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DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling | Jisoo Jeong, Hong Cai, Risheek Garrepalli, Fatih Porikli | We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames. Based on a mixing ratio, we combine one of the frames in the pair with a distractor image depicting a similar domain, which allows for inducing visual perturbations congruent with natural objects and scenes. We refer to such pairs as distracted pairs. Our intuition is that using semantically meaningful distractors enables the model to learn related variations and attain robustness against challenging deviations, compared to conventional augmentation schemes focusing only on low-level aspects and modifications. More specifically, in addition to the supervised loss computed between the estimated flow for the original pair and its ground-truth flow, we include a second supervised loss defined between the distracted pair's flow and the original pair's ground-truth flow, weighted with the same mixing ratio. Furthermore, when unlabeled data is available, we extend our augmentation approach to self-supervised settings through pseudo-labeling and cross-consistency regularization. Given an original pair and its distracted version, we enforce the estimated flow on the distracted pair to agree with the flow of the original pair. Our approach allows increasing the number of available training pairs significantly without requiring additional annotations. It is agnostic to the model architecture and can be applied to training any optical flow estimation models. Our extensive evaluations on multiple benchmarks, including Sintel, KITTI, and SlowFlow, show that DistractFlow improves existing models consistently, outperforming the latest state of the art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jeong_DistractFlow_Improving_Optical_Flow_Estimation_via_Realistic_Distractions_and_Pseudo-Labeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jeong_DistractFlow_Improving_Optical_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jeong_DistractFlow_Improving_Optical_Flow_Estimation_via_Realistic_Distractions_and_Pseudo-Labeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jeong_DistractFlow_Improving_Optical_Flow_Estimation_via_Realistic_Distractions_and_Pseudo-Labeling_CVPR_2023_paper.html | CVPR 2023 | null |
Test of Time: Instilling Video-Language Models With a Sense of Time | Piyush Bagad, Makarand Tapaswi, Cees G. M. Snoek | Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bagad_Test_of_Time_Instilling_Video-Language_Models_With_a_Sense_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bagad_Test_of_Time_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02074 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bagad_Test_of_Time_Instilling_Video-Language_Models_With_a_Sense_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bagad_Test_of_Time_Instilling_Video-Language_Models_With_a_Sense_of_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Segment Every Referring Object Point by Point | Mengxue Qu, Yu Wu, Yunchao Wei, Wu Liu, Xiaodan Liang, Yao Zhao | Referring Expression Segmentation (RES) can facilitate pixel-level semantic alignment between vision and language. Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., training using abundant referring bounding boxes and only a few (e.g., 1%) pixel-level referring masks. To maximize the transferability from the REC model, we construct our model based on the point-based sequence prediction model. We propose the co-content teacher-forcing to make the model explicitly associate the point coordinates (scale values) with the referred spatial features, which alleviates the exposure bias caused by the limited segmentation masks. To make the most of referring bounding box annotations, we further propose the resampling pseudo points strategy to select more accurate pseudo-points as supervision. Extensive experiments show that our model achieves 52.06% in terms of accuracy (versus 58.93% in fully supervised setting) on RefCOCO+@testA, when only using 1% of the mask annotations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qu_Learning_To_Segment_Every_Referring_Object_Point_by_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qu_Learning_To_Segment_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_Learning_To_Segment_Every_Referring_Object_Point_by_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_Learning_To_Segment_Every_Referring_Object_Point_by_Point_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing With Sound: Long-range Acoustic Beamforming for Multimodal Scene Understanding | Praneeth Chakravarthula, Jim Aldon D’Souza, Ethan Tseng, Joe Bartusek, Felix Heide | Existing autonomous vehicles primarily use sensors that rely on electromagnetic waves which are undisturbed in good environmental conditions but can suffer in adverse scenarios, such as low light or for objects with low reflectance. Moreover, only objects in direct line-of-sight are typically detected by these existing methods. Acoustic pressure waves emanating from road users do not share these limitations. However, such signals are typically ignored in automotive perception because they suffer from low spatial resolution and lack directional information. In this work, we introduce long-range acoustic beamforming of pressure waves from noise directly produced by automotive vehicles in-the-wild as a complementary sensing modality to traditional optical sensor approaches for detection of objects in dynamic traffic environments. To this end, we introduce the first multimodal long-range acoustic beamforming dataset. We propose a neural aperture expansion method for beamforming and we validate its utility for multimodal automotive object detection. We validate the benefit of adding sound detections to existing RGB cameras in challenging automotive scenarios, where camera-only approaches fail or do not deliver the ultra-fast rates of pressure sensors. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chakravarthula_Seeing_With_Sound_Long-range_Acoustic_Beamforming_for_Multimodal_Scene_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chakravarthula_Seeing_With_Sound_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chakravarthula_Seeing_With_Sound_Long-range_Acoustic_Beamforming_for_Multimodal_Scene_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chakravarthula_Seeing_With_Sound_Long-range_Acoustic_Beamforming_for_Multimodal_Scene_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
OpenScene: 3D Scene Understanding With Open Vocabularies | Songyou Peng, Kyle Genova, Chiyu “Max” Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser | Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach where a model predicts dense features for 3D scene points that are co-embedded with text and image pixels in CLIP feature space. This zero-shot approach enables task-agnostic training and open-vocabulary queries. For example, to perform SOTA zero-shot 3D semantic segmentation it first infers CLIP features for every 3D point and later classifies them based on similarities to embeddings of arbitrary class labels. More interestingly, it enables a suite of open-vocabulary scene understanding applications that have never been done before. For example, it allows a user to enter an arbitrary text query and then see a heat map indicating which parts of a scene match. Our approach is effective at identifying objects, materials, affordances, activities, and room types in complex 3D scenes, all using a single model trained without any labeled 3D data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_OpenScene_3D_Scene_Understanding_With_Open_Vocabularies_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Peng_OpenScene_3D_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15654 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_OpenScene_3D_Scene_Understanding_With_Open_Vocabularies_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_OpenScene_3D_Scene_Understanding_With_Open_Vocabularies_CVPR_2023_paper.html | CVPR 2023 | null |
Movies2Scenes: Using Movie Metadata To Learn Scene Representation | Shixing Chen, Chun-Hao Liu, Xiang Hao, Xiaohan Nie, Maxim Arap, Raffay Hamid | Understanding scenes in movies is crucial for a variety of applications such as video moderation, search, and recommendation. However, labeling individual scenes is a time-consuming process. In contrast, movie level metadata (e.g., genre, synopsis, etc.) regularly gets produced as part of the film production process, and is therefore significantly more commonly available. In this work, we propose a novel contrastive learning approach that uses movie metadata to learn a general-purpose scene representation. Specifically, we use movie metadata to define a measure of movie similarity, and use it during contrastive learning to limit our search for positive scene-pairs to only the movies that are considered similar to each other. Our learned scene representation consistently outperforms existing state-of-the-art methods on a diverse set of tasks evaluated using multiple benchmark datasets. Notably, our learned representation offers an average improvement of 7.9% on the seven classification tasks and 9.7% improvement on the two regression tasks in LVU dataset. Furthermore, using a newly collected movie dataset, we present comparative results of our scene representation on a set of video moderation tasks to demonstrate its generalizability on previously less explored tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Movies2Scenes_Using_Movie_Metadata_To_Learn_Scene_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Movies2Scenes_Using_Movie_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2202.10650 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Movies2Scenes_Using_Movie_Metadata_To_Learn_Scene_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Movies2Scenes_Using_Movie_Metadata_To_Learn_Scene_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
Think Twice Before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving | Xiaosong Jia, Penghao Wu, Li Chen, Jiangwei Xie, Conghui He, Junchi Yan, Hongyang Li | End-to-end autonomous driving has made impressive progress in recent years. Existing methods usually adopt the decoupled encoder-decoder paradigm, where the encoder extracts hidden features from raw sensor data, and the decoder outputs the ego-vehicle's future trajectories or actions. Under such a paradigm, the encoder does not have access to the intended behavior of the ego agent, leaving the burden of finding out safety-critical regions from the massive receptive field and inferring about future situations to the decoder. Even worse, the decoder is usually composed of several simple multi-layer perceptrons (MLP) or GRUs while the encoder is delicately designed (e.g., a combination of heavy ResNets or Transformer). Such an imbalanced resource-task division hampers the learning process. In this work, we aim to alleviate the aforementioned problem by two principles: (1) fully utilizing the capacity of the encoder; (2) increasing the capacity of the decoder. Concretely, we first predict a coarse-grained future position and action based on the encoder features. Then, conditioned on the position and action, the future scene is imagined to check the ramification if we drive accordingly. We also retrieve the encoder features around the predicted coordinate to obtain fine-grained information about the safety-critical region. Finally, based on the predicted future and the retrieved salient feature, we refine the coarse-grained position and action by predicting its offset from ground-truth. The above refinement module could be stacked in a cascaded fashion, which extends the capacity of the decoder with spatial-temporal prior knowledge about the conditioned future. We conduct experiments on the CARLA simulator and achieve state-of-the-art performance in closed-loop benchmarks. Extensive ablation studies demonstrate the effectiveness of each proposed module. Code and models are available at https://github.com/opendrivelab/ThinkTwice. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jia_Think_Twice_Before_Driving_Towards_Scalable_Decoders_for_End-to-End_Autonomous_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jia_Think_Twice_Before_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.06242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jia_Think_Twice_Before_Driving_Towards_Scalable_Decoders_for_End-to-End_Autonomous_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jia_Think_Twice_Before_Driving_Towards_Scalable_Decoders_for_End-to-End_Autonomous_CVPR_2023_paper.html | CVPR 2023 | null |
DSVT: Dynamic Sparse Voxel Transformer With Rotated Sets | Haiyang Wang, Chen Shi, Shaoshuai Shi, Meng Lei, Sen Wang, Di He, Bernt Schiele, Liwei Wang | Designing an efficient yet deployment-friendly 3D backbone to handle sparse point clouds is a fundamental problem in 3D perception. Compared with the customized sparse convolution, the attention mechanism in Transformers is more appropriate for flexibly modeling long-range relationships and is easier to be deployed in real-world applications. However, due to the sparse characteristics of point clouds, it is non-trivial to apply a standard transformer on sparse points. In this paper, we present Dynamic Sparse Voxel Transformer (DSVT), a single-stride window-based voxel Transformer backbone for outdoor 3D perception. In order to efficiently process sparse points in parallel, we propose Dynamic Sparse Window Attention, which partitions a series of local regions in each window according to its sparsity and then computes the features of all regions in a fully parallel manner. To allow the cross-set connection, we design a rotated set partitioning strategy that alternates between two partitioning configurations in consecutive self-attention layers. To support effective downsampling and better encode geometric information, we also propose an attention-style 3D pooling module on sparse points, which is powerful and deployment-friendly without utilizing any customized CUDA operations. Our model achieves state-of-the-art performance with a broad range of 3D perception tasks. More importantly, DSVT can be easily deployed by TensorRT with real-time inference speed (27Hz). Code will be available at https://github.com/Haiyang-W/DSVT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_DSVT_Dynamic_Sparse_Voxel_Transformer_With_Rotated_Sets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_DSVT_Dynamic_Sparse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.06051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DSVT_Dynamic_Sparse_Voxel_Transformer_With_Rotated_Sets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DSVT_Dynamic_Sparse_Voxel_Transformer_With_Rotated_Sets_CVPR_2023_paper.html | CVPR 2023 | null |
Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers | Siyuan Wei, Tianzhu Ye, Shen Zhang, Yao Tang, Jiajun Liang | Although vision transformers (ViTs) have shown promising results in various computer vision tasks recently, their high computational cost limits their practical applications. Previous approaches that prune redundant tokens have demonstrated a good trade-off between performance and computation costs. Nevertheless, errors caused by pruning strategies can lead to significant information loss. Our quantitative experiments reveal that the impact of pruned tokens on performance should be noticeable. To address this issue, we propose a novel joint Token Pruning & Squeezing module (TPS) for compressing vision transformers with higher efficiency. Firstly, TPS adopts pruning to get the reserved and pruned subsets. Secondly, TPS squeezes the information of pruned tokens into partial reserved tokens via the unidirectional nearest-neighbor matching and similarity-oriented fusing steps. Compared to state-of-the-art methods, our approach outperforms them under all token pruning intensities. Especially while shrinking DeiT-tiny&small computational budgets to 35%, it improves the accuracy by 1%-6% compared with baselines on ImageNet classification. The proposed method can accelerate the throughput of DeiT-small beyond DeiT-tiny, while its accuracy surpasses DeiT-tiny by 4.78%. Experiments on various transformers demonstrate the effectiveness of our method, while analysis experiments prove our higher robustness to the errors of the token pruning policy. Code is available at https://github.com/megvii-research/TPS-CVPR2023. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Joint_Token_Pruning_and_Squeezing_Towards_More_Aggressive_Compression_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Joint_Token_Pruning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10716 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Joint_Token_Pruning_and_Squeezing_Towards_More_Aggressive_Compression_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Joint_Token_Pruning_and_Squeezing_Towards_More_Aggressive_Compression_of_CVPR_2023_paper.html | CVPR 2023 | null |
