dependent exposure ratio balancing and ghosting artifacts. Event cameras provide an alternative visual representation with a much higher dynamic range and temporal resolution free from the above issues, which could be an effective guidance for HDR imaging from LDR videos. In this paper, we propose a multimodal learning framework for event guided HDR video reconstruction. In order to better leverage the knowledge of the same scene from the two modalities of visual signals, a multimodal representation alignment strategy to learn a shared latent space and a fusion module tailored to complementing two types of signals for different dynamic ranges in different regions are proposed. Temporal correlations are utilized recurrently to suppress the flickering effects in the reconstructed HDR video. The proposed HDRev-Net demonstrates state-of-the-art performance quantitatively and qualitatively for both synthetic and real-world data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Learning_Event_Guided_High_Dynamic_Range_Video_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Learning_Event_Guided_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Learning_Event_Guided_High_Dynamic_Range_Video_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Learning_Event_Guided_High_Dynamic_Range_Video_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph | Rixin Zhou, Jiafu Wei, Qian Zhang, Ruihua Qi, Xi Yang, Chuntao Li | The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://github.com/zhourixin/bronze-Ding. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Multi-Granularity_Archaeological_Dating_of_Chinese_Bronze_Dings_Based_on_a_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Multi-Granularity_Archaeological_Dating_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.15266 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Multi-Granularity_Archaeological_Dating_of_Chinese_Bronze_Dings_Based_on_a_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Multi-Granularity_Archaeological_Dating_of_Chinese_Bronze_Dings_Based_on_a_CVPR_2023_paper.html | CVPR 2023 | null |
CASP-Net: Rethinking Video Saliency Prediction From an Audio-Visual Consistency Perceptual Perspective | Junwen Xiong, Ganglai Wang, Peng Zhang, Wei Huang, Yufei Zha, Guangtao Zhai | Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain. By focusing on the benefits of joint auditory and visual information, most VSP methods are capable of exploiting semantic correlation between vision and audio modalities but ignoring the negative effects due to the temporal inconsistency of audio-visual intrinsics. Inspired by the biological inconsistency-correction within multi-sensory information, in this study, a consistency-aware audio-visual saliency prediction network (CASP-Net) is proposed, which takes a comprehensive consideration of the audio-visual semantic interaction and consistent perception. In addition a two-stream encoder for elegant association between video frames and corresponding sound source, a novel consistency-aware predictive coding is also designed to improve the consistency within audio and visual representations iteratively. To further aggregate the multi-scale audio-visual information, a saliency decoder is introduced for the final saliency map generation. Substantial experiments demonstrate that the proposed CASP-Net outperforms the other state-of-the-art methods on six challenging audio-visual eye-tracking datasets. For a demo of our system please see https://woshihaozhu.github.io/CASP-Net/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiong_CASP-Net_Rethinking_Video_Saliency_Prediction_From_an_Audio-Visual_Consistency_Perceptual_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_CASP-Net_Rethinking_Video_Saliency_Prediction_From_an_Audio-Visual_Consistency_Perceptual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_CASP-Net_Rethinking_Video_Saliency_Prediction_From_an_Audio-Visual_Consistency_Perceptual_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Expressive Prompting With Residuals for Vision Transformers | Rajshekhar Das, Yonatan Dukler, Avinash Ravichandran, Ashwin Swaminathan | Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adaptation of vision transformers (ViT). Out method constructs downstream representations via learnable "output" tokens, that are akin to the learned class tokens of the ViT. Further for better steering of the downstream representation processed by the frozen transformer, we introduce residual learnable tokens that are added to the output of various computations. We apply EXPRES for image classification, few shot learning, and semantic segmentation, and show our method is capable of achieving state of the art prompt tuning on 3/3 categories of the VTAB benchmark. In addition to strong performance, we observe that our approach is an order of magnitude more prompt efficient than existing visual prompting baselines. We analytically show the computational benefits of our approach over weight space adaptation techniques like finetuning. Lastly we systematically corroborate the architectural design of our method via a series of ablation experiments. | https://openaccess.thecvf.com/content/CVPR2023/papers/Das_Learning_Expressive_Prompting_With_Residuals_for_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Das_Learning_Expressive_Prompting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15591 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Das_Learning_Expressive_Prompting_With_Residuals_for_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Das_Learning_Expressive_Prompting_With_Residuals_for_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Prototypical Residual Networks for Anomaly Detection and Localization | Hui Zhang, Zuxuan Wu, Zheng Wang, Zhineng Chen, Yu-Gang Jiang | Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multi-size self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Prototypical_Residual_Networks_for_Anomaly_Detection_and_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Prototypical_Residual_Networks_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Prototypical_Residual_Networks_for_Anomaly_Detection_and_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Prototypical_Residual_Networks_for_Anomaly_Detection_and_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
What Happened 3 Seconds Ago? Inferring the Past With Thermal Imaging | Zitian Tang, Wenjie Ye, Wei-Chiu Ma, Hang Zhao | Inferring past human motion from RGB images is challenging due to the inherent uncertainty of the prediction problem. Thermal images, on the other hand, encode traces of past human-object interactions left in the environment via thermal radiation measurement. Based on this observation, we collect the first RGB-Thermal dataset for human motion analysis, dubbed Thermal-IM. Then we develop a three-stage neural network model for accurate past human pose estimation. Comprehensive experiments show that thermal cues significantly reduce the ambiguities of this task, and the proposed model achieves remarkable performance. The dataset is available at https://github.com/ZitianTang/Thermal-IM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_What_Happened_3_Seconds_Ago_Inferring_the_Past_With_Thermal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_What_Happened_3_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.13651 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_What_Happened_3_Seconds_Ago_Inferring_the_Past_With_Thermal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_What_Happened_3_Seconds_Ago_Inferring_the_Past_With_Thermal_CVPR_2023_paper.html | CVPR 2023 | null |
Ultrahigh Resolution Image/Video Matting With Spatio-Temporal Sparsity | Yanan Sun, Chi-Keung Tang, Yu-Wing Tai | Commodity ultra-high definition (UHD) displays are becoming more affordable which demand imaging in ultra high resolution (UHR). This paper proposes SparseMat, a computationally efficient approach for UHR image/video matting. Note that it is infeasible to directly process UHR images at full resolution in one shot using existing matting algorithms without running out of memory on consumer-level computational platforms, e.g., Nvidia 1080Ti with 11G memory, while patch-based approaches can introduce unsightly artifacts due to patch partitioning. Instead, our method resorts to spatial and temporal sparsity for solving general UHR matting. During processing videos, huge computation redundancy can be reduced through the rational use of spatial and temporal sparsity. In this paper, we show how to effectively estimate spatio-temporal sparsity, which serves as a gate to activate input pixels for the matting model. Under the guidance of such sparsity, our method discards patch-based inference in lieu of memory-efficient and full-resolution matte refinement. Extensive experiments demonstrate that SparseMat can effectively and efficiently generate high-quality alpha matte for UHR images and videos in one shot. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Ultrahigh_Resolution_ImageVideo_Matting_With_Spatio-Temporal_Sparsity_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Ultrahigh_Resolution_ImageVideo_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Ultrahigh_Resolution_ImageVideo_Matting_With_Spatio-Temporal_Sparsity_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Ultrahigh_Resolution_ImageVideo_Matting_With_Spatio-Temporal_Sparsity_CVPR_2023_paper.html | CVPR 2023 | null |
AnyFlow: Arbitrary Scale Optical Flow With Implicit Neural Representation | Hyunyoung Jung, Zhuo Hui, Lei Luo, Haitao Yang, Feng Liu, Sungjoo Yoo, Rakesh Ranjan, Denis Demandolx | To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs. However, downsizing inputs makes the estimation more challenging because objects and motion ranges become smaller. Even though recent approaches have demonstrated high-quality flow estimation, they tend to fail to accurately model small objects and precise boundaries when the input resolution is lowered, restricting their applicability to high-resolution inputs. In this paper, we introduce AnyFlow, a robust network that estimates accurate flow from images of various resolutions. By representing optical flow as a continuous coordinate-based representation, AnyFlow generates outputs at arbitrary scales from low-resolution inputs, demonstrating superior performance over prior works in capturing tiny objects with detail preservation on a wide range of scenes. We establish a new state-of-the-art performance of cross-dataset generalization on the KITTI dataset, while achieving comparable accuracy on the online benchmarks to other SOTA methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jung_AnyFlow_Arbitrary_Scale_Optical_Flow_With_Implicit_Neural_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jung_AnyFlow_Arbitrary_Scale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_AnyFlow_Arbitrary_Scale_Optical_Flow_With_Implicit_Neural_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_AnyFlow_Arbitrary_Scale_Optical_Flow_With_Implicit_Neural_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Noise2Noise: Efficient Image Denoising Without Any Data | Youssef Mansour, Reinhard Heckel | Recently, self-supervised neural networks have shown excellent image denoising performance. However, current dataset free methods are either computationally expensive, require a noise model, or have inadequate image quality. In this work we show that a simple 2-layer network, without any training data or knowledge of the noise distribution, can enable high-quality image denoising at low computational cost. Our approach is motivated by Noise2Noise and Neighbor2Neighbor and works well for denoising pixel-wise independent noise. Our experiments on artificial, real-world camera, and microscope noise show that our method termed ZS-N2N (Zero Shot Noise2Noise) often outperforms existing dataset-free methods at a reduced cost, making it suitable for use cases with scarce data availability and limited compute. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mansour_Zero-Shot_Noise2Noise_Efficient_Image_Denoising_Without_Any_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mansour_Zero-Shot_Noise2Noise_Efficient_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11253 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mansour_Zero-Shot_Noise2Noise_Efficient_Image_Denoising_Without_Any_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mansour_Zero-Shot_Noise2Noise_Efficient_Image_Denoising_Without_Any_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Vector Quantization With Self-Attention for Quality-Independent Representation Learning | Zhou Yang, Weisheng Dong, Xin Li, Mengluan Huang, Yulin Sun, Guangming Shi | Recently, the robustness of deep neural networks has drawn extensive attention due to the potential distribution shift between training and testing data (e.g., deep models trained on high-quality images are sensitive to corruption during testing). Many researchers attempt to make the model learn invariant representations from multiple corrupted data through data augmentation or image-pair-based feature distillation to improve the robustness. Inspired by sparse representation in image restoration, we opt to address this issue by learning image-quality-independent feature representation in a simple plug-and-play manner, that is, to introduce discrete vector quantization (VQ) to remove redundancy in recognition models. Specifically, we first add a codebook module to the network to quantize deep features. Then we concatenate them and design a self-attention module to enhance the representation. During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images. Qualitative and quantitative experimental results show that our method achieved this goal effectively, leading to a new state-of-the-art result of 43.1% mCE on ImageNet-C with ResNet50 as the backbone. On other robustness benchmark datasets, such as ImageNet-R, our method also has an accuracy improvement of almost 2%. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Vector_Quantization_With_Self-Attention_for_Quality-Independent_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Vector_Quantization_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Vector_Quantization_With_Self-Attention_for_Quality-Independent_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Vector_Quantization_With_Self-Attention_for_Quality-Independent_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Generating Anomalies for Video Anomaly Detection With Prompt-Based Feature Mapping | Zuhao Liu, Xiao-Ming Wu, Dian Zheng, Kun-Yu Lin, Wei-Shi Zheng | Anomaly detection in surveillance videos is a challenging computer vision task where only normal videos are available during training. Recent work released the first virtual anomaly detection dataset to assist real-world detection. However, an anomaly gap exists because the anomalies are bounded in the virtual dataset but unbounded in the real world, so it reduces the generalization ability of the virtual dataset. There also exists a scene gap between virtual and real scenarios, including scene-specific anomalies (events that are abnormal in one scene but normal in another) and scene-specific attributes, such as the viewpoint of the surveillance camera. In this paper, we aim to solve the problem of the anomaly gap and scene gap by proposing a prompt-based feature mapping framework (PFMF). The PFMF contains a mapping network guided by an anomaly prompt to generate unseen anomalies with unbounded types in the real scenario, and a mapping adaptation branch to narrow the scene gap by applying domain classifier and anomaly classifier. The proposed framework outperforms the state-of-the-art on three benchmark datasets. Extensive ablation experiments also show the effectiveness of our framework design. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Generating_Anomalies_for_Video_Anomaly_Detection_With_Prompt-Based_Feature_Mapping_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Generating_Anomalies_for_Video_Anomaly_Detection_With_Prompt-Based_Feature_Mapping_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Generating_Anomalies_for_Video_Anomaly_Detection_With_Prompt-Based_Feature_Mapping_CVPR_2023_paper.html | CVPR 2023 | null |