Enhancing the Self-Universality for Transferable Targeted Attacks | Zhipeng Wei, Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang | In this paper, we propose a novel transfer-based targeted attack method that optimizes the adversarial perturbations without any extra training efforts for auxiliary networks on training data. Our new attack method is proposed based on the observation that highly universal adversarial perturbations tend to be more transferable for targeted attacks. Therefore, we propose to make the perturbation to be agnostic to different local regions within one image, which we called as self-universality. Instead of optimizing the perturbations on different images, optimizing on different regions to achieve self-universality can get rid of using extra data. Specifically, we introduce a feature similarity loss that encourages the learned perturbations to be universal by maximizing the feature similarity between adversarial perturbed global images and randomly cropped local regions. With the feature similarity loss, our method makes the features from adversarial perturbations to be more dominant than that of benign images, hence improving targeted transferability. We name the proposed attack method as Self-Universality (SU) attack. Extensive experiments demonstrate that SU can achieve high success rates for transfer-based targeted attacks. On ImageNet-compatible dataset, SU yields an improvement of 12% compared with existing state-of-the-art methods. Code is available at https://github.com/zhipeng-wei/Self-Universality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Enhancing_the_Self-Universality_for_Transferable_Targeted_Attacks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Enhancing_the_Self-Universality_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.03716 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Enhancing_the_Self-Universality_for_Transferable_Targeted_Attacks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Enhancing_the_Self-Universality_for_Transferable_Targeted_Attacks_CVPR_2023_paper.html | CVPR 2023 | null |
Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation With Cross-Scale Distortion Awareness | Zhijie Shen, Zishuo Zheng, Chunyu Lin, Lang Nie, Kang Liao, Shuai Zheng, Yao Zhao | Based on the Manhattan World assumption, most existing indoor layout estimation schemes focus on recovering layouts from vertically compressed 1D sequences. However, the compression procedure confuses the semantics of different planes, yielding inferior performance with ambiguous interpretability. To address this issue, we propose to disentangle this 1D representation by pre-segmenting orthogonal (vertical and horizontal) planes from a complex scene, explicitly capturing the geometric cues for indoor layout estimation. Considering the symmetry between the floor boundary and ceiling boundary, we also design a soft-flipping fusion strategy to assist the pre-segmentation. Besides, we present a feature assembling mechanism to effectively integrate shallow and deep features with distortion distribution awareness. To compensate for the potential errors in pre-segmentation, we further leverage triple attention to reconstruct the disentangled sequences for better performance. Experiments on four popular benchmarks demonstrate our superiority over existing SoTA solutions, especially on the 3DIoU metric. The code is available at https://github.com/zhijieshen-bjtu/DOPNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_Disentangling_Orthogonal_Planes_for_Indoor_Panoramic_Room_Layout_Estimation_With_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_Disentangling_Orthogonal_Planes_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00971 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Disentangling_Orthogonal_Planes_for_Indoor_Panoramic_Room_Layout_Estimation_With_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Disentangling_Orthogonal_Planes_for_Indoor_Panoramic_Room_Layout_Estimation_With_CVPR_2023_paper.html | CVPR 2023 | null |
EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points | Chengwei Zheng, Wenbin Lin, Feng Xu | Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a challenging problem to edit the scenes modeled by NeRF-based methods, especially for dynamic scenes. We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes and even support topological changes. Input with an image sequence from a single camera, our network is trained fully automatically and models topologically varying dynamics using our picked-out surface key points. Then end-users can edit the scene by easily dragging the key points to desired new positions. To achieve this, we propose a scene analysis method to detect and initialize key points by considering the dynamics in the scene, and a weighted key points strategy to model topologically varying dynamics by joint key points and weights optimization. Our method supports intuitive multi-dimensional (up to 3D) editing and can generate novel scenes that are unseen in the input sequence. Experiments demonstrate that our method achieves high-quality editing on various dynamic scenes and outperforms the state-of-the-art. Our code and captured data are available at https://chengwei-zheng.github.io/EditableNeRF/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_EditableNeRF_Editing_Topologically_Varying_Neural_Radiance_Fields_by_Key_Points_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_EditableNeRF_Editing_Topologically_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_EditableNeRF_Editing_Topologically_Varying_Neural_Radiance_Fields_by_Key_Points_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_EditableNeRF_Editing_Topologically_Varying_Neural_Radiance_Fields_by_Key_Points_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Map Prior for Autonomous Driving | Xuan Xiong, Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao | High-definition (HD) semantic maps are a crucial component for autonomous driving on urban streets. Traditional offline HD maps are created through labor-intensive manual annotation processes, which are costly and do not accommodate timely updates. Recently, researchers have proposed to infer local maps based on online sensor observations. However, the range of online map inference is constrained by sensor perception range and is easily affected by occlusions. In this work, we propose Neural Map Prior (NMP), a neural representation of global maps that enables automatic global map updates and enhances local map inference performance. To incorporate the strong map prior into local map inference, we leverage cross-attention to dynamically capture the correlations between current features and prior features. For updating the global neural map prior, we use a learning-based fusion module to guide the network in fusing features from previous traversals. This design allows the network to capture a global neural map prior while making sequential online map predictions. Experimental results on the nuScenes dataset demonstrate that our framework is compatible with most map segmentation/detection methods, improving map prediction performance in challenging weather conditions and over an extended horizon. To the best of our knowledge, this represents the first learning-based system for constructing a global map prior. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiong_Neural_Map_Prior_for_Autonomous_Driving_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.08481 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Neural_Map_Prior_for_Autonomous_Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Neural_Map_Prior_for_Autonomous_Driving_CVPR_2023_paper.html | CVPR 2023 | null |
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective | Yuexiao Ma, Huixia Li, Xiawu Zheng, Xuefeng Xiao, Rui Wang, Shilei Wen, Xin Pan, Fei Chao, Rongrong Ji | Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. In this paper, we take the initiative to explore and present a theoretical proof to explain why such a problem is essential in PTQ. And then, we try to solve this problem by introducing a principled and generalized framework theoretically. In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity. To this end, we define the module capacity (ModCap) under data-dependent and data-free scenarios, where the differentials between adjacent modules are used to measure the degree of oscillation. The problem is then solved by selecting top-k differentials, in which the corresponding modules are jointly optimized and quantized. Extensive experiments demonstrate that our method successfully reduces the performance drop and is generalized to different neural networks and PTQ methods. For example, with 2/4 bit ResNet-50 quantization, our method surpasses the previous state-of-the-art method by 1.9%. It becomes more significant on small model quantization, e.g. surpasses BRECQ method by 6.61% on MobileNetV2*0.5. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_Solving_Oscillation_Problem_in_Post-Training_Quantization_Through_a_Theoretical_Perspective_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_Solving_Oscillation_Problem_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11906 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Solving_Oscillation_Problem_in_Post-Training_Quantization_Through_a_Theoretical_Perspective_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Solving_Oscillation_Problem_in_Post-Training_Quantization_Through_a_Theoretical_Perspective_CVPR_2023_paper.html | CVPR 2023 | null |
PEAL: Prior-Embedded Explicit Attention Learning for Low-Overlap Point Cloud Registration | Junle Yu, Luwei Ren, Yu Zhang, Wenhui Zhou, Lili Lin, Guojun Dai | Learning distinctive point-wise features is critical for low-overlap point cloud registration. Recently, it has achieved huge success in incorporating Transformer into point cloud feature representation, which usually adopts a self-attention module to learn intra-point-cloud features first, then utilizes a cross-attention module to perform feature exchange between input point clouds. Self-attention is computed by capturing the global dependency in geometric space. However, this global dependency can be ambiguous and lacks distinctiveness, especially in indoor low-overlap scenarios, as which the dependence with an extensive range of non-overlapping points introduces ambiguity. To address this issue, we present PEAL, a Prior-embedded Explicit Attention Learning model. By incorporating prior knowledge into the learning process, the points are divided into two parts. One includes points lying in the putative overlapping region and the other includes points lying in the putative non-overlapping region. Then PEAL explicitly learns one-way attention with the putative overlapping points. This simplistic design attains surprising performance, significantly relieving the aforementioned feature ambiguity. Our method improves the Registration Recall by 6+% on the challenging 3DLoMatch benchmark and achieves state-of-the-art performance on Feature Matching Recall, Inlier Ratio, and Registration Recall on both 3DMatch and 3DLoMatch. Code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_PEAL_Prior-Embedded_Explicit_Attention_Learning_for_Low-Overlap_Point_Cloud_Registration_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_PEAL_Prior-Embedded_Explicit_Attention_Learning_for_Low-Overlap_Point_Cloud_Registration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_PEAL_Prior-Embedded_Explicit_Attention_Learning_for_Low-Overlap_Point_Cloud_Registration_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds | Jun-Kun Chen, Jipeng Lyu, Yu-Xiong Wang | This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud representation as the underlying structure to construct NeRFs, inspired by the intuitive interpretation of NeRF rendering as a process that projects or "plots" the associated 3D point cloud to a 2D image plane. To this end, NeuralEditor introduces a novel rendering scheme based on deterministic integration within K-D tree-guided density-adaptive voxels, which produces both high-quality rendering results and precise point clouds through optimization. NeuralEditor then performs shape editing via mapping associated points between point clouds. Extensive evaluation shows that NeuralEditor achieves state-of-the-art performance in both shape deformation and scene morphing tasks. Notably, NeuralEditor supports both zero-shot inference and further fine-tuning over the edited scene. Our code, benchmark, and demo video are available at https://immortalco.github.io/NeuralEditor. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_NeuralEditor_Editing_Neural_Radiance_Fields_via_Manipulating_Point_Clouds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_NeuralEditor_Editing_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.03049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_NeuralEditor_Editing_Neural_Radiance_Fields_via_Manipulating_Point_Clouds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_NeuralEditor_Editing_Neural_Radiance_Fields_via_Manipulating_Point_Clouds_CVPR_2023_paper.html | CVPR 2023 | null |
NIKI: Neural Inverse Kinematics With Invertible Neural Networks for 3D Human Pose and Shape Estimation | Jiefeng Li, Siyuan Bian, Qi Liu, Jiasheng Tang, Fan Wang, Cewu Lu | With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bi-directional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI can learn from both the forward and inverse processes with invertible networks. In the inverse process, the model separates the error from the plausible 3D pose manifold for a robust 3D human pose estimation. In the forward process, we enforce the zero-error boundary conditions to improve the sensitivity to reliable joint positions for better mesh-image alignment. Furthermore, NIKI emulates the analytical inverse kinematics algorithms with the twist-and-swing decomposition for better interpretability. Experiments on standard and occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we exhibit robust and well-aligned results simultaneously. Code is available at https://github.com/Jeff-sjtu/NIKI | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_NIKI_Neural_Inverse_Kinematics_With_Invertible_Neural_Networks_for_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_NIKI_Neural_Inverse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.08590 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_NIKI_Neural_Inverse_Kinematics_With_Invertible_Neural_Networks_for_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_NIKI_Neural_Inverse_Kinematics_With_Invertible_Neural_Networks_for_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Image Modeling With Local Multi-Scale Reconstruction | Haoqing Wang, Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhi-Hong Deng, Kai Han | Masked Image Modeling (MIM) achieves outstanding success in self-supervised representation learning. Unfortunately, MIM models typically have huge computational burden and slow learning process, which is an inevitable obstacle for their industrial applications. Although the lower layers play the key role in MIM, existing MIM models conduct reconstruction task only at the top layer of encoder. The lower layers are not explicitly guided and the interaction among their patches is only used for calculating new activations. Considering the reconstruction task requires non-trivial inter-patch interactions to reason target signals, we apply it to multiple local layers including lower and upper layers. Further, since the multiple layers expect to learn the information of different scales, we design local multi-scale reconstruction, where the lower and upper layers reconstruct fine-scale and coarse-scale supervision signals respectively. This design not only accelerates the representation learning process by explicitly guiding multiple layers, but also facilitates multi-scale semantical understanding to the input. Extensive experiments show that with significantly less pre-training burden, our model achieves comparable or better performance on classification, detection and segmentation tasks than existing MIM models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Masked_Image_Modeling_With_Local_Multi-Scale_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Masked_Image_Modeling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Masked_Image_Modeling_With_Local_Multi-Scale_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Masked_Image_Modeling_With_Local_Multi-Scale_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Transfer4D: A Framework for Frugal Motion Capture and Deformation Transfer | Shubh Maheshwari, Rahul Narain, Ramya Hebbalaguppe | Animating a virtual character based on a real performance of an actor is a challenging task that currently requires expensive motion capture setups and additional effort by expert animators, rendering it accessible only to large production houses. The goal of our work is to democratize this task by developing a frugal alternative termed "Transfer4D" that uses only commodity depth sensors and further reduces animators' effort by automating the rigging and animation transfer process. To handle sparse, incomplete videos from depth video inputs and large variations between source and target objects, we propose to use skeletons as an intermediary representation between motion capture and transfer. We propose a novel skeleton extraction pipeline from single-view depth sequence that incorporates additional geometric information, resulting in superior performance in motion reconstruction and transfer in comparison to the contemporary methods. We use non-rigid reconstruction to track motion from the depth sequence, and then we rig the source object using skinning decomposition. Finally, the rig is embedded into the target object for motion retargeting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Maheshwari_Transfer4D_A_Framework_for_Frugal_Motion_Capture_and_Deformation_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Maheshwari_Transfer4D_A_Framework_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Maheshwari_Transfer4D_A_Framework_for_Frugal_Motion_Capture_and_Deformation_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Maheshwari_Transfer4D_A_Framework_for_Frugal_Motion_Capture_and_Deformation_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
GeoVLN: Learning Geometry-Enhanced Visual Representation With Slot Attention for Vision-and-Language Navigation | Jingyang Huo, Qiang Sun, Boyan Jiang, Haitao Lin, Yanwei Fu | Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huo_GeoVLN_Learning_Geometry-Enhanced_Visual_Representation_With_Slot_Attention_for_Vision-and-Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huo_GeoVLN_Learning_Geometry-Enhanced_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huo_GeoVLN_Learning_Geometry-Enhanced_Visual_Representation_With_Slot_Attention_for_Vision-and-Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huo_GeoVLN_Learning_Geometry-Enhanced_Visual_Representation_With_Slot_Attention_for_Vision-and-Language_CVPR_2023_paper.html | CVPR 2023 | null |
KiUT: Knowledge-Injected U-Transformer for Radiology Report Generation | Zhongzhen Huang, Xiaofan Zhang, Shaoting Zhang | Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing. Although various image caption methods have shown remarkable performance in the natural image field, generating accurate reports for medical images requires knowledge of multiple modalities, including vision, language, and medical terminology. We propose a Knowledge-injected U-Transformer (KiUT) to learn multi-level visual representation and adaptively distill the information with contextual and clinical knowledge for word prediction. In detail, a U-connection schema between the encoder and decoder is designed to model interactions between different modalities. And a symptom graph and an injected knowledge distiller are developed to assist the report generation. Experimentally, we outperform state-of-the-art methods on two widely used benchmark datasets: IU-Xray and MIMIC-CXR. Further experimental results prove the advantages of our architecture and the complementary benefits of the injected knowledge. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_KiUT_Knowledge-Injected_U-Transformer_for_Radiology_Report_Generation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_KiUT_Knowledge-Injected_U-Transformer_for_Radiology_Report_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_KiUT_Knowledge-Injected_U-Transformer_for_Radiology_Report_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Flexible-Cm GAN: Towards Precise 3D Dose Prediction in Radiotherapy | Riqiang Gao, Bin Lou, Zhoubing Xu, Dorin Comaniciu, Ali Kamen | Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient. However, previous deep methods are primarily limited to simple scenarios, e.g., a fixed planning type or a consistent beam angle configuration. This in fact limits the usability of such approaches and makes them not generalizable over a larger set of clinical scenarios. Herein, we propose a novel conditional generative model, Flexible-C^m GAN, utilizing additional information regarding planning types and various beam geometries. A miss-consistency loss is proposed to deal with the challenge of having a limited set of conditions on the input data, e.g., incomplete training samples. To address the challenges of including clinical preferences, we derive a differentiable shift-dose-volume loss to incorporate the well-known dose-volume histogram constraints. During inference, users can flexibly choose a specific planning type and a set of beam angles to meet the clinical requirements. We conduct experiments on an illustrative face dataset to show the motivation of Flexible-C^m GAN and further validate our model's potential clinical values with two radiotherapy datasets. The results demonstrate the superior performance of the proposed method in a practical heterogeneous radiotherapy planning application compared to existing deep learning-based approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Flexible-Cm_GAN_Towards_Precise_3D_Dose_Prediction_in_Radiotherapy_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_Flexible-Cm_GAN_Towards_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Flexible-Cm_GAN_Towards_Precise_3D_Dose_Prediction_in_Radiotherapy_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Flexible-Cm_GAN_Towards_Precise_3D_Dose_Prediction_in_Radiotherapy_CVPR_2023_paper.html | CVPR 2023 | null |
Randomized Adversarial Training via Taylor Expansion | Gaojie Jin, Xinping Yi, Dengyu Wu, Ronghui Mu, Xiaowei Huang | In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the most successful methods. To deal with both the robustness against adversarial examples and the accuracy over clean examples, many works develop enhanced adversarial training methods to achieve various trade-offs between them. Leveraging over the studies that smoothed update on weights during training may help find flat minima and improve generalization, we suggest reconciling the robustness-accuracy trade-off from another perspective, i.e., by adding random noise into deterministic weights. The randomized weights enable our design of a novel adversarial training method via Taylor expansion of a small Gaussian noise, and we show that the new adversarial training method can flatten loss landscape and find flat minima. With PGD, CW, and Auto Attacks, an extensive set of experiments demonstrate that our method enhances the state-of-the-art adversarial training methods, boosting both robustness and clean accuracy. The code is available at https://github.com/Alexkael/Randomized-Adversarial-Training. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Randomized_Adversarial_Training_via_Taylor_Expansion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Randomized_Adversarial_Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10653 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Randomized_Adversarial_Training_via_Taylor_Expansion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Randomized_Adversarial_Training_via_Taylor_Expansion_CVPR_2023_paper.html | CVPR 2023 | null |
Handy: Towards a High Fidelity 3D Hand Shape and Appearance Model | Rolandos Alexandros Potamias, Stylianos Ploumpis, Stylianos Moschoglou, Vasileios Triantafyllou, Stefanos Zafeiriou | Over the last few years, with the advent of virtual and augmented reality, an enormous amount of research has been focused on modeling, tracking and reconstructing human hands. Given their power to express human behavior, hands have been a very important, but challenging component of the human body. Currently, most of the state-of-the-art reconstruction and pose estimation methods rely on the low polygon MANO model. Apart from its low polygon count, MANO model was trained with only 31 adult subjects, which not only limits its expressive power but also imposes unnecessary shape reconstruction constraints on pose estimation methods. Moreover, hand appearance remains almost unexplored and neglected from the majority of hand reconstruction methods. In this work, we propose "Handy", a large-scale model of the human hand, modeling both shape and appearance composed of over 1200 subjects which we make publicly available for the benefit of the research community. In contrast to current models, our proposed hand model was trained on a dataset with large diversity in age, gender, and ethnicity, which tackles the limitations of MANO and accurately reconstructs out-of-distribution samples. In order to create a high quality texture model, we trained a powerful GAN, which preserves high frequency details and is able to generate high resolution hand textures. To showcase the capabilities of the proposed model, we built a synthetic dataset of textured hands and trained a hand pose estimation network to reconstruct both the shape and appearance from single images. As it is demonstrated in an extensive series of quantitative as well as qualitative experiments, our model proves to be robust against the state-of-the-art and realistically captures the 3D hand shape and pose along with a high frequency detailed texture even in adverse "in-the-wild" conditions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Potamias_Handy_Towards_a_High_Fidelity_3D_Hand_Shape_and_Appearance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Potamias_Handy_Towards_a_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Potamias_Handy_Towards_a_High_Fidelity_3D_Hand_Shape_and_Appearance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Potamias_Handy_Towards_a_High_Fidelity_3D_Hand_Shape_and_Appearance_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space | Tianxin Huang, Zhonggan Ding, Jiangning Zhang, Ying Tai, Zhenyu Zhang, Mingang Chen, Chengjie Wang, Yong Liu | For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. Most existing works measure the training loss with point-to-point distance, which may introduce extra defects as predefined matching rules may deviate from the real shape differences. Although some learning-based works have been proposed to overcome the weaknesses of manually-defined rules, they still measure the shape differences in 3D Euclidean space, which may limit their ability to capture defects in reconstructed shapes. In this work, we propose a learning-based Contrastive Adversarial Loss (CALoss) to measure the point cloud reconstruction loss dynamically in a non-linear representation space by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Learning_To_Measure_the_Point_Cloud_Reconstruction_Loss_in_a_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Learning_To_Measure_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_To_Measure_the_Point_Cloud_Reconstruction_Loss_in_a_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_To_Measure_the_Point_Cloud_Reconstruction_Loss_in_a_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Neighbor Consistency Mining for Correspondence Pruning | Xin Liu, Jufeng Yang | The goal of correspondence pruning is to recognize correct correspondences (inliers) from initial ones, with applications to various feature matching based tasks. Seeking neighbors in the coordinate and feature spaces is a common strategy in many previous methods. However, it is difficult to ensure that these neighbors are always consistent, since the distribution of false correspondences is extremely irregular. For addressing this problem, we propose a novel global-graph space to search for consistent neighbors based on a weighted global graph that can explicitly explore long-range dependencies among correspondences. On top of that, we progressively construct three neighbor embeddings according to different neighbor search spaces, and design a Neighbor Consistency block to extract neighbor context and explore their interactions sequentially. In the end, we develop a Neighbor Consistency Mining Network (NCMNet) for accurately recovering camera poses and identifying inliers. Experimental results indicate that our NCMNet achieves a significant performance advantage over state-of-the-art competitors on challenging outdoor and indoor matching scenes. The source code can be found at https://github.com/xinliu29/NCMNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Progressive_Neighbor_Consistency_Mining_for_Correspondence_Pruning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Progressive_Neighbor_Consistency_Mining_for_Correspondence_Pruning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Progressive_Neighbor_Consistency_Mining_for_Correspondence_Pruning_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Zoom and Unzoom | Chittesh Thavamani, Mengtian Li, Francesco Ferroni, Deva Ramanan | Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to upsample" as well. Code and additional visuals are available at https://tchittesh.github.io/lzu/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Thavamani_Learning_To_Zoom_and_Unzoom_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Thavamani_Learning_To_Zoom_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15390 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Thavamani_Learning_To_Zoom_and_Unzoom_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Thavamani_Learning_To_Zoom_and_Unzoom_CVPR_2023_paper.html | CVPR 2023 | null |
Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning | Wenjin Wang, Yunqing Hu, Qianglong Chen, Yin Zhang | Parameter regularization or allocation methods are effective in overcoming catastrophic forgetting in lifelong learning. However, they solve all tasks in a sequence uniformly and ignore the differences in the learning difficulty of different tasks. So parameter regularization methods face significant forgetting when learning a new task very different from learned tasks, and parameter allocation methods face unnecessary parameter overhead when learning simple tasks. In this paper, we propose the Parameter Allocation & Regularization (PAR), which adaptively select an appropriate strategy for each task from parameter allocation and regularization based on its learning difficulty. A task is easy for a model that has learned tasks related to it and vice versa. We propose a divergence estimation method based on the Nearest-Prototype distance to measure the task relatedness using only features of the new task. Moreover, we propose a time-efficient relatedness-aware sampling-based architecture search strategy to reduce the parameter overhead for allocation. Experimental results on multiple benchmarks demonstrate that, compared with SOTAs, our method is scalable and significantly reduces the model's redundancy while improving the model's performance. Further qualitative analysis indicates that PAR obtains reasonable task-relatedness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Task_Difficulty_Aware_Parameter_Allocation__Regularization_for_Lifelong_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Task_Difficulty_Aware_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05288 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Task_Difficulty_Aware_Parameter_Allocation__Regularization_for_Lifelong_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Task_Difficulty_Aware_Parameter_Allocation__Regularization_for_Lifelong_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Bootstrapping Objectness From Videos by Relaxed Common Fate and Visual Grouping | Long Lian, Zhirong Wu, Stella X. Yu | We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move at the same speed belong together, has inspired unsupervised object discovery based on motion segmentation. However, common fate is not a reliable indicator of objectness: Parts of an articulated / deformable object may not move at the same speed, whereas shadows / reflections of an object always move with it but are not part of it. Our insight is to bootstrap objectness by first learning image features from relaxed common fate and then refining them based on visual appearance grouping within the image itself and across images statistically. Specifically, we learn an image segmenter first in the loop of approximating optical flow with constant segment flow plus small within-segment residual flow, and then by refining it for more coherent appearance and statistical figure-ground relevance. On unsupervised video object segmentation, using only ResNet and convolutional heads, our model surpasses the state-of-the-art by absolute gains of 7/9/5% on DAVIS16 / STv2 / FBMS59 respectively, demonstrating the effectiveness of our ideas. Our code is publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lian_Bootstrapping_Objectness_From_Videos_by_Relaxed_Common_Fate_and_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lian_Bootstrapping_Objectness_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08025 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lian_Bootstrapping_Objectness_From_Videos_by_Relaxed_Common_Fate_and_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lian_Bootstrapping_Objectness_From_Videos_by_Relaxed_Common_Fate_and_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
From Node Interaction To Hop Interaction: New Effective and Scalable Graph Learning Paradigm | Jie Chen, Zilong Li, Yin Zhu, Junping Zhang, Jian Pu | Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made, such node interaction paradigms still have the following limitation. First, the scalability limitation precludes the broad application of GNNs in large-scale industrial settings since the node interaction among rapidly expanding neighbors incurs high computation and memory costs. Second, the over-smoothing problem restricts the discrimination ability of nodes, i.e., node representations of different classes will converge to indistinguishable after repeated node interactions. In this work, we propose a novel hop interaction paradigm to address these limitations simultaneously. The core idea is to convert the interaction target among nodes to pre-processed multi-hop features inside each node. We design a simple yet effective HopGNN framework that can easily utilize existing GNNs to achieve hop interaction. Furthermore, we propose a multi-task learning strategy with a self-supervised learning objective to enhance HopGNN. We conduct extensive experiments on 12 benchmark datasets in a wide range of domains, scales, and smoothness of graphs. Experimental results show that our methods achieve superior performance while maintaining high scalability and efficiency. The code is at https://github.com/JC-202/HopGNN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_From_Node_Interaction_To_Hop_Interaction_New_Effective_and_Scalable_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_From_Node_Interaction_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_From_Node_Interaction_To_Hop_Interaction_New_Effective_and_Scalable_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_From_Node_Interaction_To_Hop_Interaction_New_Effective_and_Scalable_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised Hand Appearance Recovery via Structure Disentanglement and Dual Adversarial Discrimination | Zimeng Zhao, Binghui Zuo, Zhiyu Long, Yangang Wang | Enormous hand images with reliable annotations are collected through marker-based MoCap. Unfortunately, degradations caused by markers limit their application in hand appearance reconstruction. A clear appearance recovery insight is an image-to-image translation trained with unpaired data. However, most frameworks fail because there exists structure inconsistency from a degraded hand to a bare one. The core of our approach is to first disentangle the bare hand structure from those degraded images and then wrap the appearance to this structure with a dual adversarial discrimination (DAD) scheme. Both modules take full advantage of the semi-supervised learning paradigm: The structure disentanglement benefits from the modeling ability of ViT, and the translator is enhanced by the dual discrimination on both translation processes and translation results. Comprehensive evaluations have been conducted to prove that our framework can robustly recover photo-realistic hand appearance from diverse marker-contained and even object-occluded datasets. It provides a novel avenue to acquire bare hand appearance data for other downstream learning problems. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Semi-Supervised_Hand_Appearance_Recovery_via_Structure_Disentanglement_and_Dual_Adversarial_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Semi-Supervised_Hand_Appearance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06380 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Semi-Supervised_Hand_Appearance_Recovery_via_Structure_Disentanglement_and_Dual_Adversarial_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Semi-Supervised_Hand_Appearance_Recovery_via_Structure_Disentanglement_and_Dual_Adversarial_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding and Improving Features Learned in Deep Functional Maps | Souhaib Attaiki, Maks Ovsjanikov | Deep functional maps have recently emerged as a successful paradigm for non-rigid 3D shape correspondence tasks. An essential step in this pipeline consists in learning feature functions that are used as constraints to solve for a functional map inside the network. However, the precise nature of the information learned and stored in these functions is not yet well understood. Specifically, a major question is whether these features can be used for any other objective, apart from their purely algebraic role, in solving for functional map matrices. In this paper, we show that under some mild conditions, the features learned within deep functional map approaches can be used as point-wise descriptors and thus are directly comparable across different shapes, even without the necessity of solving for a functional map at test time. Furthermore, informed by our analysis, we propose effective modifications to the standard deep functional map pipeline, which promotes structural properties of learned features, significantly improving the matching results. Finally, we demonstrate that previously unsuccessful attempts at using extrinsic architectures for deep functional map feature extraction can be remedied via simple architectural changes, which promote the theoretical properties suggested by our analysis. We thus bridge the gap between intrinsic and extrinsic surface-based learning, suggesting the necessary and sufficient conditions for successful shape matching. Our code is available at https://github.com/pvnieo/clover. | https://openaccess.thecvf.com/content/CVPR2023/papers/Attaiki_Understanding_and_Improving_Features_Learned_in_Deep_Functional_Maps_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Attaiki_Understanding_and_Improving_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16527 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Attaiki_Understanding_and_Improving_Features_Learned_in_Deep_Functional_Maps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Attaiki_Understanding_and_Improving_Features_Learned_in_Deep_Functional_Maps_CVPR_2023_paper.html | CVPR 2023 | null |
Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption | Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, Dequan Wang | Test-time adaptation harnesses test inputs to improve the accuracy of a model trained on source data when tested on shifted target data. Most methods update the source model by (re-)training on each target domain. While re-training can help, it is sensitive to the amount and order of the data and the hyperparameters for optimization. We update the target data instead, and project all test inputs toward the source domain with a generative diffusion model. Our diffusion-driven adaptation (DDA) method shares its models for classification and generation across all domains, training both on source then freezing them for all targets, to avoid expensive domain-wise re-training. We augment diffusion with image guidance and classifier self-ensembling to automatically decide how much to adapt. Input adaptation by DDA is more robust than model adaptation across a variety of corruptions, models, and data regimes on the ImageNet-C benchmark. With its input-wise updates, DDA succeeds where model adaptation degrades on too little data (small batches), on dependent data (correlated orders), or on mixed data (multiple corruptions). | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Back_to_the_Source_Diffusion-Driven_Adaptation_To_Test-Time_Corruption_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_Back_to_the_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Back_to_the_Source_Diffusion-Driven_Adaptation_To_Test-Time_Corruption_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Back_to_the_Source_Diffusion-Driven_Adaptation_To_Test-Time_Corruption_CVPR_2023_paper.html | CVPR 2023 | null |
PartManip: Learning Cross-Category Generalizable Part Manipulation Policy From Point Cloud Observations | Haoran Geng, Ziming Li, Yiran Geng, Jiayi Chen, Hao Dong, He Wang | Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes. Parts, as the shared components in different object categories, have the potential to increase the generalization ability of the manipulation policy and achieve cross-category object manipulation. In this work, we build the first large-scale, part-based cross-category object manipulation benchmark, PartManip, which is composed of 11 object categories, 494 objects, and 1432 tasks in 6 task classes. Compared to previous work, our benchmark is also more diverse and realistic, i.e., having more objects and using sparse-view point cloud as input without oracle information like part segmentation. To tackle the difficulties of vision-based policy learning, we first train a state-based expert with our proposed part-based canonicalization and part-aware rewards, and then distill the knowledge to a vision-based student. We also find an expressive backbone is essential to overcome the large diversity of different objects. For cross-category generalization, we introduce domain adversarial learning for domain-invariant feature extraction. Extensive experiments in simulation show that our learned policy can outperform other methods by a large margin, especially on unseen object categories. We also demonstrate our method can successfully manipulate novel objects in the real world. | https://openaccess.thecvf.com/content/CVPR2023/papers/Geng_PartManip_Learning_Cross-Category_Generalizable_Part_Manipulation_Policy_From_Point_Cloud_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Geng_PartManip_Learning_Cross-Category_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16958 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_PartManip_Learning_Cross-Category_Generalizable_Part_Manipulation_Policy_From_Point_Cloud_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Geng_PartManip_Learning_Cross-Category_Generalizable_Part_Manipulation_Policy_From_Point_Cloud_CVPR_2023_paper.html | CVPR 2023 | null |
Polynomial Implicit Neural Representations for Large Diverse Datasets | Rajhans Singh, Ankita Shukla, Pavan Turaga | Implicit neural representations (INR) have gained significant popularity for signal and image representation for many end-tasks, such as superresolution, 3D modeling, and more. Most INR architectures rely on sinusoidal positional encoding, which accounts for high-frequency information in data. However, the finite encoding size restricts the model's representational power. Higher representational power is needed to go from representing a single given image to representing large and diverse datasets. Our approach addresses this gap by representing an image with a polynomial function and eliminates the need for positional encodings. Therefore, to achieve a progressively higher degree of polynomial representation, we use element-wise multiplications between features and affine-transformed coordinate locations after every ReLU layer. The proposed method is evaluated qualitatively and quantitatively on large datasets like ImageNet. The proposed Poly-INR model performs comparably to state-of-the-art generative models without any convolution, normalization, or self-attention layers, and with far fewer trainable parameters. With much fewer training parameters and higher representative power, our approach paves the way for broader adoption of INR models for generative modeling tasks in complex domains. The code is available at https://github.com/Rajhans0/Poly_INR | https://openaccess.thecvf.com/content/CVPR2023/papers/Singh_Polynomial_Implicit_Neural_Representations_for_Large_Diverse_Datasets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Singh_Polynomial_Implicit_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_Polynomial_Implicit_Neural_Representations_for_Large_Diverse_Datasets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_Polynomial_Implicit_Neural_Representations_for_Large_Diverse_Datasets_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Video Compression With Diverse Contexts | Jiahao Li, Bin Li, Yan Lu | For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://github.com/microsoft/DCVC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Neural_Video_Compression_With_Diverse_Contexts_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Neural_Video_Compression_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14402 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Neural_Video_Compression_With_Diverse_Contexts_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Neural_Video_Compression_With_Diverse_Contexts_CVPR_2023_paper.html | CVPR 2023 | null |