Diffusion-Based Signed Distance Fields for 3D Shape Generation | Jaehyeok Shim, Changwoo Kang, Kyungdon Joo | We propose a 3D shape generation framework (SDF-Diffusion in short) that uses denoising diffusion models with continuous 3D representation via signed distance fields (SDF). Unlike most existing methods that depend on discontinuous forms, such as point clouds, SDF-Diffusion generates high-resolution 3D shapes while alleviating memory issues by separating the generative process into two-stage: generation and super-resolution. In the first stage, a diffusion-based generative model generates a low-resolution SDF of 3D shapes. Using the estimated low-resolution SDF as a condition, the second stage diffusion model performs super-resolution to generate high-resolution SDF. Our framework can generate a high-fidelity 3D shape despite the extreme spatial complexity. On the ShapeNet dataset, our model shows competitive performance to the state-of-the-art methods and shows applicability on the shape completion task without modification. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shim_Diffusion-Based_Signed_Distance_Fields_for_3D_Shape_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shim_Diffusion-Based_Signed_Distance_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shim_Diffusion-Based_Signed_Distance_Fields_for_3D_Shape_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shim_Diffusion-Based_Signed_Distance_Fields_for_3D_Shape_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition From Egocentric RGB Videos | Yilin Wen, Hao Pan, Lei Yang, Jia Pan, Taku Komura, Wenping Wang | Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame pose and object information over a longer time span to recognize the action. Our approach achieves competitive results on two first-person hand action benchmarks, namely FPHA and H2O. Extensive ablation studies verify our design choices. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wen_Hierarchical_Temporal_Transformer_for_3D_Hand_Pose_Estimation_and_Action_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wen_Hierarchical_Temporal_Transformer_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2209.09484 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Hierarchical_Temporal_Transformer_for_3D_Hand_Pose_Estimation_and_Action_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_Hierarchical_Temporal_Transformer_for_3D_Hand_Pose_Estimation_and_Action_CVPR_2023_paper.html | CVPR 2023 | null |
CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer | Linfeng Wen, Chengying Gao, Changqing Zou | Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer. This paper proposes a new framework named CAP-VSTNet, which consists of a new reversible residual network and an unbiased linear transform module, for versatile style transfer. This reversible residual network can not only preserve content affinity but not introduce redundant information as traditional reversible networks, and hence facilitate better stylization. Empowered by Matting Laplacian training loss which can address the pixel affinity loss problem led by the linear transform, the proposed framework is applicable and effective on versatile style transfer. Extensive experiments show that CAP-VSTNet can produce better qualitative and quantitative results in comparison with the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wen_CAP-VSTNet_Content_Affinity_Preserved_Versatile_Style_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wen_CAP-VSTNet_Content_Affinity_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_CAP-VSTNet_Content_Affinity_Preserved_Versatile_Style_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_CAP-VSTNet_Content_Affinity_Preserved_Versatile_Style_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits | Polina Karpikova, Ekaterina Radionova, Anastasia Yaschenko, Andrei Spiridonov, Leonid Kostyushko, Riccardo Fabbricatore, Aleksei Ivakhnenko | Generative DNNs are a powerful tool for image synthesis, but they are limited by their computational load. On the other hand, given a trained model and a task, e.g. faces generation within a range of characteristics, the output image quality will be unevenly distributed among images with different characteristics. It follows, that we might restrain the model's complexity on some instances, maintaining a high quality. We propose a method for diminishing computations by adding so-called early exit branches to the original architecture, and dynamically switching the computational path depending on how difficult it will be to render the output. We apply our method on two different SOTA models performing generative tasks: generation from a semantic map, and cross reenactment of face expressions; showing it is able to output images with custom lower quality thresholds. For a threshold of LPIPS <=0.1, we diminish their computations by up to a half. This is especially relevant for real-time applications such as synthesis of faces, when quality loss needs to be contained, but most of the inputs need fewer computations than the complex instances. | https://openaccess.thecvf.com/content/CVPR2023/papers/Karpikova_FIANCEE_Faster_Inference_of_Adversarial_Networks_via_Conditional_Early_Exits_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Karpikova_FIANCEE_Faster_Inference_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.10306 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Karpikova_FIANCEE_Faster_Inference_of_Adversarial_Networks_via_Conditional_Early_Exits_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Karpikova_FIANCEE_Faster_Inference_of_Adversarial_Networks_via_Conditional_Early_Exits_CVPR_2023_paper.html | CVPR 2023 | null |
Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation | Jiangwei Lao, Weixiang Hong, Xin Guo, Yingying Zhang, Jian Wang, Jingdong Chen, Wei Chu | In order to tackle video semantic segmentation task at a lower cost, e.g., only one frame annotated per video, lots of efforts have been devoted to investigate the utilization of those unlabeled frames by either assigning pseudo labels or performing feature enhancement. In this work, we propose a novel feature enhancement network to simultaneously model short- and long-term temporal correlation. Compared with existing work that only leverage short-term correspondence, the long-term temporal correlation obtained from distant frames can effectively expand the temporal perception field and provide richer contextual prior. More importantly, modeling adjacent and distant frames together can alleviate the risk of over-fitting, hence produce high-quality feature representation for the distant unlabeled frames in training set and unseen videos in testing set. To this end, we term our method SSLTM, short for Simultaneously Short- and Long-Term Temporal Modeling. In the setting of only one frame annotated per video, SSLTM significantly outperforms the state-of-the-art methods by 2% 3% mIoU on the challenging VSPW dataset. Furthermore, when working with a pseudo label based method such as MeanTeacher, our final model only exhibits 0.13% mIoU less than the ceiling performance (i.e., all frames are manually annotated). | https://openaccess.thecvf.com/content/CVPR2023/papers/Lao_Simultaneously_Short-_and_Long-Term_Temporal_Modeling_for_Semi-Supervised_Video_Semantic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lao_Simultaneously_Short-_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lao_Simultaneously_Short-_and_Long-Term_Temporal_Modeling_for_Semi-Supervised_Video_Semantic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lao_Simultaneously_Short-_and_Long-Term_Temporal_Modeling_for_Semi-Supervised_Video_Semantic_CVPR_2023_paper.html | CVPR 2023 | null |
Federated Domain Generalization With Generalization Adjustment | Ruipeng Zhang, Qinwei Xu, Jiangchao Yao, Ya Zhang, Qi Tian, Yanfeng Wang | Federated Domain Generalization (FedDG) attempts to learn a global model in a privacy-preserving manner that generalizes well to new clients possibly with domain shift. Recent exploration mainly focuses on designing an unbiased training strategy within each individual domain. However, without the support of multi-domain data jointly in the mini-batch training, almost all methods cannot guarantee the generalization under domain shift. To overcome this problem, we propose a novel global objective incorporating a new variance reduction regularizer to encourage fairness. A novel FL-friendly method named Generalization Adjustment (GA) is proposed to optimize the above objective by dynamically calibrating the aggregation weights. The theoretical analysis of GA demonstrates the possibility to achieve a tighter generalization bound with an explicit re-weighted aggregation, substituting the implicit multi-domain data sharing that is only applicable to the conventional DG settings. Besides, the proposed algorithm is generic and can be combined with any local client training-based methods. Extensive experiments on several benchmark datasets have shown the effectiveness of the proposed method, with consistent improvements over several FedDG algorithms when used in combination. The source code is released at https://github.com/MediaBrain-SJTU/FedDG-GA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Federated_Domain_Generalization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.html | CVPR 2023 | null |
Tunable Convolutions With Parametric Multi-Loss Optimization | Matteo Maggioni, Thomas Tanay, Francesca Babiloni, Steven McDonagh, Aleš Leonardis | Behavior of neural networks is irremediably determined by the specific loss and data used during training. However it is often desirable to tune the model at inference time based on external factors such as preferences of the user or dynamic characteristics of the data. This is especially important to balance the perception-distortion trade-off of ill-posed image-to-image translation tasks. In this work, we propose to optimize a parametric tunable convolutional layer, which includes a number of different kernels, using a parametric multi-loss, which includes an equal number of objectives. Our key insight is to use a shared set of parameters to dynamically interpolate both the objectives and the kernels. During training, these parameters are sampled at random to explicitly optimize all possible combinations of objectives and consequently disentangle their effect into the corresponding kernels. During inference, these parameters become interactive inputs of the model hence enabling reliable and consistent control over the model behavior. Extensive experimental results demonstrate that our tunable convolutions effectively work as a drop-in replacement for traditional convolutions in existing neural networks at virtually no extra computational cost, outperforming state-of-the-art control strategies in a wide range of applications; including image denoising, deblurring, super-resolution, and style transfer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Maggioni_Tunable_Convolutions_With_Parametric_Multi-Loss_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Maggioni_Tunable_Convolutions_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00898 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Maggioni_Tunable_Convolutions_With_Parametric_Multi-Loss_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Maggioni_Tunable_Convolutions_With_Parametric_Multi-Loss_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs | Junbum Cha, Jonghwan Mun, Byungseok Roh | We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task. However, these CL-based methods suffer from a train-test discrepancy, since it only considers image-text alignment during training, whereas segmentation requires region-text alignment during testing. In this paper, we proposed a novel Text-grounded Contrastive Learning (TCL) framework that enables a model to directly learn region-text alignment. Our method generates a segmentation mask for a given text, extracts text-grounded image embedding from the masked region, and aligns it with text embedding via TCL. By learning region-text alignment directly, our framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performances with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cha_Learning_To_Generate_Text-Grounded_Mask_for_Open-World_Semantic_Segmentation_From_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cha_Learning_To_Generate_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cha_Learning_To_Generate_Text-Grounded_Mask_for_Open-World_Semantic_Segmentation_From_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cha_Learning_To_Generate_Text-Grounded_Mask_for_Open-World_Semantic_Segmentation_From_CVPR_2023_paper.html | CVPR 2023 | null |
CoMFormer: Continual Learning in Semantic and Panoptic Segmentation | Fabio Cermelli, Matthieu Cord, Arthur Douillard | Continual learning for segmentation has recently seen increasing interest. However, all previous works focus on narrow semantic segmentation and disregard panoptic segmentation, an important task with real-world impacts. In this paper, we present the first continual learning model capable of operating on both semantic and panoptic segmentation. Inspired by recent transformer approaches that consider segmentation as a mask-classification problem, we design CoMFormer. Our method carefully exploits the properties of transformer architectures to learn new classes over time. Specifically, we propose a novel adaptive distillation loss along with a mask-based pseudo-labeling technique to effectively prevent forgetting. To evaluate our approach, we introduce a novel continual panoptic segmentation benchmark on the challenging ADE20K dataset. Our CoMFormer outperforms all the existing baselines by forgetting less old classes but also learning more effectively new classes. In addition, we also report an extensive evaluation in the large-scale continual semantic segmentation scenario showing that CoMFormer also significantly outperforms state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cermelli_CoMFormer_Continual_Learning_in_Semantic_and_Panoptic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cermelli_CoMFormer_Continual_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13999 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cermelli_CoMFormer_Continual_Learning_in_Semantic_and_Panoptic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cermelli_CoMFormer_Continual_Learning_in_Semantic_and_Panoptic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
DeepSolo: Let Transformer Decoder With Explicit Points Solo for Text Spotting | Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao | End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. In this paper, we present DeepSolo, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Besides, we also introduce a text-matching criterion to deliver more accurate supervisory signals, thus enabling more efficient training. Quantitative experiments on public benchmarks demonstrate that DeepSolo outperforms previous state-of-the-art methods and achieves better training efficiency. In addition, DeepSolo is also compatible with line annotations, which require much less annotation cost than polygons. The code is available at https://github.com/ViTAE-Transformer/DeepSolo. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_DeepSolo_Let_Transformer_Decoder_With_Explicit_Points_Solo_for_Text_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_DeepSolo_Let_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10772 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_DeepSolo_Let_Transformer_Decoder_With_Explicit_Points_Solo_for_Text_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_DeepSolo_Let_Transformer_Decoder_With_Explicit_Points_Solo_for_Text_CVPR_2023_paper.html | CVPR 2023 | null |
Conditional Generation of Audio From Video via Foley Analogies | Yuexi Du, Ziyang Chen, Justin Salamon, Bryan Russell, Andrew Owens | The sound effects that designers add to videos are designed to convey a particular artistic effect and, thus, may be quite different from a scene's true sound. Inspired by the challenges of creating a soundtrack for a video that differs from its true sound, but that nonetheless matches the actions occurring on screen, we propose the problem of conditional Foley. We present the following contributions to address this problem. First, we propose a pretext task for training our model to predict sound for an input video clip using a conditional audio-visual clip sampled from another time within the same source video. Second, we propose a model for generating a soundtrack for a silent input video, given a user-supplied example that specifies what the video should "sound like". We show through human studies and automated evaluation metrics that our model successfully generates sound from video, while varying its output according to the content of a supplied example. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Conditional_Generation_of_Audio_From_Video_via_Foley_Analogies_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Conditional_Generation_of_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.08490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Conditional_Generation_of_Audio_From_Video_via_Foley_Analogies_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Conditional_Generation_of_Audio_From_Video_via_Foley_Analogies_CVPR_2023_paper.html | CVPR 2023 | null |
Diverse 3D Hand Gesture Prediction From Body Dynamics by Bilateral Hand Disentanglement | Xingqun Qi, Chen Liu, Muyi Sun, Lincheng Li, Changjie Fan, Xin Yu | Predicting natural and diverse 3D hand gestures from the upper body dynamics is a practical yet challenging task in virtual avatar creation. Previous works usually overlook the asymmetric motions between two hands and generate two hands in a holistic manner, leading to unnatural results. In this work, we introduce a novel bilateral hand disentanglement based two-stage 3D hand generation method to achieve natural and diverse 3D hand prediction from body dynamics. In the first stage, we intend to generate natural hand gestures by two hand-disentanglement branches. Considering the asymmetric gestures and motions of two hands, we introduce a Spatial-Residual Memory (SRM) module to model spatial interaction between the body and each hand by residual learning. To enhance the coordination of two hand motions wrt. body dynamics holistically, we then present a Temporal-Motion Memory (TMM) module. TMM can effectively model the temporal association between body dynamics and two hand motions. The second stage is built upon the insight that 3D hand predictions should be non-deterministic given the sequential body postures. Thus, we further diversify our 3D hand predictions based on the initial output from the stage one. Concretely, we propose a Prototypical-Memory Sampling Strategy (PSS) to generate the non-deterministic hand gestures by gradient-based Markov Chain Monte Carlo (MCMC) sampling. Extensive experiments demonstrate that our method outperforms the state-of-the-art models on the B2H dataset and our newly collected TED Hands dataset. The dataset and code are available at: https://github.com/XingqunQi-lab/Diverse-3D-Hand-Gesture-Prediction. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qi_Diverse_3D_Hand_Gesture_Prediction_From_Body_Dynamics_by_Bilateral_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qi_Diverse_3D_Hand_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01765 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qi_Diverse_3D_Hand_Gesture_Prediction_From_Body_Dynamics_by_Bilateral_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qi_Diverse_3D_Hand_Gesture_Prediction_From_Body_Dynamics_by_Bilateral_CVPR_2023_paper.html | CVPR 2023 | null |
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation | Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman | Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts. In this work, we present a new approach for "personalization" of text-to-image diffusion models. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can be used to synthesize novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views and lighting conditions that do not appear in the reference images. We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, and artistic rendering, all while preserving the subject's key features. We also provide a new dataset and evaluation protocol for this new task of subject-driven generation. Project page: https://dreambooth.github.io/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Ruiz_DreamBooth_Fine_Tuning_Text-to-Image_Diffusion_Models_for_Subject-Driven_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ruiz_DreamBooth_Fine_Tuning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.12242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ruiz_DreamBooth_Fine_Tuning_Text-to-Image_Diffusion_Models_for_Subject-Driven_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ruiz_DreamBooth_Fine_Tuning_Text-to-Image_Diffusion_Models_for_Subject-Driven_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
MOSO: Decomposing MOtion, Scene and Object for Video Prediction | Mingzhen Sun, Weining Wang, Xinxin Zhu, Jing Liu | Motion, scene and object are three primary visual components of a video. In particular, objects represent the foreground, scenes represent the background, and motion traces their dynamics. Based on this insight, we propose a two-stage MOtion, Scene and Object decomposition framework (MOSO) for video prediction, consisting of MOSO-VQVAE and MOSO-Transformer. In the first stage, MOSO-VQVAE decomposes a previous video clip into the motion, scene and object components, and represents them as distinct groups of discrete tokens. Then, in the second stage, MOSO-Transformer predicts the object and scene tokens of the subsequent video clip based on the previous tokens and adds dynamic motion at the token level to the generated object and scene tokens. Our framework can be easily extended to unconditional video generation and video frame interpolation tasks. Experimental results demonstrate that our method achieves new state-of-the-art performance on five challenging benchmarks for video prediction and unconditional video generation: BAIR, RoboNet, KTH, KITTI and UCF101. In addition, MOSO can produce realistic videos by combining objects and scenes from different videos. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_MOSO_Decomposing_MOtion_Scene_and_Object_for_Video_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_MOSO_Decomposing_MOtion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03684 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_MOSO_Decomposing_MOtion_Scene_and_Object_for_Video_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_MOSO_Decomposing_MOtion_Scene_and_Object_for_Video_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Shakes on a Plane: Unsupervised Depth Estimation From Unstabilized Photography | Ilya Chugunov, Yuxuan Zhang, Felix Heide | Modern mobile burst photography pipelines capture and merge a short sequence of frames to recover an enhanced image, but often disregard the 3D nature of the scene they capture, treating pixel motion between images as a 2D aggregation problem. We show that in a "long-burst", forty-two 12-megapixel RAW frames captured in a two-second sequence, there is enough parallax information from natural hand tremor alone to recover high-quality scene depth. To this end, we devise a test-time optimization approach that fits a neural RGB-D representation to long-burst data and simultaneously estimates scene depth and camera motion. Our plane plus depth model is trained end-to-end, and performs coarse-to-fine refinement by controlling which multi-resolution volume features the network has access to at what time during training. We validate the method experimentally, and demonstrate geometrically accurate depth reconstructions with no additional hardware or separate data pre-processing and pose-estimation steps. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chugunov_Shakes_on_a_Plane_Unsupervised_Depth_Estimation_From_Unstabilized_Photography_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chugunov_Shakes_on_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.12324 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chugunov_Shakes_on_a_Plane_Unsupervised_Depth_Estimation_From_Unstabilized_Photography_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chugunov_Shakes_on_a_Plane_Unsupervised_Depth_Estimation_From_Unstabilized_Photography_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Video Representations From Large Language Models | Yue Zhao, Ishan Misra, Philipp Krähenbühl, Rohit Girdhar | We introduce LAVILA, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create automatic video narrators. Our auto-generated narrations offer a number of advantages, including dense coverage of long videos, better temporal synchronization of the visual information and text, and much higher diversity of text. The video-language embedding learned contrastively with these narrations outperforms the previous state-of-the-art on multiple first-person and third-person video tasks, both in zero-shot and finetuned setups. Most notably, LAVILA obtains an absolute gain of 10.1% on EGTEA classification and 5.9% Epic-Kitchens-100 multi-instance retrieval benchmarks. Furthermore, LAVILA trained with only half the narrations from the Ego4D dataset outperforms models trained on the full set, and shows positive scaling behavior on increasing pre-training data and model size. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Learning_Video_Representations_From_Large_Language_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Learning_Video_Representations_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Learning_Video_Representations_From_Large_Language_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Learning_Video_Representations_From_Large_Language_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation | Liyan Chen, Weihan Wang, Philippos Mordohai | We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets. Our code is available at https://github.com/lly00412/SEDNet.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Learning_the_Distribution_of_Errors_in_Stereo_Matching_for_Joint_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Learning_the_Distribution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_the_Distribution_of_Errors_in_Stereo_Matching_for_Joint_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_the_Distribution_of_Errors_in_Stereo_Matching_for_Joint_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares | Dominik Muhle, Lukas Koestler, Krishna Murthy Jatavallabhula, Daniel Cremers | We propose a differentiable nonlinear least squares framework to account for uncertainty in relative pose estimation from feature correspondences. Specifically, we introduce a symmetric version of the probabilistic normal epipolar constraint, and an approach to estimate the covariance of feature positions by differentiating through the camera pose estimation procedure. We evaluate our approach on synthetic, as well as the KITTI and EuRoC real-world datasets. On the synthetic dataset, we confirm that our learned covariances accurately approximate the true noise distribution. In real world experiments, we find that our approach consistently outperforms state-of-the-art non-probabilistic and probabilistic approaches, regardless of the feature extraction algorithm of choice. | https://openaccess.thecvf.com/content/CVPR2023/papers/Muhle_Learning_Correspondence_Uncertainty_via_Differentiable_Nonlinear_Least_Squares_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Muhle_Learning_Correspondence_Uncertainty_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Muhle_Learning_Correspondence_Uncertainty_via_Differentiable_Nonlinear_Least_Squares_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Muhle_Learning_Correspondence_Uncertainty_via_Differentiable_Nonlinear_Least_Squares_CVPR_2023_paper.html | CVPR 2023 | null |
Samples With Low Loss Curvature Improve Data Efficiency | Isha Garg, Kaushik Roy | In this paper, we study the second order properties of the loss of trained deep neural networks with respect to the training data points to understand the curvature of the loss surface in the vicinity of these points. We find that there is an unexpected concentration of samples with very low curvature. We note that these low curvature samples are largely consistent across completely different architectures, and identifiable in the early epochs of training. We show that the curvature relates to the 'cleanliness' of the data points, with low curvatures samples corresponding to clean, higher clarity samples, representative of their category. Alternatively, high curvature samples are often occluded, have conflicting features and visually atypical of their category. Armed with this insight, we introduce SLo-Curves, a novel coreset identification and training algorithm. SLo-curves identifies the samples with low curvatures as being more data-efficient and trains on them with an additional regularizer that penalizes high curvature of the loss surface in their vicinity. We demonstrate the efficacy of SLo-Curves on CIFAR-10 and CIFAR-100 datasets, where it outperforms state of the art coreset selection methods at small coreset sizes by up to 9%. The identified coresets generalize across architectures, and hence can be pre-computed to generate condensed versions of datasets for use in downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Garg_Samples_With_Low_Loss_Curvature_Improve_Data_Efficiency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Garg_Samples_With_Low_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Garg_Samples_With_Low_Loss_Curvature_Improve_Data_Efficiency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Garg_Samples_With_Low_Loss_Curvature_Improve_Data_Efficiency_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Effective Visual Representations for Partial-Label Learning | Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng | Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance of PLL on vision tasks, attributed to representations learned by contrasting the same/different classes of entities. Without access to true labels, positive points are predicted using pseudolabels that are inherently noisy, and negative points often require large batches or momentum encoders, resulting in unreliable similarity information and a high computational overhead. In this paper, we rethink a state-of-the-art contrastive PLL method PiCO [24], inspiring the design of a simple framework termed PaPi (Partial-label learning with a guided Prototypical classifier), which demonstrates significant scope for improvement in representation learning, thus contributing to label disambiguation. PaPi guides the optimization of a prototypical classifier by a linear classifier with which they share the same feature encoder, thus explicitly encouraging the representation to reflect visual similarity between categories. It is also technically appealing, as PaPi requires only a few components in PiCO with the opposite direction of guidance, and directly eliminates the contrastive learning module that would introduce noise and consume computational resources. We empirically demonstrate that PaPi significantly outperforms other PLL methods on various image classification tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xia_Towards_Effective_Visual_Representations_for_Partial-Label_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xia_Towards_Effective_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.06080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_Towards_Effective_Visual_Representations_for_Partial-Label_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_Towards_Effective_Visual_Representations_for_Partial-Label_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining | Xiaoyi Dong, Jianmin Bao, Yinglin Zheng, Ting Zhang, Dongdong Chen, Hao Yang, Ming Zeng, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu | This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. The core idea of masked self-distillation is to distill representation from a full image to the representation predicted from a masked image. Such incorporation enjoys two vital benefits. First, masked self-distillation targets local patch representation learning, which is complementary to vision-language contrastive focusing on text-related representation. Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language. We provide specially designed experiments with a comprehensive analysis to validate the two benefits. Symmetrically, we also introduce the local semantic supervision into the text branch, which further improves the pretraining performance. With extensive experiments, we show that MaskCLIP, when applied to various challenging downstream tasks, achieves superior results in linear probing, finetuning, and zero-shot performance with the guidance of the language encoder. We will release the code and data after the publication. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_MaskCLIP_Masked_Self-Distillation_Advances_Contrastive_Language-Image_Pretraining_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_MaskCLIP_Masked_Self-Distillation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.12262 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_MaskCLIP_Masked_Self-Distillation_Advances_Contrastive_Language-Image_Pretraining_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_MaskCLIP_Masked_Self-Distillation_Advances_Contrastive_Language-Image_Pretraining_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Vocabulary Semantic Segmentation With Mask-Adapted CLIP | Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, Diana Marculescu | Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify masked regions. We identify the performance bottleneck of this paradigm to be the pre-trained CLIP model, since it does not perform well on masked images. To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions. We collect training data by mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to match masked image regions to nouns in the image captions. Compared with the more precise and manually annotated segmentation labels with fixed classes (e.g., COCO-Stuff), we find our noisy but diverse dataset can better retain CLIP's generalization ability. Along with finetuning the entire model, we utilize the "blank" areas in masked images using a method we dub mask prompt tuning. Experiments demonstrate mask prompt tuning brings significant improvement without modifying any weights of CLIP, and it can further improve a fully finetuned model. In particular, when trained on COCO and evaluated on ADE20K-150, our best model achieves 29.6% mIoU, which is +8.5% higher than the previous state-of-the-art. For the first time, open-vocabulary generalist models match the performance of supervised specialist models in 2017 without dataset-specific adaptations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_Open-Vocabulary_Semantic_Segmentation_With_Mask-Adapted_CLIP_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liang_Open-Vocabulary_Semantic_Segmentation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.04150 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Open-Vocabulary_Semantic_Segmentation_With_Mask-Adapted_CLIP_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Open-Vocabulary_Semantic_Segmentation_With_Mask-Adapted_CLIP_CVPR_2023_paper.html | CVPR 2023 | null |