High-Frequency Stereo Matching Network | Haoliang Zhao, Huizhou Zhou, Yongjun Zhang, Jie Chen, Yitong Yang, Yong Zhao | In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_High-Frequency_Stereo_Matching_Network_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_High-Frequency_Stereo_Matching_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_High-Frequency_Stereo_Matching_Network_CVPR_2023_paper.html | CVPR 2023 | null |
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation | Naoto Inoue, Kotaro Kikuchi, Edgar Simo-Serra, Mayu Otani, Kota Yamaguchi | Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named LayoutDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our LayoutDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Inoue_LayoutDM_Discrete_Diffusion_Model_for_Controllable_Layout_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Inoue_LayoutDM_Discrete_Diffusion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Inoue_LayoutDM_Discrete_Diffusion_Model_for_Controllable_Layout_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Inoue_LayoutDM_Discrete_Diffusion_Model_for_Controllable_Layout_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Markerless Camera-to-Robot Pose Estimation via Self-Supervised Sim-to-Real Transfer | Jingpei Lu, Florian Richter, Michael C. Yip | Solving the camera-to-robot pose is a fundamental requirement for vision-based robot control, and is a process that takes considerable effort and cares to make accurate. Traditional approaches require modification of the robot via markers, and subsequent deep learning approaches enabled markerless feature extraction. Mainstream deep learning methods only use synthetic data and rely on Domain Randomization to fill the sim-to-real gap, because acquiring the 3D annotation is labor-intensive. In this work, we go beyond the limitation of 3D annotations for real-world data. We propose an end-to-end pose estimation framework that is capable of online camera-to-robot calibration and a self-supervised training method to scale the training to unlabeled real-world data. Our framework combines deep learning and geometric vision for solving the robot pose, and the pipeline is fully differentiable. To train the Camera-to-Robot Pose Estimation Network (CtRNet), we leverage foreground segmentation and differentiable rendering for image-level self-supervision. The pose prediction is visualized through a renderer and the image loss with the input image is back-propagated to train the neural network. Our experimental results on two public real datasets confirm the effectiveness of our approach over existing works. We also integrate our framework into a visual servoing system to demonstrate the promise of real-time precise robot pose estimation for automation tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Markerless_Camera-to-Robot_Pose_Estimation_via_Self-Supervised_Sim-to-Real_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_Markerless_Camera-to-Robot_Pose_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.14332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Markerless_Camera-to-Robot_Pose_Estimation_via_Self-Supervised_Sim-to-Real_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Markerless_Camera-to-Robot_Pose_Estimation_via_Self-Supervised_Sim-to-Real_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects | Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Rares Andrei Ambrus, Jeannette Bohg, Abhinav Valada, Thomas Kollar | We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a single geometry and articulation decoder for multiple object categories. Despite training on multiple categories, our decoder achieves a comparable reconstruction accuracy to methods that train bespoke decoders separately for each category. Combined with our stereo image encoder we infer the 3D shape, 6D pose, size, joint type, and the joint state of multiple unknown objects in a single forward pass. Our method achieves a 20.4% absolute improvement in mAP 3D IOU50 for novel instances when compared to a two-stage pipeline. Inference time is fast and can run on a NVIDIA TITAN XP GPU at 1 HZ for eight or less objects present. While only trained on simulated data, CARTO transfers to real-world object instances. Code and evaluation data is available at: http://carto.cs.uni-freiburg.de | https://openaccess.thecvf.com/content/CVPR2023/papers/Heppert_CARTO_Category_and_Joint_Agnostic_Reconstruction_of_ARTiculated_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Heppert_CARTO_Category_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15782 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Heppert_CARTO_Category_and_Joint_Agnostic_Reconstruction_of_ARTiculated_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Heppert_CARTO_Category_and_Joint_Agnostic_Reconstruction_of_ARTiculated_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
ShapeTalk: A Language Dataset and Framework for 3D Shape Edits and Deformations | Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, Leonidas Guibas | Editing 3D geometry is a challenging task requiring specialized skills. In this work, we aim to facilitate the task of editing the geometry of 3D models through the use of natural language. For example, we may want to modify a 3D chair model to "make its legs thinner" or to "open a hole in its back". To tackle this problem in a manner that promotes open-ended language use and enables fine-grained shape edits, we introduce the most extensive existing corpus of natural language utterances describing shape differences: ShapeTalk. ShapeTalk contains over half a million discriminative utterances produced by contrasting the shapes of common 3D objects for a variety of object classes and degrees of similarity. We also introduce a generic framework, ChangeIt3D, which builds on ShapeTalk and can use an arbitrary 3D generative model of shapes to produce edits that align the output better with the edit or deformation description. Finally, we introduce metrics for the quantitative evaluation of language-assisted shape editing methods that reflect key desiderata within this editing setup. We note that ShapeTalk allows methods to be trained with explicit 3D-to-language data, bypassing the necessity of "lifting" 2D to 3D using methods like neural rendering, as required by extant 2D image-language foundation models. Our code and data are publicly available at https://changeit3d.github.io/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Achlioptas_ShapeTalk_A_Language_Dataset_and_Framework_for_3D_Shape_Edits_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Achlioptas_ShapeTalk_A_Language_Dataset_and_Framework_for_3D_Shape_Edits_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Achlioptas_ShapeTalk_A_Language_Dataset_and_Framework_for_3D_Shape_Edits_CVPR_2023_paper.html | CVPR 2023 | null |
Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning | Chengzhi Cao, Xueyang Fu, Hongjian Liu, Yukun Huang, Kunyu Wang, Jiebo Luo, Zheng-Jun Zha | Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications. Most existing methods utilize spatial and temporal correlations in frame sequences to obtain discriminative person features. However, inevitable degradations, e.g., motion blur contained in frames often cause ambiguity texture noise and temporal disturbance, leading to the loss of identity-discriminating cues. Recently, a new bio-inspired sensor called event camera, which can asynchronously record intensity changes, brings new vitality to the Re-ID task. With the microsecond resolution and low latency, event cameras can accurately capture the movements of pedestrians even in the aforementioned degraded environments. Inspired by the properties of event cameras, in this work, we propose a Sparse-Dense Complementary Learning Framework, which effectively extracts identity features by fully exploiting the complementary information of dense frames and sparse events. Specifically, for frames, we build a CNN-based module to aggregate the dense features of pedestrian appearance step-by-step, while for event streams, we design a bio-inspired spiking neural backbone, which encodes event signals into sparse feature maps in a spiking form, to present the dynamic motion cues of pedestrians. Finally, a cross feature alignment module is constructed to complementarily fuse motion information from events and appearance cues from frames to enhance identity representation learning. Experiments on several benchmarks show that by employing events and SNN into Re-ID, our method significantly outperforms competitive methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Event-Guided_Person_Re-Identification_via_Sparse-Dense_Complementary_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Event-Guided_Person_Re-Identification_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Event-Guided_Person_Re-Identification_via_Sparse-Dense_Complementary_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Event-Guided_Person_Re-Identification_via_Sparse-Dense_Complementary_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Regularizing Second-Order Influences for Continual Learning | Zhicheng Sun, Yadong Mu, Gang Hua | Continual learning aims to learn on non-stationary data streams without catastrophically forgetting previous knowledge. Prevalent replay-based methods address this challenge by rehearsing on a small buffer holding the seen data, for which a delicate sample selection strategy is required. However, existing selection schemes typically seek only to maximize the utility of the ongoing selection, overlooking the interference between successive rounds of selection. Motivated by this, we dissect the interaction of sequential selection steps within a framework built on influence functions. We manage to identify a new class of second-order influences that will gradually amplify incidental bias in the replay buffer and compromise the selection process. To regularize the second-order effects, a novel selection objective is proposed, which also has clear connections to two widely adopted criteria. Furthermore, we present an efficient implementation for optimizing the proposed criterion. Experiments on multiple continual learning benchmarks demonstrate the advantage of our approach over state-of-the-art methods. Code is available at https://github.com/feifeiobama/InfluenceCL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Regularizing_Second-Order_Influences_for_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Regularizing_Second-Order_Influences_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10177 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Regularizing_Second-Order_Influences_for_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Regularizing_Second-Order_Influences_for_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Spatial-Then-Temporal Self-Supervised Learning for Video Correspondence | Rui Li, Dong Liu | In low-level video analyses, effective representations are important to derive the correspondences between video frames. These representations have been learned in a self-supervised fashion from unlabeled images/videos, using carefully designed pretext tasks in some recent studies. However, the previous work concentrates on either spatial-discriminative features or temporal-repetitive features, with little attention to the synergy between spatial and temporal cues. To address this issue, we propose a novel spatial-then-temporal self-supervised learning method. Specifically, we firstly extract spatial features from unlabeled images via contrastive learning, and secondly enhance the features by exploiting the temporal cues in unlabeled videos via reconstructive learning. In the second step, we design a global correlation distillation loss to ensure the learning not to forget the spatial cues, and we design a local correlation distillation loss to combat the temporal discontinuity that harms the reconstruction. The proposed method outperforms the state-of-the-art self-supervised methods, as established by the experimental results on a series of correspondence-based video analysis tasks. Also, we performed ablation studies to verify the effectiveness of the two-step design as well as the distillation losses. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Spatial-Then-Temporal_Self-Supervised_Learning_for_Video_Correspondence_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Spatial-Then-Temporal_Self-Supervised_Learning_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2209.07778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Spatial-Then-Temporal_Self-Supervised_Learning_for_Video_Correspondence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Spatial-Then-Temporal_Self-Supervised_Learning_for_Video_Correspondence_CVPR_2023_paper.html | CVPR 2023 | null |
Super-Resolution Neural Operator | Min Wei, Xuesong Zhang | We propose Super-resolution Neural Operator (SRNO), a deep operator learning framework that can resolve high-resolution (HR) images at arbitrary scales from the low-resolution (LR) counterparts. Treating the LR-HR image pairs as continuous functions approximated with different grid sizes, SRNO learns the mapping between the corresponding function spaces. From the perspective of approximation theory, SRNO first embeds the LR input into a higher-dimensional latent representation space, trying to capture sufficient basis functions, and then iteratively approximates the implicit image function with a kernel integral mechanism, followed by a final dimensionality reduction step to generate the RGB representation at the target coordinates. The key characteristics distinguishing SRNO from prior continuous SR works are: 1) the kernel integral in each layer is efficiently implemented via the Galerkin-type attention, which possesses non-local properties in the spatial domain and therefore benefits the grid-free continuum; and 2) the multilayer attention architecture allows for the dynamic latent basis update, which is crucial for SR problems to "hallucinate" high-frequency information from the LR image. Experiments show that SRNO outperforms existing continuous SR methods in terms of both accuracy and running time. Our code is at https://github.com/2y7c3/Super-Resolution-Neural-Operator. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Super-Resolution_Neural_Operator_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Super-Resolution_Neural_Operator_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02584 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Super-Resolution_Neural_Operator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Super-Resolution_Neural_Operator_CVPR_2023_paper.html | CVPR 2023 | null |
GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency | Lin Tian, Hastings Greer, François-Xavier Vialard, Roland Kwitt, Raúl San José Estépar, Richard Jarrett Rushmore, Nikolaos Makris, Sylvain Bouix, Marc Niethammer | We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the Jacobian of this composition from the identity matrix. This regularizer -- GradICON -- results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol. The code is available at https://github.com/uncbiag/ICON. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_GradICON_Approximate_Diffeomorphisms_via_Gradient_Inverse_Consistency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_GradICON_Approximate_Diffeomorphisms_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_GradICON_Approximate_Diffeomorphisms_via_Gradient_Inverse_Consistency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_GradICON_Approximate_Diffeomorphisms_via_Gradient_Inverse_Consistency_CVPR_2023_paper.html | CVPR 2023 | null |
LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and Scenes | Meng Wang, Yu-Shen Liu, Yue Gao, Kanle Shi, Yi Fang, Zhizhong Han | Deep Implicit Function (DIF) has gained much popularity as an efficient 3D shape representation. To capture geometry details, current mainstream methods divide 3D shapes into local regions and then learn each one with a local latent code via a decoder, where the decoder shares the geometric similarities among different local regions. Although such local methods can capture more local details, a large diversity of different local regions increases the difficulty of learning an implicit function when treating all regions equally using only a single decoder. In addition, these local regions often exhibit imbalanced distributions, where certain regions have significantly fewer observations. This leads that fine geometry details could not be preserved well. To solve this problem, we propose a novel Local Pattern-specific Implicit Function, named LP-DIF, for representing a shape with some clusters of local regions and multiple decoders, where each decoder only focuses on one cluster of local regions which share a certain pattern. Specifically, we first extract local codes for all regions, and then cluster them into multiple groups in the latent space, where similar regions sharing a common pattern fall into one group. After that, we train multiple decoders for mining local patterns of different groups, which simplifies learning of fine geometric details by reducing the diversity of local regions seen by each decoder. To further alleviate the data-imbalance problem, we introduce a region re-weighting module to each pattern-specific decoder by kernel density estimator, which dynamically re-weights the regions during learning. Our LP-DIF can restore more geometry details, and thus improve the quality of 3D reconstruction. Experiments demonstrate that our method can achieve the state-of-the-art performance over previous methods. Code is available at https://github.com/gtyxyz/lpdif. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_LP-DIF_Learning_Local_Pattern-Specific_Deep_Implicit_Function_for_3D_Objects_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LP-DIF_Learning_Local_Pattern-Specific_Deep_Implicit_Function_for_3D_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LP-DIF_Learning_Local_Pattern-Specific_Deep_Implicit_Function_for_3D_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
PeakConv: Learning Peak Receptive Field for Radar Semantic Segmentation | Liwen Zhang, Xinyan Zhang, Youcheng Zhang, Yufei Guo, Yuanpei Chen, Xuhui Huang, Zhe Ma | The modern machine learning-based technologies have shown considerable potential in automatic radar scene understanding. Among these efforts, radar semantic segmentation (RSS) can provide more refined and detailed information including the moving objects and background clutters within the effective receptive field of the radar. Motivated by the success of convolutional networks in various visual computing tasks, these networks have also been introduced to solve RSS task. However, neither the regular convolution operation nor the modified ones are specific to interpret radar signals. The receptive fields of existing convolutions are defined by the object presentation in optical signals, but these two signals have different perception mechanisms. In classic radar signal processing, the object signature is detected according to a local peak response, i.e., CFAR detection. Inspired by this idea, we redefine the receptive field of the convolution operation as the peak receptive field (PRF) and propose the peak convolution operation (PeakConv) to learn the object signatures in an end-to-end network. By incorporating the proposed PeakConv layers into the encoders, our RSS network can achieve better segmentation results compared with other SoTA methods on a multi-view real-measured dataset collected from an FMCW radar. Our code for PeakConv is available at https://github.com/zlw9161/PKC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_PeakConv_Learning_Peak_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses | Junbong Jang, Kwonmoo Lee, Tae-Kyun Kim | Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://github.com/JunbongJang/contour-tracking/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Jang_Unsupervised_Contour_Tracking_of_Live_Cells_by_Mechanical_and_Cycle_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jang_Unsupervised_Contour_Tracking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08364 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jang_Unsupervised_Contour_Tracking_of_Live_Cells_by_Mechanical_and_Cycle_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jang_Unsupervised_Contour_Tracking_of_Live_Cells_by_Mechanical_and_Cycle_CVPR_2023_paper.html | CVPR 2023 | null |
Explaining Image Classifiers With Multiscale Directional Image Representation | Stefan Kolek, Robert Windesheim, Hector Andrade-Loarca, Gitta Kutyniok, Ron Levie | Image classifiers are known to be difficult to interpret and therefore require explanation methods to understand their decisions. We present ShearletX, a novel mask explanation method for image classifiers based on the shearlet transform -- a multiscale directional image representation. Current mask explanation methods are regularized by smoothness constraints that protect against undesirable fine-grained explanation artifacts. However, the smoothness of a mask limits its ability to separate fine-detail patterns, that are relevant for the classifier, from nearby nuisance patterns, that do not affect the classifier. ShearletX solves this problem by avoiding smoothness regularization all together, replacing it by shearlet sparsity constraints. The resulting explanations consist of a few edges, textures, and smooth parts of the original image, that are the most relevant for the decision of the classifier. To support our method, we propose a mathematical definition for explanation artifacts and an information theoretic score to evaluate the quality of mask explanations. We demonstrate the superiority of ShearletX over previous mask based explanation methods using these new metrics, and present exemplary situations where separating fine-detail patterns allows explaining phenomena that were not explainable before. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kolek_Explaining_Image_Classifiers_With_Multiscale_Directional_Image_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kolek_Explaining_Image_Classifiers_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kolek_Explaining_Image_Classifiers_With_Multiscale_Directional_Image_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kolek_Explaining_Image_Classifiers_With_Multiscale_Directional_Image_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
RGBD2: Generative Scene Synthesis via Incremental View Inpainting Using RGBD Diffusion Models | Jiabao Lei, Jiapeng Tang, Kui Jia | We address the challenge of recovering an underlying scene geometry and colors from a sparse set of RGBD view observations. In this work, we present a new solution termed RGBD2 that sequentially generates novel RGBD views along a camera trajectory, and the scene geometry is simply the fusion result of these views. More specifically, we maintain an intermediate surface mesh used for rendering new RGBD views, which subsequently becomes complete by an inpainting network; each rendered RGBD view is later back-projected as a partial surface and is supplemented into the intermediate mesh. The use of intermediate mesh and camera projection helps solve the tough problem of multi-view inconsistency. We practically implement the RGBD inpainting network as a versatile RGBD diffusion model, which is previously used for 2D generative modeling; we make a modification to its reverse diffusion process to enable our use. We evaluate our approach on the task of 3D scene synthesis from sparse RGBD inputs; extensive experiments on the ScanNet dataset demonstrate the superiority of our approach over existing ones. Project page: https://jblei.site/proj/rgbd-diffusion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lei_RGBD2_Generative_Scene_Synthesis_via_Incremental_View_Inpainting_Using_RGBD_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.05993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_RGBD2_Generative_Scene_Synthesis_via_Incremental_View_Inpainting_Using_RGBD_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_RGBD2_Generative_Scene_Synthesis_via_Incremental_View_Inpainting_Using_RGBD_CVPR_2023_paper.html | CVPR 2023 | null |
Distribution Shift Inversion for Out-of-Distribution Prediction | Runpeng Yu, Songhua Liu, Xingyi Yang, Xinchao Wang | Machine learning society has witnessed the emergence of a myriad of Out-of-Distribution (OoD) algorithms, which address the distribution shift between the training and the testing distribution by searching for a unified predictor or invariant feature representation. However, the task of directly mitigating the distribution shift in the unseen testing set is rarely investigated, due to the unavailability of the testing distribution during the training phase and thus the impossibility of training a distribution translator mapping between the training and testing distribution. In this paper, we explore how to bypass the requirement of testing distribution for distribution translator training and make the distribution translation useful for OoD prediction. We propose a portable Distribution Shift Inversion (DSI) algorithm, in which, before being fed into the prediction model, the OoD testing samples are first linearly combined with additional Gaussian noise and then transferred back towards the training distribution using a diffusion model trained only on the source distribution. Theoretical analysis reveals the feasibility of our method. Experimental results, on both multiple-domain generalization datasets and single-domain generalization datasets, show that our method provides a general performance gain when plugged into a wide range of commonly used OoD algorithms. Our code is available at https://github.com/yu-rp/Distribution-Shift-Iverson https://github.com/yu-rp/Distribution-Shift-Iverson. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Distribution_Shift_Inversion_for_Out-of-Distribution_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Distribution_Shift_Inversion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Distribution_Shift_Inversion_for_Out-of-Distribution_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Distribution_Shift_Inversion_for_Out-of-Distribution_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Polarization Reconstruction With PDAVIS Events | Haiyang Mei, Zuowen Wang, Xin Yang, Xiaopeng Wei, Tobi Delbruck | The polarization event camera PDAVIS is a novel bio-inspired neuromorphic vision sensor that reports both conventional polarization frames and asynchronous, continuously per-pixel polarization brightness changes (polarization events) with fast temporal resolution and large dynamic range. A deep neural network method (Polarization FireNet) was previously developed to reconstruct the polarization angle and degree from polarization events for bridging the gap between the polarization event camera and mainstream computer vision. However, Polarization FireNet applies a network pre-trained for normal event-based frame reconstruction independently on each of four channels of polarization events from four linear polarization angles, which ignores the correlations between channels and inevitably introduces content inconsistency between the four reconstructed frames, resulting in unsatisfactory polarization reconstruction performance. In this work, we strive to train an effective, yet efficient, DNN model that directly outputs polarization from the input raw polarization events. To this end, we constructed the first large-scale event-to-polarization dataset, which we subsequently employed to train our events-to-polarization network E2P. E2P extracts rich polarization patterns from input polarization events and enhances features through cross-modality context integration. We demonstrate that E2P outperforms Polarization FireNet by a significant margin with no additional computing cost. Experimental results also show that E2P produces more accurate measurement of polarization than the PDAVIS frames in challenging fast and high dynamic range scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mei_Deep_Polarization_Reconstruction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Deep_Polarization_Reconstruction_With_PDAVIS_Events_CVPR_2023_paper.html | CVPR 2023 | null |
VideoTrack: Learning To Track Objects via Video Transformer | Fei Xie, Lei Chu, Jiahao Li, Yan Lu, Chao Ma | Existing Siamese tracking methods, which are built on pair-wise matching between two single frames, heavily rely on additional sophisticated mechanism to exploit temporal information among successive video frames, hindering them from high efficiency and industrial deployments. In this work, we resort to sequence-level target matching that can encode temporal contexts into the spatial features through a neat feedforward video model. Specifically, we adapt the standard video transformer architecture to visual tracking by enabling spatiotemporal feature learning directly from frame-level patch sequences. To better adapt to the tracking task, we carefully blend the spatiotemporal information in the video clips through sequential multi-branch triplet blocks, which formulates a video transformer backbone. Our experimental study compares different model variants, such as tokenization strategies, hierarchical structures, and video attention schemes. Then, we propose a disentangled dual-template mechanism that decouples static and dynamic appearance changes over time, and reduces the temporal redundancy in video frames. Extensive experiments show that our method, named as VideoTrack, achieves state-of-the-art results while running in real-time. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_VideoTrack_Learning_To_Track_Objects_via_Video_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_VideoTrack_Learning_To_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_VideoTrack_Learning_To_Track_Objects_via_Video_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_VideoTrack_Learning_To_Track_Objects_via_Video_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
System-Status-Aware Adaptive Network for Online Streaming Video Understanding | Lin Geng Foo, Jia Gong, Zhipeng Fan, Jun Liu | Recent years have witnessed great progress in deep neural networks for real-time applications. However, most existing works do not explicitly consider the general case where the device's state and the available resources fluctuate over time, and none of them investigate or address the impact of varying computational resources for online video understanding tasks. This paper proposes a System-status-aware Adaptive Network (SAN) that considers the device's real-time state to provide high-quality predictions with low delay. Usage of our agent's policy improves efficiency and robustness to fluctuations of the system status. On two widely used video understanding tasks, SAN obtains state-of-the-art performance while constantly keeping processing delays low. Moreover, training such an agent on various types of hardware configurations is not easy as the labeled training data might not be available, or can be computationally prohibitive. To address this challenging problem, we propose a Meta Self-supervised Adaptation (MSA) method that adapts the agent's policy to new hardware configurations at test-time, allowing for easy deployment of the model onto other unseen hardware platforms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Foo_System-Status-Aware_Adaptive_Network_for_Online_Streaming_Video_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Foo_System-Status-Aware_Adaptive_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15742 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Foo_System-Status-Aware_Adaptive_Network_for_Online_Streaming_Video_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Foo_System-Status-Aware_Adaptive_Network_for_Online_Streaming_Video_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Parallel Diffusion Models of Operator and Image for Blind Inverse Problems | Hyungjin Chung, Jeongsol Kim, Sehui Kim, Jong Chul Ye | Diffusion model-based inverse problem solvers have demonstrated state-of-the-art performance in cases where the forward operator is known (i.e. non-blind). However, the applicability of the method to blind inverse problems has yet to be explored. In this work, we show that we can indeed solve a family of blind inverse problems by constructing another diffusion prior for the forward operator. Specifically, parallel reverse diffusion guided by gradients from the intermediate stages enables joint optimization of both the forward operator parameters as well as the image, such that both are jointly estimated at the end of the parallel reverse diffusion procedure. We show the efficacy of our method on two representative tasks --- blind deblurring, and imaging through turbulence --- and show that our method yields state-of-the-art performance, while also being flexible to be applicable to general blind inverse problems when we know the functional forms. Code available: https://github.com/BlindDPS/blind-dps | https://openaccess.thecvf.com/content/CVPR2023/papers/Chung_Parallel_Diffusion_Models_of_Operator_and_Image_for_Blind_Inverse_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chung_Parallel_Diffusion_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10656 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chung_Parallel_Diffusion_Models_of_Operator_and_Image_for_Blind_Inverse_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chung_Parallel_Diffusion_Models_of_Operator_and_Image_for_Blind_Inverse_CVPR_2023_paper.html | CVPR 2023 | null |