A Loopback Network for Explainable Microvascular Invasion Classification | Shengxuming Zhang, Tianqi Shi, Yang Jiang, Xiuming Zhang, Jie Lei, Zunlei Feng, Mingli Song | Microvascular invasion (MVI) is a critical factor for prognosis evaluation and cancer treatment. The current diagnosis of MVI relies on pathologists to manually find out cancerous cells from hundreds of blood vessels, which is time-consuming, tedious, and subjective. Recently, deep learning has achieved promising results in medical image analysis tasks. However, the unexplainability of black box models and the requirement of massive annotated samples limit the clinical application of deep learning based diagnostic methods. In this paper, aiming to develop an accurate, objective, and explainable diagnosis tool for MVI, we propose a Loopback Network (LoopNet) for classifying MVI efficiently. With the image-level category annotations of the collected Pathologic Vessel Image Dataset (PVID), LoopNet is devised to be composed binary classification branch and cell locating branch. The latter is devised to locate the area of cancerous cells, regular non-cancerous cells, and background. For healthy samples, the pseudo masks of cells supervise the cell locating branch to distinguish the area of regular non-cancerous cells and background. For each MVI sample, the cell locating branch predicts the mask of cancerous cells. Then the masked cancerous and non-cancerous areas of the same sample are inputted back to the binary classification branch separately. The loopback between two branches enables the category label to supervise the cell locating branch to learn the locating ability for cancerous areas. Experiment results show that the proposed LoopNet achieves 97.5% accuracy on MVI classification. Surprisingly, the proposed loopback mechanism not only enables LoopNet to predict the cancerous area but also facilitates the classification backbone to achieve better classification performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_A_Loopback_Network_for_Explainable_Microvascular_Invasion_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_A_Loopback_Network_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_A_Loopback_Network_for_Explainable_Microvascular_Invasion_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_A_Loopback_Network_for_Explainable_Microvascular_Invasion_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
TINC: Tree-Structured Implicit Neural Compression | Runzhao Yang | Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://github.com/RichealYoung/TINC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_TINC_Tree-Structured_Implicit_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.06689 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.html | CVPR 2023 | null |
Unifying Short and Long-Term Tracking With Graph Hierarchies | Orcun Cetintas, Guillem Brasó, Laura Leal-Taixé | Tracking objects over long videos effectively means solving a spectrum of problems, from short-term association for un-occluded objects to long-term association for objects that are occluded and then reappear in the scene. Methods tackling these two tasks are often disjoint and crafted for specific scenarios, and top-performing approaches are often a mix of techniques, which yields engineering-heavy solutions that lack generality. In this work, we question the need for hybrid approaches and introduce SUSHI, a unified and scalable multi-object tracker. Our approach processes long clips by splitting them into a hierarchy of subclips, which enables high scalability. We leverage graph neural networks to process all levels of the hierarchy, which makes our model unified across temporal scales and highly general. As a result, we obtain significant improvements over state-of-the-art on four diverse datasets. Our code and models are available at bit.ly/sushi-mot. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cetintas_Unifying_Short_and_Long-Term_Tracking_With_Graph_Hierarchies_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cetintas_Unifying_Short_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cetintas_Unifying_Short_and_Long-Term_Tracking_With_Graph_Hierarchies_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cetintas_Unifying_Short_and_Long-Term_Tracking_With_Graph_Hierarchies_CVPR_2023_paper.html | CVPR 2023 | null |
Inferring and Leveraging Parts From Object Shape for Improving Semantic Image Synthesis | Yuxiang Wei, Zhilong Ji, Xiaohe Wu, Jinfeng Bai, Lei Zhang, Wangmeng Zuo | Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can undoubtedly benefit image synthesis, but is bothersome and inconvenient to be provided by users. To improve part synthesis, this paper presents to infer Parts from Object ShapE (iPOSE) and leverage it for improving semantic image synthesis. However, albeit several part segmentation datasets are available, part annotations are still not provided for many object categories in semantic image synthesis. To circumvent it, we resort to few-shot regime to learn a PartNet for predicting the object part map with the guidance of pre-defined support part maps. PartNet can be readily generalized to handle a new object category when a small number (e.g., 3) of support part maps for this category are provided. Furthermore, part semantic modulation is presented to incorporate both inferred part map and semantic map for image synthesis. Experiments show that our iPOSE not only generates objects with rich part details, but also enables to control the image synthesis flexibly. And our iPOSE performs favorably against the state-of-the-art methods in terms of quantitative and qualitative evaluation. Our code will be publicly available at https://github.com/csyxwei/iPOSE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Inferring_and_Leveraging_Parts_From_Object_Shape_for_Improving_Semantic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Inferring_and_Leveraging_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Inferring_and_Leveraging_Parts_From_Object_Shape_for_Improving_Semantic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Inferring_and_Leveraging_Parts_From_Object_Shape_for_Improving_Semantic_CVPR_2023_paper.html | CVPR 2023 | null |
MIME: Human-Aware 3D Scene Generation | Hongwei Yi, Chun-Hao P. Huang, Shashank Tripathi, Lea Hering, Justus Thies, Michael J. Black | Generating realistic 3D worlds occupied by moving humans has many applications in games, architecture, and synthetic data creation. But generating such scenes is expensive and labor intensive. Recent work generates human poses and motions given a 3D scene. Here, we take the opposite approach and generate 3D indoor scenes given 3D human motion. Such motions can come from archival motion capture or from IMU sensors worn on the body, effectively turning human movement in a "scanner" of the 3D world. Intuitively, human movement indicates the free-space in a room and human contact indicates surfaces or objects that support activities such as sitting, lying or touching. We propose MIME (Mining Interaction and Movement to infer 3D Environments), which is a generative model of indoor scenes that produces furniture layouts that are consistent with the human movement. MIME uses an auto-regressive transformer architecture that takes the already generated objects in the scene as well as the human motion as input, and outputs the next plausible object. To train MIME, we build a dataset by populating the 3D FRONT scene dataset with 3D humans. Our experiments show that MIME produces more diverse and plausible 3D scenes than a recent generative scene method that does not know about human movement. Code and data will be available for research. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yi_MIME_Human-Aware_3D_Scene_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yi_MIME_Human-Aware_3D_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.04360 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_MIME_Human-Aware_3D_Scene_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_MIME_Human-Aware_3D_Scene_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Re-Basin via Implicit Sinkhorn Differentiation | Fidel A. Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail, Masih Aminbeidokhti, Eric Granger, Marco Pedersoli | The recent emergence of new algorithms for permuting models into functionally equivalent regions of the solution space has shed some light on the complexity of error surfaces and some promising properties like mode connectivity. However, finding the permutation that minimizes some objectives is challenging, and current optimization techniques are not differentiable, which makes it difficult to integrate into a gradient-based optimization, and often leads to sub-optimal solutions. In this paper, we propose a Sinkhorn re-basin network with the ability to obtain the transportation plan that better suits a given objective. Unlike the current state-of-art, our method is differentiable and, therefore, easy to adapt to any task within the deep learning domain. Furthermore, we show the advantage of our re-basin method by proposing a new cost function that allows performing incremental learning by exploiting the linear mode connectivity property. The benefit of our method is compared against similar approaches from the literature under several conditions for both optimal transport and linear mode connectivity. The effectiveness of our continual learning method based on re-basin is also shown for several common benchmark datasets, providing experimental results that are competitive with the state-of-art. The source code is provided at https://github.com/fagp/sinkhorn-rebasin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pena_Re-Basin_via_Implicit_Sinkhorn_Differentiation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pena_Re-Basin_via_Implicit_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pena_Re-Basin_via_Implicit_Sinkhorn_Differentiation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pena_Re-Basin_via_Implicit_Sinkhorn_Differentiation_CVPR_2023_paper.html | CVPR 2023 | null |
NerVE: Neural Volumetric Edges for Parametric Curve Extraction From Point Cloud | Xiangyu Zhu, Dong Du, Weikai Chen, Zhiyou Zhao, Yinyu Nie, Xiaoguang Han | Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_NerVE_Neural_Volumetric_Edges_for_Parametric_Curve_Extraction_From_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_NerVE_Neural_Volumetric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16465 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_NerVE_Neural_Volumetric_Edges_for_Parametric_Curve_Extraction_From_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_NerVE_Neural_Volumetric_Edges_for_Parametric_Curve_Extraction_From_Point_CVPR_2023_paper.html | CVPR 2023 | null |
ShapeClipper: Scalable 3D Shape Learning From Single-View Images via Geometric and CLIP-Based Consistency | Zixuan Huang, Varun Jampani, Anh Thai, Yuanzhen Li, Stefan Stojanov, James M. Rehg | We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images. Instead of relying on laborious 3D, multi-view or camera pose annotation, ShapeClipper learns shape reconstruction from a set of single-view segmented images. The key idea is to facilitate shape learning via CLIP-based shape consistency, where we encourage objects with similar CLIP encodings to share similar shapes. We also leverage off-the-shelf normals as an additional geometric constraint so the model can learn better bottom-up reasoning of detailed surface geometry. These two novel consistency constraints, when used to regularize our model, improve its ability to learn both global shape structure and local geometric details. We evaluate our method over three challenging real-world datasets, Pix3D, Pascal3D+, and OpenImages, where we achieve superior performance over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_ShapeClipper_Scalable_3D_Shape_Learning_From_Single-View_Images_via_Geometric_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_ShapeClipper_Scalable_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_ShapeClipper_Scalable_3D_Shape_Learning_From_Single-View_Images_via_Geometric_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_ShapeClipper_Scalable_3D_Shape_Learning_From_Single-View_Images_via_Geometric_CVPR_2023_paper.html | CVPR 2023 | null |
Supervised Masked Knowledge Distillation for Few-Shot Transformers | Han Lin, Guangxing Han, Jiawei Ma, Shiyuan Huang, Xudong Lin, Shih-Fu Chang | Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features. However, under few-shot learning (FSL) settings on small datasets with only a few labeled data, ViT tends to overfit and suffers from severe performance degradation due to its absence of CNN-alike inductive bias. Previous works in FSL avoid such problem either through the help of self-supervised auxiliary losses, or through the dextile uses of label information under supervised settings. But the gap between self-supervised and supervised few-shot Transformers is still unfilled. Inspired by recent advances in self-supervised knowledge distillation and masked image modeling (MIM), we propose a novel Supervised Masked Knowledge Distillation model (SMKD) for few-shot Transformers which incorporates label information into self-distillation frameworks. Compared with previous self-supervised methods, we allow intra-class knowledge distillation on both class and patch tokens, and introduce the challenging task of masked patch tokens reconstruction across intra-class images. Experimental results on four few-shot classification benchmark datasets show that our method with simple design outperforms previous methods by a large margin and achieves a new start-of-the-art. Detailed ablation studies confirm the effectiveness of each component of our model. Code for this paper is available here: https://github.com/HL-hanlin/SMKD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Supervised_Masked_Knowledge_Distillation_for_Few-Shot_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Supervised_Masked_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15466 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Supervised_Masked_Knowledge_Distillation_for_Few-Shot_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Supervised_Masked_Knowledge_Distillation_for_Few-Shot_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors | Rui-Qi Wu, Zheng-Peng Duan, Chun-Le Guo, Zhi Chai, Chongyi Li | Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto physical scattering model, we rethink the degradation of real hazy images and propose a phenomenological pipeline considering diverse degradation types. (2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is pre-trained on a large-scale high-quality dataset to obtain the discrete codebook, encapsulating high-quality priors (HQPs). After replacing the negative effects brought by haze with HQPs, the decoder equipped with a novel normalized feature alignment module can effectively utilize high-quality features and produce clean results. However, although our degradation pipeline drastically mitigates the domain gap between synthetic and real data, it is still intractable to avoid it, which challenges HQPs matching in the wild. Thus, we re-calculate the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts. We provide a recommendation to control the matching based on an explainable solution. Users can also flexibly adjust the enhancement degree as per their preference. Extensive experiments verify the effectiveness of our data synthesis pipeline and the superior performance of RIDCP in real image dehazing. Code and data will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_RIDCP_Revitalizing_Real_Image_Dehazing_via_High-Quality_Codebook_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_RIDCP_Revitalizing_Real_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03994 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_RIDCP_Revitalizing_Real_Image_Dehazing_via_High-Quality_Codebook_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_RIDCP_Revitalizing_Real_Image_Dehazing_via_High-Quality_Codebook_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields | Brian K. S. Isaac-Medina, Chris G. Willcocks, Toby P. Breckon | Neural Radiance Fields (NeRF) have attracted significant attention due to their ability to synthesize novel scene views with great accuracy. However, inherent to their underlying formulation, the sampling of points along a ray with zero width may result in ambiguous representations that lead to further rendering artifacts such as aliasing in the final scene. To address this issue, the recent variant mip-NeRF proposes an Integrated Positional Encoding (IPE) based on a conical view frustum. Although this is expressed with an integral formulation, mip-NeRF instead approximates this integral as the expected value of a multivariate Gaussian distribution. This approximation is reliable for short frustums but degrades with highly elongated regions, which arises when dealing with distant scene objects under a larger depth of field. In this paper, we explore the use of an exact approach for calculating the IPE by using a pyramid-based integral formulation instead of an approximated conical-based one. We denote this formulation as Exact-NeRF and contribute the first approach to offer a precise analytical solution to the IPE within the NeRF domain. Our exploratory work illustrates that such an exact formulation (Exact-NeRF) matches the accuracy of mip-NeRF and furthermore provides a natural extension to more challenging scenarios without further modification, such as in the case of unbounded scenes. Our contribution aims to both address the hitherto unexplored issues of frustum approximation in earlier NeRF work and additionally provide insight into the potential future consideration of analytical solutions in future NeRF extensions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Isaac-Medina_Exact-NeRF_An_Exploration_of_a_Precise_Volumetric_Parameterization_for_Neural_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Isaac-Medina_Exact-NeRF_An_Exploration_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Isaac-Medina_Exact-NeRF_An_Exploration_of_a_Precise_Volumetric_Parameterization_for_Neural_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Isaac-Medina_Exact-NeRF_An_Exploration_of_a_Precise_Volumetric_Parameterization_for_Neural_CVPR_2023_paper.html | CVPR 2023 | null |
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger | Yi Yu, Yufei Wang, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex C. Kot | Recent deep-learning-based compression methods have achieved superior performance compared with traditional approaches. However, deep learning models have proven to be vulnerable to backdoor attacks, where some specific trigger patterns added to the input can lead to malicious behavior of the models. In this paper, we present a novel backdoor attack with multiple triggers against learned image compression models. Motivated by the widely used discrete cosine transform (DCT) in existing compression systems and standards, we propose a frequency-based trigger injection model that adds triggers in the DCT domain. In particular, we design several attack objectives for various attacking scenarios, including: 1) attacking compression quality in terms of bit-rate and reconstruction quality; 2) attacking task-driven measures, such as down-stream face recognition and semantic segmentation. Moreover, a novel simple dynamic loss is designed to balance the influence of different loss terms adaptively, which helps achieve more efficient training. Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Backdoor_Attacks_Against_Deep_Image_Compression_via_Adaptive_Frequency_Trigger_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tan_Backdoor_Attacks_Against_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.14677 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Backdoor_Attacks_Against_Deep_Image_Compression_via_Adaptive_Frequency_Trigger_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Backdoor_Attacks_Against_Deep_Image_Compression_via_Adaptive_Frequency_Trigger_CVPR_2023_paper.html | CVPR 2023 | null |
Recurrence Without Recurrence: Stable Video Landmark Detection With Deep Equilibrium Models | Paul Micaelli, Arash Vahdat, Hongxu Yin, Jan Kautz, Pavlo Molchanov | Cascaded computation, whereby predictions are recurrently refined over several stages, has been a persistent theme throughout the development of landmark detection models. In this work, we show that the recently proposed Deep Equilibrium Model (DEQ) can be naturally adapted to this form of computation. Our Landmark DEQ (LDEQ) achieves state-of-the-art performance on the challenging WFLW facial landmark dataset, reaching 3.92 NME with fewer parameters and a training memory cost of O(1) in the number of recurrent modules. Furthermore, we show that DEQs are particularly suited for landmark detection in videos. In this setting, it is typical to train on still images due to the lack of labelled videos. This can lead to a "flickering" effect at inference time on video, whereby a model can rapidly oscillate between different plausible solutions across consecutive frames. By rephrasing DEQs as a constrained optimization, we emulate recurrence at inference time, despite not having access to temporal data at training time. This Recurrence without Recurrence (RwR) paradigm helps in reducing landmark flicker, which we demonstrate by introducing a new metric, normalized mean flicker (NMF), and contributing a new facial landmark video dataset (WFLW-V) targeting landmark uncertainty. On the WFLW-V hard subset made up of 500 videos, our LDEQ with RwR improves the NME and NMF by 10 and 13% respectively, compared to the strongest previously published model using a hand-tuned conventional filter. | https://openaccess.thecvf.com/content/CVPR2023/papers/Micaelli_Recurrence_Without_Recurrence_Stable_Video_Landmark_Detection_With_Deep_Equilibrium_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Micaelli_Recurrence_Without_Recurrence_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Micaelli_Recurrence_Without_Recurrence_Stable_Video_Landmark_Detection_With_Deep_Equilibrium_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Micaelli_Recurrence_Without_Recurrence_Stable_Video_Landmark_Detection_With_Deep_Equilibrium_CVPR_2023_paper.html | CVPR 2023 | null |
Generalized Relation Modeling for Transformer Tracking | Shenyuan Gao, Chunluan Zhou, Jun Zhang | Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which allows earlier interaction between the template and search region, has achieved a remarkable performance gain. However, existing one-stream trackers always let the template interact with all parts inside the search region throughout all the encoder layers. This could potentially lead to target-background confusion when the extracted feature representations are not sufficiently discriminative. To alleviate this issue, we propose a generalized relation modeling method based on adaptive token division. The proposed method is a generalized formulation of attention-based relation modeling for Transformer tracking, which inherits the merits of both previous two-stream and one-stream pipelines whilst enabling more flexible relation modeling by selecting appropriate search tokens to interact with template tokens. An attention masking strategy and the Gumbel-Softmax technique are introduced to facilitate the parallel computation and end-to-end learning of the token division module. Extensive experiments show that our method is superior to the two-stream and one-stream pipelines and achieves state-of-the-art performance on six challenging benchmarks with a real-time running speed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Generalized_Relation_Modeling_for_Transformer_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_Generalized_Relation_Modeling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16580 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Generalized_Relation_Modeling_for_Transformer_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Generalized_Relation_Modeling_for_Transformer_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Non-Line-of-Sight Imaging With Signal Superresolution Network | Jianyu Wang, Xintong Liu, Leping Xiao, Zuoqiang Shi, Lingyun Qiu, Xing Fu | Non-line-of-sight (NLOS) imaging aims at reconstructing the location, shape, albedo, and surface normal of the hidden object around the corner with measured transient data. Due to its strong potential in various fields, it has drawn much attention in recent years. However, long exposure time is not always available for applications such as auto-driving, which hinders the practical use of NLOS imaging. Although scanning fewer points can reduce the total measurement time, it also brings the problem of imaging quality degradation. This paper proposes a general learning-based pipeline for increasing imaging quality with only a few scanning points. We tailor a neural network to learn the operator that recovers a high spatial resolution signal. Experiments on synthetic and measured data indicate that the proposed method provides faithful reconstructions of the hidden scene under both confocal and non-confocal settings. Compared with original measurements, the acquisition of our approach is 16 times faster while maintaining similar reconstruction quality. Besides, the proposed pipeline can be applied directly to existing optical systems and imaging algorithms as a plug-in-and-play module. We believe the proposed pipeline is powerful in increasing the frame rate in NLOS video imaging. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Non-Line-of-Sight_Imaging_With_Signal_Superresolution_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Non-Line-of-Sight_Imaging_With_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Non-Line-of-Sight_Imaging_With_Signal_Superresolution_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Non-Line-of-Sight_Imaging_With_Signal_Superresolution_Network_CVPR_2023_paper.html | CVPR 2023 | null |
WildLight: In-the-Wild Inverse Rendering With a Flashlight | Ziang Cheng, Junxuan Li, Hongdong Li | This paper proposes a practical photometric solution for the challenging problem of in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. The key idea is to exploit smartphone's built-in flashlight as a minimally controlled light source, and decompose image intensities into two photometric components -- a static appearance corresponds to ambient flux, plus a dynamic reflection induced by the moving flashlight. Our method does not require flash/non-flash images to be captured in pairs. Building on the success of neural light fields, we use an off-the-shelf method to capture the ambient reflections, while the flashlight component enables physically accurate photometric constraints to decouple reflectance and illumination. Compared to existing inverse rendering methods, our setup is applicable to non-darkroom environments yet sidesteps the inherent difficulties of explicit solving ambient reflections. We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques. Finally, our neural reconstruction can be easily exported to PBR textured triangle mesh ready for industrial renderers. Our source code and data are released to https://github.com/za-cheng/WildLight | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_WildLight_In-the-Wild_Inverse_Rendering_With_a_Flashlight_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cheng_WildLight_In-the-Wild_Inverse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14190 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_WildLight_In-the-Wild_Inverse_Rendering_With_a_Flashlight_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_WildLight_In-the-Wild_Inverse_Rendering_With_a_Flashlight_CVPR_2023_paper.html | CVPR 2023 | null |
A Probabilistic Attention Model With Occlusion-Aware Texture Regression for 3D Hand Reconstruction From a Single RGB Image | Zheheng Jiang, Hossein Rahmani, Sue Black, Bryan M. Williams | Recently, deep learning based approaches have shown promising results in 3D hand reconstruction from a single RGB image. These approaches can be roughly divided into model-based approaches, which are heavily dependent on the model's parameter space, and model-free approaches, which require large numbers of 3D ground truths to reduce depth ambiguity and struggle in weakly-supervised scenarios. To overcome these issues, we propose a novel probabilistic model to achieve the robustness of model-based approaches and reduced dependence on the model's parameter space of model-free approaches. The proposed probabilistic model incorporates a model-based network as a prior-net to estimate the prior probability distribution of joints and vertices. An Attention-based Mesh Vertices Uncertainty Regression (AMVUR) model is proposed to capture dependencies among vertices and the correlation between joints and mesh vertices to improve their feature representation. We further propose a learning based occlusion-aware Hand Texture Regression model to achieve high-fidelity texture reconstruction. We demonstrate the flexibility of the proposed probabilistic model to be trained in both supervised and weakly-supervised scenarios. The experimental results demonstrate our probabilistic model's state-of-the-art accuracy in 3D hand and texture reconstruction from a single image in both training schemes, including in the presence of severe occlusions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_A_Probabilistic_Attention_Model_With_Occlusion-Aware_Texture_Regression_for_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_A_Probabilistic_Attention_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14299 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_A_Probabilistic_Attention_Model_With_Occlusion-Aware_Texture_Regression_for_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_A_Probabilistic_Attention_Model_With_Occlusion-Aware_Texture_Regression_for_3D_CVPR_2023_paper.html | CVPR 2023 | null |
MixNeRF: Modeling a Ray With Mixture Density for Novel View Synthesis From Sparse Inputs | Seunghyeon Seo, Donghoon Han, Yeonjin Chang, Nojun Kwak | Neural Radiance Field (NeRF) has broken new ground in the novel view synthesis due to its simple concept and state-of-the-art quality. However, it suffers from severe performance degradation unless trained with a dense set of images with different camera poses, which hinders its practical applications. Although previous methods addressing this problem achieved promising results, they relied heavily on the additional training resources, which goes against the philosophy of sparse-input novel-view synthesis pursuing the training efficiency. In this work, we propose MixNeRF, an effective training strategy for novel view synthesis from sparse inputs by modeling a ray with a mixture density model. Our MixNeRF estimates the joint distribution of RGB colors along the ray samples by modeling it with mixture of distributions. We also propose a new task of ray depth estimation as a useful training objective, which is highly correlated with 3D scene geometry. Moreover, we remodel the colors with regenerated blending weights based on the estimated ray depth and further improves the robustness for colors and viewpoints. Our MixNeRF outperforms other state-of-the-art methods in various standard benchmarks with superior efficiency of training and inference. | https://openaccess.thecvf.com/content/CVPR2023/papers/Seo_MixNeRF_Modeling_a_Ray_With_Mixture_Density_for_Novel_View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Seo_MixNeRF_Modeling_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.08788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Seo_MixNeRF_Modeling_a_Ray_With_Mixture_Density_for_Novel_View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Seo_MixNeRF_Modeling_a_Ray_With_Mixture_Density_for_Novel_View_CVPR_2023_paper.html | CVPR 2023 | null |