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning | Hyesong Choi, Hunsang Lee, Wonil Song, Sangryul Jeon, Kwanghoon Sohn, Dongbo Min | Recent vision-based reinforcement learning (RL) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies. However, these methods focus on learning global representations of images, and disregard local spatial structures present in the consecutively stacked frames. In this paper, we propose a novel approach, termed self-supervised Paired Similarity Representation Learning (PSRL) for effectively encoding spatial structures in an unsupervised manner. Given the input frames, the latent volumes are first generated individually using an encoder, and they are used to capture the variance in terms of local spatial structures, i.e., correspondence maps among multiple frames. This enables for providing plenty of fine-grained samples for training the encoder of deep RL. We further attempt to learn the global semantic representations in the global prediction module that predicts future state representations using action vector as a medium. The proposed method imposes similarity constraints on the three latent volumes; transformed query representations by estimated pixel-wise correspondence, predicted query representations from the global prediction model, and target representations of future state, guiding global prediction with locality-inherent volume. Experimental results on complex tasks in Atari Games and DeepMind Control Suite demonstrate that the RL methods are significantly boosted by the proposed self-supervised learning of structured representations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Local-Guided_Global_Paired_Similarity_Representation_for_Visual_Reinforcement_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_Local-Guided_Global_Paired_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Local-Guided_Global_Paired_Similarity_Representation_for_Visual_Reinforcement_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Local-Guided_Global_Paired_Similarity_Representation_for_Visual_Reinforcement_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Semidefinite Relaxations for Robust Multiview Triangulation | Linus Härenstam-Nielsen, Niclas Zeller, Daniel Cremers | We propose an approach based on convex relaxations for certifiably optimal robust multiview triangulation. To this end, we extend existing relaxation approaches to non-robust multiview triangulation by incorporating a least squares cost function. We propose two formulations, one based on epipolar constraints and one based on fractional reprojection constraints. The first is lower dimensional and remains tight under moderate noise and outlier levels, while the second is higher dimensional and therefore slower but remains tight even under extreme noise and outlier levels. We demonstrate through extensive experiments that the proposed approaches allow us to compute provably optimal reconstructions even under significant noise and a large percentage of outliers. | https://openaccess.thecvf.com/content/CVPR2023/papers/Harenstam-Nielsen_Semidefinite_Relaxations_for_Robust_Multiview_Triangulation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Harenstam-Nielsen_Semidefinite_Relaxations_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Harenstam-Nielsen_Semidefinite_Relaxations_for_Robust_Multiview_Triangulation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Harenstam-Nielsen_Semidefinite_Relaxations_for_Robust_Multiview_Triangulation_CVPR_2023_paper.html | CVPR 2023 | null |
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation | Dahyun Kang, Piotr Koniusz, Minsu Cho, Naila Murray | We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions through separate task heads. Our model is able to effectively learn to perform classification and segmentation in the absence of pixel-level labels during training, using only image-level labels. To do this it uses attention maps, created from tokens generated by the self-supervised ViT backbone, as pixel-level pseudo-labels. We also explore a practical setup with "mixed" supervision, where a small number of training images contains ground-truth pixel-level labels and the remaining images have only image-level labels. For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels. Experiments on Pascal-5i and COCO-20i demonstrate significant performance gains in a variety of supervision settings, and in particular when little-to-no pixel-level labels are available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Distilling_Self-Supervised_Vision_Transformers_for_Weakly-Supervised_Few-Shot_Classification__Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Distilling_Self-Supervised_Vision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Distilling_Self-Supervised_Vision_Transformers_for_Weakly-Supervised_Few-Shot_Classification__Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Distilling_Self-Supervised_Vision_Transformers_for_Weakly-Supervised_Few-Shot_Classification__Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
FFCV: Accelerating Training by Removing Data Bottlenecks | Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Mądry | We present FFCV, a library for easy, fast, resource-efficient training of machine learning models. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process. In particular, we combine techniques such as an efficient file storage format, caching, data pre-loading, asynchronous data transfer, and just-in-time compilation to (a) make data loading and transfer significantly more efficient, ensuring that GPUs can reach full utilization; and (b) offload as much data processing as possible to the CPU asynchronously, freeing GPU up capacity for training. Using FFCV, we train ResNet-18 and ResNet-50 on the ImageNet dataset with a state-of-the-art tradeoff between accuracy and training time. For example, across the range of ResNet-50 models we test, we obtain the same accuracy as the best baselines in half the time. We demonstrate FFCV's performance, ease-of-use, extensibility, and ability to adapt to resource constraints through several case studies. | https://openaccess.thecvf.com/content/CVPR2023/papers/Leclerc_FFCV_Accelerating_Training_by_Removing_Data_Bottlenecks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Leclerc_FFCV_Accelerating_Training_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Leclerc_FFCV_Accelerating_Training_by_Removing_Data_Bottlenecks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Leclerc_FFCV_Accelerating_Training_by_Removing_Data_Bottlenecks_CVPR_2023_paper.html | CVPR 2023 | null |
Collaborative Noisy Label Cleaner: Learning Scene-Aware Trailers for Multi-Modal Highlight Detection in Movies | Bei Gan, Xiujun Shu, Ruizhi Qiao, Haoqian Wu, Keyu Chen, Hanjun Li, Bo Ren | Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations. (2) Besides previous supervised or unsupervised settings, some existing video corpora can be useful, e.g., trailers, but they are often noisy and incomplete to cover the full highlights. In this work, we study a more practical and promising setting, i.e., reformulating highlight detection as "learning with noisy labels". This setting does not require time-consuming manual annotations and can fully utilize existing abundant video corpora. First, based on movie trailers, we leverage scene segmentation to obtain complete shots, which are regarded as noisy labels. Then, we propose a Collaborative noisy Label Cleaner (CLC) framework to learn from noisy highlight moments. CLC consists of two modules: augmented cross-propagation (ACP) and multi-modality cleaning (MMC). The former aims to exploit the closely related audio-visual signals and fuse them to learn unified multi-modal representations. The latter aims to achieve cleaner highlight labels by observing the changes in losses among different modalities. To verify the effectiveness of CLC, we further collect a large-scale highlight dataset named MovieLights. Comprehensive experiments on MovieLights and YouTube Highlights datasets demonstrate the effectiveness of our approach. Code has been made available at: https://github.com/TencentYoutuResearch/HighlightDetection-CLC | https://openaccess.thecvf.com/content/CVPR2023/papers/Gan_Collaborative_Noisy_Label_Cleaner_Learning_Scene-Aware_Trailers_for_Multi-Modal_Highlight_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gan_Collaborative_Noisy_Label_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14768 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gan_Collaborative_Noisy_Label_Cleaner_Learning_Scene-Aware_Trailers_for_Multi-Modal_Highlight_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gan_Collaborative_Noisy_Label_Cleaner_Learning_Scene-Aware_Trailers_for_Multi-Modal_Highlight_CVPR_2023_paper.html | CVPR 2023 | null |
Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning | Heng Zhang, Daqing Liu, Qi Zheng, Bing Su | A meaningful video is semantically coherent and changes smoothly. However, most existing fine-grained video representation learning methods learn frame-wise features by aligning frames across videos or exploring relevance between multiple views, neglecting the inherent dynamic process of each video. In this paper, we propose to learn video representations by modeling Video as Stochastic Processes (VSP) via a novel process-based contrastive learning framework, which aims to discriminate between video processes and simultaneously capture the temporal dynamics in the processes. Specifically, we enforce the embeddings of the frame sequence of interest to approximate a goal-oriented stochastic process, i.e., Brownian bridge, in the latent space via a process-based contrastive loss. To construct the Brownian bridge, we adapt specialized sampling strategies under different annotations for both self-supervised and weakly-supervised learning. Experimental results on four datasets show that VSP stands as a state-of-the-art method for various video understanding tasks, including phase progression, phase classification and frame retrieval. Code is available at 'https://github.com/hengRUC/VSP'. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Modeling_Video_As_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-Real Novel View Synthesis via Contrastive Learning | Hao Yang, Lanqing Hong, Aoxue Li, Tianyang Hu, Zhenguo Li, Gim Hee Lee, Liwei Wang | Although many recent works have investigated generalizable NeRF-based novel view synthesis for unseen scenes, they seldom consider the synthetic-to-real generalization, which is desired in many practical applications. In this work, we first investigate the effects of synthetic data in synthetic-to-real novel view synthesis and surprisingly observe that models trained with synthetic data tend to produce sharper but less accurate volume densities. For pixels where the volume densities are correct, fine-grained details will be obtained. Otherwise, severe artifacts will be produced. To maintain the advantages of using synthetic data while avoiding its negative effects, we propose to introduce geometry-aware contrastive learning to learn multi-view consistent features with geometric constraints. Meanwhile, we adopt cross-view attention to further enhance the geometry perception of features by querying features across input views. Experiments demonstrate that under the synthetic-to-real setting, our method can render images with higher quality and better fine-grained details, outperforming existing generalizable novel view synthesis methods in terms of PSNR, SSIM, and LPIPS. When trained on real data, our method also achieves state-of-the-art results. https://haoy945.github.io/contranerf/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_ContraNeRF_Generalizable_Neural_Radiance_Fields_for_Synthetic-to-Real_Novel_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_ContraNeRF_Generalizable_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_ContraNeRF_Generalizable_Neural_Radiance_Fields_for_Synthetic-to-Real_Novel_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_ContraNeRF_Generalizable_Neural_Radiance_Fields_for_Synthetic-to-Real_Novel_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Region-Aware Pretraining for Open-Vocabulary Object Detection With Vision Transformers | Dahun Kim, Anelia Angelova, Weicheng Kuo | We present Region-aware Open-vocabulary Vision Transformers (RO-ViT) -- a contrastive image-text pretraining recipe to bridge the gap between image-level pretraining and open-vocabulary object detection. At the pretraining phase, we propose to randomly crop and resize regions of positional embeddings instead of using the whole image positional embeddings. This better matches the use of positional embeddings at region-level in the detection finetuning phase. In addition, we replace the common softmax cross entropy loss in contrastive learning with focal loss to better learn the informative yet difficult examples. Finally, we leverage recent advances in novel object proposals to improve open-vocabulary detection finetuning. We evaluate our full model on the LVIS and COCO open-vocabulary detection benchmarks and zero-shot transfer. RO-ViT achieves a state-of-the-art 32.1 APr on LVIS, surpassing the best existing approach by +5.8 points in addition to competitive zero-shot transfer detection. Surprisingly, RO-ViT improves the image-level representation as well and achieves the state of the art on 9 out of 12 metrics on COCO and Flickr image-text retrieval benchmarks, outperforming competitive approaches with larger models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Region-Aware_Pretraining_for_Open-Vocabulary_Object_Detection_With_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Region-Aware_Pretraining_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.07011 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Region-Aware_Pretraining_for_Open-Vocabulary_Object_Detection_With_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Region-Aware_Pretraining_for_Open-Vocabulary_Object_Detection_With_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