A New Path: Scaling Vision-and-Language Navigation With Synthetic Instructions and Imitation Learning | Aishwarya Kamath, Peter Anderson, Su Wang, Jing Yu Koh, Alexander Ku, Austin Waters, Yinfei Yang, Jason Baldridge, Zarana Parekh | Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. Pre-training on large text and image-text datasets from the web has been extensively explored but the improvements are limited. We investigate large-scale augmentation with synthetic instructions. We take 500+ indoor environments captured in densely-sampled 360 degree panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory using Marky, a high-quality multilingual navigation instruction generator. We also synthesize image observations from novel viewpoints using an image-to-image GAN. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets, and contains a wider variety of environments and viewpoints. To efficiently leverage data at this scale, we train a simple transformer agent with imitation learning. On the challenging RxR dataset, our approach outperforms all existing RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to 66.8 in unseen test environments. Our work points to a new path to improving instruction-following agents, emphasizing large-scale training on near-human quality synthetic instructions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kamath_A_New_Path_Scaling_Vision-and-Language_Navigation_With_Synthetic_Instructions_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kamath_A_New_Path_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.03112 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kamath_A_New_Path_Scaling_Vision-and-Language_Navigation_With_Synthetic_Instructions_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kamath_A_New_Path_Scaling_Vision-and-Language_Navigation_With_Synthetic_Instructions_and_CVPR_2023_paper.html | CVPR 2023 | null |
Layout-Based Causal Inference for Object Navigation | Sixian Zhang, Xinhang Song, Weijie Li, Yubing Bai, Xinyao Yu, Shuqiang Jiang | Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor. However, when the layout gap is significant, the experience exerts a negative effect on navigation. Motivated by keeping the positive effect and removing the negative effect of the experience, we propose the layout-based soft Total Direct Effect (L-sTDE) framework based on the causal inference to adjust the prediction of the navigation policy. In particular, we propose to calculate the layout gap which is defined as the KL divergence between the posterior and the prior distribution of the object layout. Then the sTDE is proposed to appropriately control the effect of the experience based on the layout gap. Experimental results on AI2THOR, RoboTHOR, and Habitat demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Layout-Based_Causal_Inference_for_Object_Navigation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Layout-Based_Causal_Inference_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Layout-Based_Causal_Inference_for_Object_Navigation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Layout-Based_Causal_Inference_for_Object_Navigation_CVPR_2023_paper.html | CVPR 2023 | null |
Pose-Disentangled Contrastive Learning for Self-Supervised Facial Representation | Yuanyuan Liu, Wenbin Wang, Yibing Zhan, Shaoze Feng, Kejun Liu, Zhe Chen | Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily. However, analytically, current contrastive-based self-supervised learning (SSL) still performs unsatisfactorily for learning facial representation. More specifically, existing contrastive learning (CL) tends to learn pose-invariant features that cannot depict the pose details of faces, compromising the learning performance. To conquer the above limitation of CL, we propose a novel Pose-disentangled Contrastive Learning (PCL) method for general self-supervised facial representation. Our PCL first devises a pose-disentangled decoder (PDD) with a delicately designed orthogonalizing regulation, which disentangles the pose-related features from the face-aware features; therefore, pose-related and other pose-unrelated facial information could be performed in individual subnetworks and do not affect each other's training. Furthermore, we introduce a pose-related contrastive learning scheme that learns pose-related information based on data augmentation of the same image, which would deliver more effective face-aware representation for various downstream tasks. We conducted linear evaluation on four challenging downstream facial understanding tasks, i.e., facial expression recognition, face recognition, AU detection and head pose estimation.Experimental results demonstrate that PCL significantly outperforms cutting-edge SSL methods. Our Code is available at https://github.com/DreamMr/PCL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Pose-Disentangled_Contrastive_Learning_for_Self-Supervised_Facial_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Pose-Disentangled_Contrastive_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Pose-Disentangled_Contrastive_Learning_for_Self-Supervised_Facial_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Pose-Disentangled_Contrastive_Learning_for_Self-Supervised_Facial_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
Cross-Domain 3D Hand Pose Estimation With Dual Modalities | Qiuxia Lin, Linlin Yang, Angela Yao | Recent advances in hand pose estimation have shed light on utilizing synthetic data to train neural networks, which however inevitably hinders generalization to real-world data due to domain gaps. To solve this problem, we present a framework for cross-domain semi-supervised hand pose estimation and target the challenging scenario of learning models from labelled multi-modal synthetic data and unlabelled real-world data. To that end, we propose a dual-modality network that exploits synthetic RGB and synthetic depth images. For pre-training, our network uses multi-modal contrastive learning and attention-fused supervision to learn effective representations of the RGB images. We then integrate a novel self-distillation technique during fine-tuning to reduce pseudo-label noise. Experiments show that the proposed method significantly improves 3D hand pose estimation and 2D keypoint detection on benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Cross-Domain_3D_Hand_Pose_Estimation_With_Dual_Modalities_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Cross-Domain_3D_Hand_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Cross-Domain_3D_Hand_Pose_Estimation_With_Dual_Modalities_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Cross-Domain_3D_Hand_Pose_Estimation_With_Dual_Modalities_CVPR_2023_paper.html | CVPR 2023 | null |
Attribute-Preserving Face Dataset Anonymization via Latent Code Optimization | Simone Barattin, Christos Tzelepis, Ioannis Patras, Nicu Sebe | This work addresses the problem of anonymizing the identity of faces in a dataset of images, such that the privacy of those depicted is not violated, while at the same time the dataset is useful for downstream task such as for training machine learning models. To the best of our knowledge, we are the first to explicitly address this issue and deal with two major drawbacks of the existing state-of-the-art approaches, namely that they (i) require the costly training of additional, purpose-trained neural networks, and/or (ii) fail to retain the facial attributes of the original images in the anonymized counterparts, the preservation of which is of paramount importance for their use in downstream tasks. We accordingly present a task-agnostic anonymization procedure that directly optimises the images' latent representation in the latent space of a pre-trained GAN. By optimizing the latent codes directly, we ensure both that the identity is of a desired distance away from the original (with an identity obfuscation loss), whilst preserving the facial attributes (using a novel feature-matching loss in FaRL's deep feature space). We demonstrate through a series of both qualitative and quantitative experiments that our method is capable of anonymizing the identity of the images whilst--crucially--better-preserving the facial attributes. We make the code and the pre-trained models publicly available at: https://github.com/chi0tzp/FALCO. | https://openaccess.thecvf.com/content/CVPR2023/papers/Barattin_Attribute-Preserving_Face_Dataset_Anonymization_via_Latent_Code_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Barattin_Attribute-Preserving_Face_Dataset_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11296 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Barattin_Attribute-Preserving_Face_Dataset_Anonymization_via_Latent_Code_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Barattin_Attribute-Preserving_Face_Dataset_Anonymization_via_Latent_Code_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Inverse Rendering of Translucent Objects Using Physical and Neural Renderers | Chenhao Li, Trung Thanh Ngo, Hajime Nagahara | In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we use a physically-based renderer and a neural renderer for scene reconstruction and material editing. Because two renderers are differentiable, we can compute a reconstruction loss to assist parameter estimation. To enhance the supervision of the proposed neural renderer, we also propose an augmented loss. In addition, we use a flash and no-flash image pair as the input. To supervise the training, we constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes. Qualitative and quantitative results on both synthetic and real-world datasets demonstrated the effectiveness of the proposed model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Inverse_Rendering_of_Translucent_Objects_Using_Physical_and_Neural_Renderers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Inverse_Rendering_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.08336 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Inverse_Rendering_of_Translucent_Objects_Using_Physical_and_Neural_Renderers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Inverse_Rendering_of_Translucent_Objects_Using_Physical_and_Neural_Renderers_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration | Kemal Oksuz, Tom Joy, Puneet K. Dokania | The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality. In this work, we address these issues, and introduce the Self Aware Object Detection (SAOD) task, a unified testing framework which respects and adheres to the challenges that object detectors face in safety-critical environments such as autonomous driving. Specifically, the SAOD task requires an object detector to be: robust to domain shift; obtain reliable uncertainty estimates for the entire scene; and provide calibrated confidence scores for the detections. We extensively use our framework, which introduces novel metrics and large scale test datasets, to test numerous object detectors in two different use-cases, allowing us to highlight critical insights into their robustness performance. Finally, we introduce a simple baseline for the SAOD task, enabling researchers to benchmark future proposed methods and move towards robust object detectors which are fit for purpose. Code is available at: https://github.com/fiveai/saod | https://openaccess.thecvf.com/content/CVPR2023/papers/Oksuz_Towards_Building_Self-Aware_Object_Detectors_via_Reliable_Uncertainty_Quantification_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Oksuz_Towards_Building_Self-Aware_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Oksuz_Towards_Building_Self-Aware_Object_Detectors_via_Reliable_Uncertainty_Quantification_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Oksuz_Towards_Building_Self-Aware_Object_Detectors_via_Reliable_Uncertainty_Quantification_and_CVPR_2023_paper.html | CVPR 2023 | null |
Ensemble-Based Blackbox Attacks on Dense Prediction | Zikui Cai, Yaoteng Tan, M. Salman Asif | We propose an approach for adversarial attacks on dense prediction models (such as object detectors and segmentation). It is well known that the attacks generated by a single surrogate model do not transfer to arbitrary (blackbox) victim models. Furthermore, targeted attacks are often more challenging than the untargeted attacks. In this paper, we show that a carefully designed ensemble can create effective attacks for a number of victim models. In particular, we show that normalization of the weights for individual models plays a critical role in the success of the attacks. We then demonstrate that by adjusting the weights of the ensemble according to the victim model can further improve the performance of the attacks. We performed a number of experiments for object detectors and segmentation to highlight the significance of the our proposed methods. Our proposed ensemble-based method outperforms existing blackbox attack methods for object detection and segmentation. Finally we show that our proposed method can also generate a single perturbation that can fool multiple blackbox detection and segmentation models simultaneously. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_Ensemble-Based_Blackbox_Attacks_on_Dense_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_Ensemble-Based_Blackbox_Attacks_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14304 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Ensemble-Based_Blackbox_Attacks_on_Dense_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Ensemble-Based_Blackbox_Attacks_on_Dense_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Fairness in Facial Albedo Estimation via Visual-Textual Cues | Xingyu Ren, Jiankang Deng, Chao Ma, Yichao Yan, Xiaokang Yang | Recent 3D face reconstruction methods have made significant advances in geometry prediction, yet further cosmetic improvements are limited by lagged albedo because inferring albedo from appearance is an ill-posed problem. Although some existing methods consider prior knowledge from illumination to improve albedo estimation, they still produce a light-skin bias due to racially biased albedo models and limited light constraints. In this paper, we reconsider the relationship between albedo and face attributes and propose an ID2Albedo to directly estimate albedo without constraining illumination. Our key insight is that intrinsic semantic attributes such as race, skin color, and age can constrain the albedo map. We first introduce visual-textual cues and design a semantic loss to supervise facial albedo estimation. Specifically, we pre-define text labels such as race, skin color, age, and wrinkles. Then, we employ the text-image model (CLIP) to compute the similarity between the text and the input image, and assign a pseudo-label to each facial image. We constrain generated albedos in the training phase to have the same attributes as the inputs. In addition, we train a high-quality, unbiased facial albedo generator and utilize the semantic loss to learn the mapping from illumination-robust identity features to the albedo latent codes. Finally, our ID2Albedo is trained in a self-supervised way and outperforms state-of-the-art albedo estimation methods in terms of accuracy and fidelity. It is worth mentioning that our approach has excellent generalizability and fairness, especially on in-the-wild data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_Improving_Fairness_in_Facial_Albedo_Estimation_via_Visual-Textual_Cues_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Improving_Fairness_in_Facial_Albedo_Estimation_via_Visual-Textual_Cues_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Improving_Fairness_in_Facial_Albedo_Estimation_via_Visual-Textual_Cues_CVPR_2023_paper.html | CVPR 2023 | null |
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning | Kai Li, Deep Patel, Erik Kruus, Martin Renqiang Min | Source-free domain adaptation (SFDA) is an emerging research topic that studies how to adapt a pretrained source model using unlabeled target data. It is derived from unsupervised domain adaptation but has the advantage of not requiring labeled source data to learn adaptive models. This makes it particularly useful in real-world applications where access to source data is restricted. While there has been some SFDA work for images, little attention has been paid to videos. Naively extending image-based methods to videos without considering the unique properties of videos often leads to unsatisfactory results. In this paper, we propose a simple and highly flexible method for Source-Free Video Domain Adaptation (SFVDA), which extensively exploits consistency learning for videos from spatial, temporal, and historical perspectives. Our method is based on the assumption that videos of the same action category are drawn from the same low-dimensional space, regardless of the spatio-temporal variations in the high-dimensional space that cause domain shifts. To overcome domain shifts, we simulate spatio-temporal variations by applying spatial and temporal augmentations on target videos, and encourage the model to make consistent predictions from a video and its augmented versions. Due to the simple design, our method can be applied to various SFVDA settings, and experiments show that our method achieves state-of-the-art performance for all the settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Source-Free_Video_Domain_Adaptation_With_Spatial-Temporal-Historical_Consistency_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Source-Free_Video_Domain_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Source-Free_Video_Domain_Adaptation_With_Spatial-Temporal-Historical_Consistency_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Source-Free_Video_Domain_Adaptation_With_Spatial-Temporal-Historical_Consistency_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
SmartAssign: Learning a Smart Knowledge Assignment Strategy for Deraining and Desnowing | Yinglong Wang, Chao Ma, Jianzhuang Liu | Existing methods mainly handle single weather types. However, the connections of different weather conditions at deep representation level are usually ignored. These connections, if used properly, can generate complementary representations for each other to make up insufficient training data, obtaining positive performance gains and better generalization. In this paper, we focus on the very correlated rain and snow to explore their connections at deep representation level. Because sub-optimal connections may cause negative effect, another issue is that if rain and snow are handled in a multi-task learning way, how to find an optimal connection strategy to simultaneously improve deraining and desnowing performance. To build desired connection, we propose a smart knowledge assignment strategy, called SmartAssign, to optimally assign the knowledge learned from both tasks to a specific one. In order to further enhance the accuracy of knowledge assignment, we propose a novel knowledge contrast mechanism, so that the knowledge assigned to different tasks preserves better uniqueness. The inherited inductive biases usually limit the modelling ability of CNNs, we introduce a novel transformer block to constitute the backbone of our network to effectively combine long-range context dependency and local image details. Extensive experiments on seven benchmark datasets verify that proposed SmartAssign explores effective connection between rain and snow, and improves the performances of both deraining and desnowing apparently. The implementation code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/SmartAssign. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_SmartAssign_Learning_a_Smart_Knowledge_Assignment_Strategy_for_Deraining_and_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_SmartAssign_Learning_a_Smart_Knowledge_Assignment_Strategy_for_Deraining_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_SmartAssign_Learning_a_Smart_Knowledge_Assignment_Strategy_for_Deraining_and_CVPR_2023_paper.html | CVPR 2023 | null |
Delving Into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling | Yulin Liu, Haoran Liu, Yingda Yin, Yang Wang, Baoquan Chen, He Wang | Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data.Rotation, as an important quantity in computer vision, graphics, and robotics, can exhibit many ambiguities when occlusion and symmetry occur and thus demands such probabilistic models. Though much progress has been made for NFs in Euclidean space, there are no effective normalizing flows without discontinuity or many-to-one mapping tailored for SO(3) manifold. Given the unique non-Euclidean properties of the rotation manifold, adapting the existing NFs to SO(3) manifold is non-trivial. In this paper, we propose a novel normalizing flow on SO(3) by combining a Mobius transformation-based coupling layer and a quaternion affine transformation. With our proposed rotation normalizing flows, one can not only effectively express arbitrary distributions on SO(3), but also conditionally build the target distribution given input observations. Extensive experiments show that our rotation normalizing flows significantly outperform the baselines on both unconditional and conditional tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Delving_Into_Discrete_Normalizing_Flows_on_SO3_Manifold_for_Probabilistic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Delving_Into_Discrete_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Delving_Into_Discrete_Normalizing_Flows_on_SO3_Manifold_for_Probabilistic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Delving_Into_Discrete_Normalizing_Flows_on_SO3_Manifold_for_Probabilistic_CVPR_2023_paper.html | CVPR 2023 | null |
SfM-TTR: Using Structure From Motion for Test-Time Refinement of Single-View Depth Networks | Sergio Izquierdo, Javier Civera | Estimating a dense depth map from a single view is geometrically ill-posed, and state-of-the-art methods rely on learning depth's relation with visual appearance using deep neural networks. On the other hand, Structure from Motion (SfM) leverages multi-view constraints to produce very accurate but sparse maps, as matching across images is typically limited by locally discriminative texture. In this work, we combine the strengths of both approaches by proposing a novel test-time refinement (TTR) method, denoted as SfM-TTR, that boosts the performance of single-view depth networks at test time using SfM multi-view cues. Specifically, and differently from the state of the art, we use sparse SfM point clouds as test-time self-supervisory signal, fine-tuning the network encoder to learn a better representation of the test scene. Our results show how the addition of SfM-TTR to several state-of-the-art self-supervised and supervised networks improves significantly their performance, outperforming previous TTR baselines mainly based on photometric multi-view consistency. The code is available at https://github.com/serizba/SfM-TTR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Izquierdo_SfM-TTR_Using_Structure_From_Motion_for_Test-Time_Refinement_of_Single-View_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Izquierdo_SfM-TTR_Using_Structure_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Izquierdo_SfM-TTR_Using_Structure_From_Motion_for_Test-Time_Refinement_of_Single-View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Izquierdo_SfM-TTR_Using_Structure_From_Motion_for_Test-Time_Refinement_of_Single-View_CVPR_2023_paper.html | CVPR 2023 | null |
Fusing Pre-Trained Language Models With Multimodal Prompts Through Reinforcement Learning | Youngjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Rowan Zellers, Prithviraj Ammanabrolu, Ronan Le Bras, Gunhee Kim, Yejin Choi | Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 manifest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning; these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://github.com/JiwanChung/esper. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Fusing_Pre-Trained_Language_Models_With_Multimodal_Prompts_Through_Reinforcement_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Fusing_Pre-Trained_Language_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Fusing_Pre-Trained_Language_Models_With_Multimodal_Prompts_Through_Reinforcement_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Fusing_Pre-Trained_Language_Models_With_Multimodal_Prompts_Through_Reinforcement_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
MELTR: Meta Loss Transformer for Learning To Fine-Tune Video Foundation Models | Dohwan Ko, Joonmyung Choi, Hyeong Kyu Choi, Kyoung-Woon On, Byungseok Roh, Hyunwoo J. Kim | Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase, a naive strategy to minimize a single task-specific loss is adopted for fine-tuning. However, such fine-tuning methods do not fully leverage other losses that are potentially beneficial for the target task. Therefore, we propose MEta Loss TRansformer (MELTR), a plug-in module that automatically and non-linearly combines various loss functions to aid learning the target task via auxiliary learning. We formulate the auxiliary learning as a bi-level optimization problem and present an efficient optimization algorithm based on Approximate Implicit Differentiation (AID). For evaluation, we apply our framework to various video foundation models (UniVL, Violet and All-in-one), and show significant performance gain on all four downstream tasks: text-to-video retrieval, video question answering, video captioning, and multi-modal sentiment analysis. Our qualitative analyses demonstrate that MELTR adequately 'transforms' individual loss functions and 'melts' them into an effective unified loss. Code is available at https://github.com/mlvlab/MELTR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ko_MELTR_Meta_Loss_Transformer_for_Learning_To_Fine-Tune_Video_Foundation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ko_MELTR_Meta_Loss_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13009 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ko_MELTR_Meta_Loss_Transformer_for_Learning_To_Fine-Tune_Video_Foundation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ko_MELTR_Meta_Loss_Transformer_for_Learning_To_Fine-Tune_Video_Foundation_CVPR_2023_paper.html | CVPR 2023 | null |
Dense Network Expansion for Class Incremental Learning | Zhiyuan Hu, Yunsheng Li, Jiancheng Lyu, Dashan Gao, Nuno Vasconcelos | The problem of class incremental learning (CIL) is considered. State-of-the-art approaches use a dynamic architecture based on network expansion (NE), in which a task expert is added per task. While effective from a computational standpoint, these methods lead to models that grow quickly with the number of tasks. A new NE method, dense network expansion (DNE), is proposed to achieve a better trade-off between accuracy and model complexity. This is accomplished by the introduction of dense connections between the intermediate layers of the task expert networks, that enable the transfer of knowledge from old to new tasks via feature sharing and reusing. This sharing is implemented with a cross-task attention mechanism, based on a new task attention block (TAB), that fuses information across tasks. Unlike traditional attention mechanisms, TAB operates at the level of the feature mixing and is decoupled with spatial attentions. This is shown more effective than a joint spatial-and-task attention for CIL. The proposed DNE approach can strictly maintain the feature space of old classes while growing the network and feature scale at a much slower rate than previous methods. In result, it outperforms the previous SOTA methods by a margin of 4% in terms of accuracy, with similar or even smaller model scale. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Dense_Network_Expansion_for_Class_Incremental_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Dense_Network_Expansion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Dense_Network_Expansion_for_Class_Incremental_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Dense_Network_Expansion_for_Class_Incremental_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Meta-Personalizing Vision-Language Models To Find Named Instances in Video | Chun-Hsiao Yeh, Bryan Russell, Josef Sivic, Fabian Caba Heilbron, Simon Jenni | Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as "My dog Biscuit" appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and DeepFashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yeh_Meta-Personalizing_Vision-Language_Models_To_Find_Named_Instances_in_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yeh_Meta-Personalizing_Vision-Language_Models_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yeh_Meta-Personalizing_Vision-Language_Models_To_Find_Named_Instances_in_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yeh_Meta-Personalizing_Vision-Language_Models_To_Find_Named_Instances_in_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Regularize Implicit Neural Representation by Itself | Zhemin Li, Hongxia Wang, Deyu Meng | This paper proposes a regularizer called Implicit Neural Representation Regularizer (INRR) to improve the generalization ability of the Implicit Neural Representation (INR). The INR is a fully connected network that can represent signals with details not restricted by grid resolution. However, its generalization ability could be improved, especially with non-uniformly sampled data. The proposed INRR is based on learned Dirichlet Energy (DE) that measures similarities between rows/columns of the matrix. The smoothness of the Laplacian matrix is further integrated by parameterizing DE with a tiny INR. INRR improves the generalization of INR in signal representation by perfectly integrating the signal's self-similarity with the smoothness of the Laplacian matrix. Through well-designed numerical experiments, the paper also reveals a series of properties derived from INRR, including momentum methods like convergence trajectory and multi-scale similarity. Moreover, the proposed method could improve the performance of other signal representation methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Regularize_Implicit_Neural_Representation_by_Itself_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Regularize_Implicit_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15484 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Regularize_Implicit_Neural_Representation_by_Itself_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Regularize_Implicit_Neural_Representation_by_Itself_CVPR_2023_paper.html | CVPR 2023 | null |
Egocentric Audio-Visual Object Localization | Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu | Humans naturally perceive surrounding scenes by unifying sound and sight in a first-person view. Likewise, machines are advanced to approach human intelligence by learning with multisensory inputs from an egocentric perspective. In this paper, we explore the challenging egocentric audio-visual object localization task and observe that 1) egomotion commonly exists in first-person recordings, even within a short duration; 2) The out-of-view sound components can be created while wearers shift their attention. To address the first problem, we propose a geometry-aware temporal aggregation module to handle the egomotion explicitly. The effect of egomotion is mitigated by estimating the temporal geometry transformation and exploiting it to update visual representations. Moreover, we propose a cascaded feature enhancement module to tackle the second issue. It improves cross-modal localization robustness by disentangling visually-indicated audio representation. During training, we take advantage of the naturally available audio-visual temporal synchronization as the "free" self-supervision to avoid costly labeling. We also annotate and create the Epic Sounding Object dataset for evaluation purposes. Extensive experiments show that our method achieves state-of-the-art localization performance in egocentric videos and can be generalized to diverse audio-visual scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Egocentric_Audio-Visual_Object_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Egocentric_Audio-Visual_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13471 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Egocentric_Audio-Visual_Object_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Egocentric_Audio-Visual_Object_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
DropKey for Vision Transformer | Bonan Li, Yinhan Hu, Xuecheng Nie, Congying Han, Xiangjian Jiang, Tiande Guo, Luoqi Liu | In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information; Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training; Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T, VOLO, CeiT and DeiT, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_DropKey_for_Vision_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_DropKey_for_Vision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DropKey_for_Vision_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DropKey_for_Vision_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
sRGB Real Noise Synthesizing With Neighboring Correlation-Aware Noise Model | Zixuan Fu, Lanqing Guo, Bihan Wen | Modeling and synthesizing real noise in the standard RGB (sRGB) domain is challenging due to the complicated noise distribution. While most of the deep noise generators proposed to synthesize sRGB real noise using an end-to-end trained model, the lack of explicit noise modeling degrades the quality of their synthesized noise. In this work, we propose to model the real noise as not only dependent on the underlying clean image pixel intensity, but also highly correlated to its neighboring noise realization within the local region. Correspondingly, we propose a novel noise synthesizing framework by explicitly learning its neighboring correlation on top of the signal dependency. With the proposed noise model, our framework greatly bridges the distribution gap between synthetic noise and real noise. We show that our generated "real" sRGB noisy images can be used for training supervised deep denoisers, thus to improve their real denoising results with a large margin, comparing to the popular classic denoisers or the deep denoisers that are trained on other sRGB noise generators. The code will be available at https://github.com/xuan611/sRGB-Real-Noise-Synthesizing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_sRGB_Real_Noise_Synthesizing_With_Neighboring_Correlation-Aware_Noise_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fu_sRGB_Real_Noise_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_sRGB_Real_Noise_Synthesizing_With_Neighboring_Correlation-Aware_Noise_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_sRGB_Real_Noise_Synthesizing_With_Neighboring_Correlation-Aware_Noise_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Meta Architecture for Point Cloud Analysis | Haojia Lin, Xiawu Zheng, Lijiang Li, Fei Chao, Shanshan Wang, Yan Wang, Yonghong Tian, Rongrong Ji | Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a unified framework called PointMeta, to which the popular 3D point cloud analysis approaches could fit. This brings three benefits. First, it allows us to compare different approaches in a fair manner, and use quick experiments to verify any empirical observations or assumptions summarized from the comparison. Second, the big picture brought by PointMeta enables us to think across different components, and revisit common beliefs and key design decisions made by the popular approaches. Third, based on the learnings from the previous two analyses, by doing simple tweaks on the existing approaches, we are able to derive a basic building block, termed PointMetaBase. It shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and thus verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta. In particular, PointMetaBase surpasses the previous state-of-the-art method by 0.7%/1.4/%2.1% mIoU with only 2%/11%/13% of the computation cost on the S3DIS datasets. Codes are available in the supplementary materials. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Meta_Architecture_for_Point_Cloud_Analysis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Meta_Architecture_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14462 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Meta_Architecture_for_Point_Cloud_Analysis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Meta_Architecture_for_Point_Cloud_Analysis_CVPR_2023_paper.html | CVPR 2023 | null |
Ambiguous Medical Image Segmentation Using Diffusion Models | Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu, Vishal M. Patel | Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code will be released publicly after the review process. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rahman_Ambiguous_Medical_Image_Segmentation_Using_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rahman_Ambiguous_Medical_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04745 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Ambiguous_Medical_Image_Segmentation_Using_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Ambiguous_Medical_Image_Segmentation_Using_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
CIRCLE: Capture in Rich Contextual Environments | João Pedro Araújo, Jiaman Li, Karthik Vetrivel, Rishi Agarwal, Jiajun Wu, Deepak Gopinath, Alexander William Clegg, Karen Liu | Synthesizing 3D human motion in a contextual, ecological environment is important for simulating realistic activities people perform in the real world. However, conventional optics-based motion capture systems are not suited for simultaneously capturing human movements and complex scenes. The lack of rich contextual 3D human motion datasets presents a roadblock to creating high-quality generative human motion models. We propose a novel motion acquisition system in which the actor perceives and operates in a highly contextual virtual world while being motion captured in the real world. Our system enables rapid collection of high-quality human motion in highly diverse scenes, without the concern of occlusion or the need for physical scene construction in the real world. We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information. Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes. To download the data please visit our website (https://stanford-tml.github.io/circle_dataset/). | https://openaccess.thecvf.com/content/CVPR2023/papers/Araujo_CIRCLE_Capture_in_Rich_Contextual_Environments_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Araujo_CIRCLE_Capture_in_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Araujo_CIRCLE_Capture_in_Rich_Contextual_Environments_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Araujo_CIRCLE_Capture_in_Rich_Contextual_Environments_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation | Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi | In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version. Intriguingly, we observe that such a simple pipeline already achieves competitive results against recent advanced works, when transferred to our segmentation scenario. Its success heavily relies on the manual design of strong data augmentations, however, which may be limited and inadequate to explore a broader perturbation space. Motivated by this, we propose an auxiliary feature perturbation stream as a supplement, leading to an expanded perturbation space. On the other, to sufficiently probe original image-level augmentations, we present a dual-stream perturbation technique, enabling two strong views to be simultaneously guided by a common weak view. Consequently, our overall Unified Dual-Stream Perturbations approach (UniMatch) surpasses all existing methods significantly across all evaluation protocols on the Pascal, Cityscapes, and COCO benchmarks. Its superiority is also demonstrated in remote sensing interpretation and medical image analysis. We hope our reproduced FixMatch and our results can inspire more future works. Code and logs are available at https://github.com/LiheYoung/UniMatch. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Revisiting_Weak-to-Strong_Consistency_in_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Revisiting_Weak-to-Strong_Consistency_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.09910 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Revisiting_Weak-to-Strong_Consistency_in_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Revisiting_Weak-to-Strong_Consistency_in_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Implicit View-Time Interpolation of Stereo Videos Using Multi-Plane Disparities and Non-Uniform Coordinates | Avinash Paliwal, Andrii Tsarov, Nima Khademi Kalantari | In this paper, we propose an approach for view-time interpolation of stereo videos. Specifically, we build upon X-Fields that approximates an interpolatable mapping between the input coordinates and 2D RGB images using a convolutional decoder. Our main contribution is to analyze and identify the sources of the problems with using X-Fields in our application and propose novel techniques to overcome these challenges. Specifically, we observe that X-Fields struggles to implicitly interpolate the disparities for large baseline cameras. Therefore, we propose multi-plane disparities to reduce the spatial distance of the objects in the stereo views. Moreover, we propose non-uniform time coordinates to handle the non-linear and sudden motion spikes in videos. We additionally introduce several simple, but important, improvements over X-Fields. We demonstrate that our approach is able to produce better results than the state of the art, while running in near real-time rates and having low memory and storage costs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Paliwal_Implicit_View-Time_Interpolation_of_Stereo_Videos_Using_Multi-Plane_Disparities_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Paliwal_Implicit_View-Time_Interpolation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Paliwal_Implicit_View-Time_Interpolation_of_Stereo_Videos_Using_Multi-Plane_Disparities_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Paliwal_Implicit_View-Time_Interpolation_of_Stereo_Videos_Using_Multi-Plane_Disparities_and_CVPR_2023_paper.html | CVPR 2023 | null |
PyPose: A Library for Robot Learning With Physics-Based Optimization | Chen Wang, Dasong Gao, Kuan Xu, Junyi Geng, Yaoyu Hu, Yuheng Qiu, Bowen Li, Fan Yang, Brady Moon, Abhinav Pandey, Aryan, Jiahe Xu, Tianhao Wu, Haonan He, Daning Huang, Zhongqiang Ren, Shibo Zhao, Taimeng Fu, Pranay Reddy, Xiao Lin, Wenshan Wang, Jingnan Shi, Rajat Talak, Kun Cao, Yi Du, Han Wang, Huai Yu, Shanzhao Wang, Siyu Chen, Ananth Kashyap, Rohan Bandaru, Karthik Dantu, Jiajun Wu, Lihua Xie, Luca Carlone, Marco Hutter, Sebastian Scherer | Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and 2nd-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than 10x speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_PyPose_A_Library_for_Robot_Learning_With_Physics-Based_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_PyPose_A_Library_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.15428 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PyPose_A_Library_for_Robot_Learning_With_Physics-Based_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PyPose_A_Library_for_Robot_Learning_With_Physics-Based_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Make Landscape Flatter in Differentially Private Federated Learning | Yifan Shi, Yingqi Liu, Kang Wei, Li Shen, Xueqian Wang, Dacheng Tao | To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise. However, existing DPFL methods tend to make a sharper loss landscape and have poorer weight perturbation robustness, resulting in severe performance degradation. To alleviate these issues, we propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with better stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance. From the theoretical perspective, we analyze in detail how DP-FedSAM mitigates the performance degradation induced by DP. Meanwhile, we give rigorous privacy guarantees with Renyi DP and present the sensitivity analysis of local updates. At last, we empirically confirm that our algorithm achieves state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shi_Make_Landscape_Flatter_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning | Changdae Oh, Hyeji Hwang, Hee-young Lee, YongTaek Lim, Geunyoung Jung, Jiyoung Jung, Hosik Choi, Kyungwoo Song | With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: https://github.com/changdaeoh/BlackVIP | https://openaccess.thecvf.com/content/CVPR2023/papers/Oh_BlackVIP_Black-Box_Visual_Prompting_for_Robust_Transfer_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Oh_BlackVIP_Black-Box_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14773 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Oh_BlackVIP_Black-Box_Visual_Prompting_for_Robust_Transfer_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Oh_BlackVIP_Black-Box_Visual_Prompting_for_Robust_Transfer_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
DeepVecFont-v2: Exploiting Transformers To Synthesize Vector Fonts With Higher Quality | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DeepVecFont-v2_Exploiting_Transformers_To_Synthesize_Vector_Fonts_With_Higher_Quality_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DeepVecFont-v2_Exploiting_Transformers_To_Synthesize_Vector_Fonts_With_Higher_Quality_CVPR_2023_paper.html | CVPR 2023 | null |
pCON: Polarimetric Coordinate Networks for Neural Scene Representations | Henry Peters, Yunhao Ba, Achuta Kadambi | Neural scene representations have achieved great success in parameterizing and reconstructing images, but current state of the art models are not optimized with the preservation of physical quantities in mind. While current architectures can reconstruct color images correctly, they create artifacts when trying to fit maps of polar quantities. We propose polarimetric coordinate networks (pCON), a new model architecture for neural scene representations aimed at preserving polarimetric information while accurately parameterizing the scene. Our model removes artifacts created by current coordinate network architectures when reconstructing three polarimetric quantities of interest. | https://openaccess.thecvf.com/content/CVPR2023/papers/Peters_pCON_Polarimetric_Coordinate_Networks_for_Neural_Scene_Representations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Peters_pCON_Polarimetric_Coordinate_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peters_pCON_Polarimetric_Coordinate_Networks_for_Neural_Scene_Representations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peters_pCON_Polarimetric_Coordinate_Networks_for_Neural_Scene_Representations_CVPR_2023_paper.html | CVPR 2023 | null |
Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization Tasks | Hyolim Kang, Hanjung Kim, Joungbin An, Minsu Cho, Seon Joo Kim | Temporal Action Localization (TAL) methods typically operate on top of feature sequences from a frozen snippet encoder that is pretrained with the Trimmed Action Classification (TAC) tasks, resulting in a task discrepancy problem. While existing TAL methods mitigate this issue either by retraining the encoder with a pretext task or by end-to-end finetuning, they commonly require an overload of high memory and computation. In this work, we introduce Soft-Landing (SoLa) strategy, an efficient yet effective framework to bridge the transferability gap between the pretrained encoder and the downstream tasks by incorporating a light-weight neural network, i.e., a SoLa module, on top of the frozen encoder. We also propose an unsupervised training scheme for the SoLa module; it learns with inter-frame Similarity Matching that uses the frame interval as its supervisory signal, eliminating the need for temporal annotations. Experimental evaluation on various benchmarks for downstream TAL tasks shows that our method effectively alleviates the task discrepancy problem with remarkable computational efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Soft-Landing_Strategy_for_Alleviating_the_Task_Discrepancy_Problem_in_Temporal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Soft-Landing_Strategy_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.06023 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Soft-Landing_Strategy_for_Alleviating_the_Task_Discrepancy_Problem_in_Temporal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Soft-Landing_Strategy_for_Alleviating_the_Task_Discrepancy_Problem_in_Temporal_CVPR_2023_paper.html | CVPR 2023 | null |
Visibility Aware Human-Object Interaction Tracking From Single RGB Camera | Xianghui Xie, Bharat Lal Bhatnagar, Gerard Pons-Moll | Capturing the interactions between humans and their environment in 3D is important for many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D human and object from a single RGB image do not have consistent relative translation across frames because they assume a fixed depth. Moreover, their performance drops significantly when the object is occluded. In this work, we propose a novel method to track the 3D human, object, contacts, and relative translation across frames from a single RGB camera, while being robust to heavy occlusions. Our method is built on two key insights. First, we condition our neural field reconstructions for human and object on per-frame SMPL model estimates obtained by pre-fitting SMPL to a video sequence. This improves neural reconstruction accuracy and produces coherent relative translation across frames. Second, human and object motion from visible frames provides valuable information to infer the occluded object. We propose a novel transformer-based neural network that explicitly uses object visibility and human motion to leverage neighboring frames to make predictions for the occluded frames. Building on these insights, our method is able to track both human and object robustly even under occlusions. Experiments on two datasets show that our method significantly improves over the state-of-the-art methods. Our code and pretrained models are available at: https://virtualhumans.mpi-inf.mpg.de/VisTracker. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Visibility_Aware_Human-Object_Interaction_Tracking_From_Single_RGB_Camera_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Visibility_Aware_Human-Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16479 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Visibility_Aware_Human-Object_Interaction_Tracking_From_Single_RGB_Camera_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Visibility_Aware_Human-Object_Interaction_Tracking_From_Single_RGB_Camera_CVPR_2023_paper.html | CVPR 2023 | null |
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