PaletteNeRF: Palette-Based Appearance Editing of Neural Radiance Fields | Zhengfei Kuang, Fujun Luan, Sai Bi, Zhixin Shu, Gordon Wetzstein, Kalyan Sunkavalli | Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the appearance of such representations can be efficiently edited while maintaining photorealism. In this work, we present PaletteNeRF, a novel method for photorealistic appearance editing of neural radiance fields (NeRF) based on 3D color decomposition. Our method decomposes the appearance of each 3D point into a linear combination of palette-based bases (i.e., 3D segmentations defined by a group of NeRF-type functions) that are shared across the scene. While our palette-based bases are view-independent, we also predict a view-dependent function to capture the color residual (e.g., specular shading). During training, we jointly optimize the basis functions and the color palettes, and we also introduce novel regularizers to encourage the spatial coherence of the decomposition. Our method allows users to efficiently edit the appearance of the 3D scene by modifying the color palettes. We also extend our framework with compressed semantic features for semantic-aware appearance editing. We demonstrate that our technique is superior to baseline methods both quantitatively and qualitatively for appearance editing of complex real-world scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kuang_PaletteNeRF_Palette-Based_Appearance_Editing_of_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kuang_PaletteNeRF_Palette-Based_Appearance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.10699 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kuang_PaletteNeRF_Palette-Based_Appearance_Editing_of_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kuang_PaletteNeRF_Palette-Based_Appearance_Editing_of_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Unsupervised Object Detection From LiDAR Point Clouds | Lunjun Zhang, Anqi Joyce Yang, Yuwen Xiong, Sergio Casas, Bin Yang, Mengye Ren, Raquel Urtasun | In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes. We present a simple yet effective method that exploits (i) point clustering in near-range areas where the point clouds are dense, (ii) temporal consistency to filter out noisy unsupervised detections, (iii) translation equivariance of CNNs to extend the auto-labels to long range, and (iv) self-supervision for improving on its own. Our approach, OYSTER (Object Discovery via Spatio-Temporal Refinement), does not impose constraints on data collection (such as repeated traversals of the same location), is able to detect objects in a zero-shot manner without supervised finetuning (even in sparse, distant regions), and continues to self-improve given more rounds of iterative self-training. To better measure model performance in self-driving scenarios, we propose a new planning-centric perception metric based on distance-to-collision. We demonstrate that our unsupervised object detector significantly outperforms unsupervised baselines on PandaSet and Argoverse 2 Sensor dataset, showing promise that self-supervision combined with object priors can enable object discovery in the wild. For more information, visit the project website: https://waabi.ai/research/oyster. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Towards_Unsupervised_Object_Detection_From_LiDAR_Point_Clouds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Towards_Unsupervised_Object_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Towards_Unsupervised_Object_Detection_From_LiDAR_Point_Clouds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Towards_Unsupervised_Object_Detection_From_LiDAR_Point_Clouds_CVPR_2023_paper.html | CVPR 2023 | null |
Contrastive Mean Teacher for Domain Adaptive Object Detectors | Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, Yu-Xiong Wang | Object detectors often suffer from the domain gap between training (source domain) and real-world applications (target domain). Mean-teacher self-training is a powerful paradigm in unsupervised domain adaptation for object detection, but it struggles with low-quality pseudo-labels. In this work, we identify the intriguing alignment and synergy between mean-teacher self-training and contrastive learning. Motivated by this, we propose Contrastive Mean Teacher (CMT) -- a unified, general-purpose framework with the two paradigms naturally integrated to maximize beneficial learning signals. Instead of using pseudo-labels solely for final predictions, our strategy extracts object-level features using pseudo-labels and optimizes them via contrastive learning, without requiring labels in the target domain. When combined with recent mean-teacher self-training methods, CMT leads to new state-of-the-art target-domain performance: 51.9% mAP on Foggy Cityscapes, outperforming the previously best by 2.1% mAP. Notably, CMT can stabilize performance and provide more significant gains as pseudo-label noise increases. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Contrastive_Mean_Teacher_for_Domain_Adaptive_Object_Detectors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Contrastive_Mean_Teacher_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.03034 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Contrastive_Mean_Teacher_for_Domain_Adaptive_Object_Detectors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Contrastive_Mean_Teacher_for_Domain_Adaptive_Object_Detectors_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Transferable Spatiotemporal Representations From Natural Script Knowledge | Ziyun Zeng, Yuying Ge, Xihui Liu, Bin Chen, Ping Luo, Shu-Tao Xia, Yixiao Ge | Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years. Despite some progress, existing methods are mostly limited to highly curated datasets (e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We argue that it is due to the fact that they only capture pixel-level knowledge rather than spatiotemporal semantics, which hinders further progress in video understanding. Inspired by the great success of image-text pre-training (e.g., CLIP), we take the first step to exploit language semantics to boost transferable spatiotemporal representation learning. We introduce a new pretext task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR scripts by attending to learned video representations. We do not rely on descriptive captions and learn purely from video, i.e., leveraging the natural transcribed speech knowledge to provide noisy but useful semantics over time. Our method enforces the vision model to contextualize what is happening over time so that it can re-organize the narrative transcripts, and can seamlessly apply to large-scale uncurated video data in the real world. Our method demonstrates strong out-of-the-box spatiotemporal representations on diverse benchmarks, e.g., +13.6% gains over VideoMAE on SSV2 via linear probing. The code is available at https://github.com/TencentARC/TVTS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_Learning_Transferable_Spatiotemporal_Representations_From_Natural_Script_Knowledge_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_Learning_Transferable_Spatiotemporal_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.15280 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Learning_Transferable_Spatiotemporal_Representations_From_Natural_Script_Knowledge_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Learning_Transferable_Spatiotemporal_Representations_From_Natural_Script_Knowledge_CVPR_2023_paper.html | CVPR 2023 | null |
NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects | Zhiwen Yan, Chen Li, Gim Hee Lee | Dynamic Neural Radiance Field (NeRF) is a powerful algorithm capable of rendering photo-realistic novel view images from a monocular RGB video of a dynamic scene. Although it warps moving points across frames from the observation spaces to a common canonical space for rendering, dynamic NeRF does not model the change of the reflected color during the warping. As a result, this approach often fails drastically on challenging specular objects in motion. We address this limitation by reformulating the neural radiance field function to be conditioned on surface position and orientation in the observation space. This allows the specular surface at different poses to keep the different reflected colors when mapped to the common canonical space. Additionally, we add the mask of moving objects to guide the deformation field. As the specular surface changes color during motion, the mask mitigates the problem of failure to find temporal correspondences with only RGB supervision. We evaluate our model based on the novel view synthesis quality with a self-collected dataset of different moving specular objects in realistic environments. The experimental results demonstrate that our method significantly improves the reconstruction quality of moving specular objects from monocular RGB videos compared to the existing NeRF models. Our code and data are available at the project website https://github.com/JokerYan/NeRF-DS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_NeRF-DS_Neural_Radiance_Fields_for_Dynamic_Specular_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_NeRF-DS_Neural_Radiance_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_NeRF-DS_Neural_Radiance_Fields_for_Dynamic_Specular_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_NeRF-DS_Neural_Radiance_Fields_for_Dynamic_Specular_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
M6Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis | Hiuyi Cheng, Peirong Zhang, Sihang Wu, Jiaxin Zhang, Qiyuan Zhu, Zecheng Xie, Jing Li, Kai Ding, Lianwen Jin | Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion. Most public datasets currently contain only PDF documents and lack realistic documents. Models trained on these datasets may not generalize well to real-world scenarios. Therefore, this paper introduces a large and diverse document layout analysis dataset called M^6-Doc. The M^6 designation represents six properties: (1) Multi-Format (including scanned, photographed, and PDF documents); (2) Multi-Type (such as scientific articles, textbooks, books, test papers, magazines, newspapers, and notes); (3) Multi-Layout (rectangular, Manhattan, non-Manhattan, and multi-column Manhattan); (4) Multi-Language (Chinese and English); (5) Multi-Annotation Category (74 types of annotation labels with 237,116 annotation instances in 9,080 manually annotated pages); and (6) Modern documents. Additionally, we propose a transformer-based document layout analysis method called TransDLANet, which leverages an adaptive element matching mechanism that enables query embedding to better match ground truth to improve recall, and constructs a segmentation branch for more precise document image instance segmentation. We conduct a comprehensive evaluation of M^6-Doc with various layout analysis methods and demonstrate its effectiveness. TransDLANet achieves state-of-the-art performance on M^6-Doc with 64.5% mAP. The M^6-Doc dataset will be available at https://github.com/HCIILAB/M6Doc. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_M6Doc_A_Large-Scale_Multi-Format_Multi-Type_Multi-Layout_Multi-Language_Multi-Annotation_Category_Dataset_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cheng_M6Doc_A_Large-Scale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_M6Doc_A_Large-Scale_Multi-Format_Multi-Type_Multi-Layout_Multi-Language_Multi-Annotation_Category_Dataset_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_M6Doc_A_Large-Scale_Multi-Format_Multi-Type_Multi-Layout_Multi-Language_Multi-Annotation_Category_Dataset_CVPR_2023_paper.html | CVPR 2023 | null |
RealFusion: 360deg Reconstruction of Any Object From a Single Image | Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Andrea Vedaldi | We consider the problem of reconstructing a full 360deg photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed. We thus take an off-the-self conditional image generator based on diffusion and engineer a prompt that encourages it to "dream up" novel views of the object. Using the recent DreamFusion method, we fuse the given input view, the conditional prior, and other regularizers in a final, consistent reconstruction. We demonstrate state-of-the-art reconstruction results on benchmark images when compared to prior methods for monocular 3D reconstruction of objects. Qualitatively, our reconstructions provide a faithful match of the input view and a plausible extrapolation of its appearance and 3D shape, including to the side of the object not visible in the image. | https://openaccess.thecvf.com/content/CVPR2023/papers/Melas-Kyriazi_RealFusion_360deg_Reconstruction_of_Any_Object_From_a_Single_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Melas-Kyriazi_RealFusion_360deg_Reconstruction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Melas-Kyriazi_RealFusion_360deg_Reconstruction_of_Any_Object_From_a_Single_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Melas-Kyriazi_RealFusion_360deg_Reconstruction_of_Any_Object_From_a_Single_Image_CVPR_2023_paper.html | CVPR 2023 | null |
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning | Yiting Cheng, Fangyun Wei, Jianmin Bao, Dong Chen, Wenqiang Zhang | This work focuses on sign language retrieval--a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) retrieval and sign-video-to-text (V2T) retrieval. Different from traditional video-text retrieval, sign language videos, not only contain visual signals but also carry abundant semantic meanings by themselves due to the fact that sign languages are also natural languages. Considering this character, we formulate sign language retrieval as a cross-lingual retrieval problem as well as a video-text retrieval task. Concretely, we take into account the linguistic properties of both sign languages and natural languages, and simultaneously identify the fine-grained cross-lingual (i.e., sign-to-word) mappings while contrasting the texts and the sign videos in a joint embedding space. This process is termed as cross-lingual contrastive learning. Another challenge is raised by the data scarcity issue--sign language datasets are orders of magnitude smaller in scale than that of speech recognition. We alleviate this issue by adopting a domain-agnostic sign encoder pre-trained on large-scale sign videos into the target domain via pseudo-labeling. Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e.g., +22.4 T2V and +28.0 V2T R@1 improvements on How2Sign dataset, and +13.7 T2V and +17.1 V2T R@1 improvements on PHOENIX-2014T dataset. Code and models are available at: https://github.com/FangyunWei/SLRT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bao_CiCo_Domain-Aware_Sign_Language_Retrieval_via_Cross-Lingual_Contrastive_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bao_CiCo_Domain-Aware_Sign_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12793 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_CiCo_Domain-Aware_Sign_Language_Retrieval_via_Cross-Lingual_Contrastive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_CiCo_Domain-Aware_Sign_Language_Retrieval_via_Cross-Lingual_Contrastive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Relational Space-Time Query in Long-Form Videos | Xitong Yang, Fu-Jen Chu, Matt Feiszli, Raghav Goyal, Lorenzo Torresani, Du Tran | Egocentric videos are often available in the form of uninterrupted, uncurated long videos capturing the camera wearers' daily life activities.Understanding these videos requires models to be able to reason about activities, objects, and their interactions. However, current video benchmarks study these problems independently and under short, curated clips. In contrast, real-world applications, e.g., AR assistants, require bundling these problems for both model development and evaluation. In this paper, we propose to study these problems in a joint framework for long video understanding. Our contributions are three-fold. First, we propose an integrated framework, namely Relational Space-Time Query (ReST), for evaluating video understanding models via templated spatiotemporal queries. Second, we introduce two new benchmarks, ReST-ADL and ReST-Ego4D, which augment the existing egocentric video datasets with abundant query annotations generated by the ReST framework. Finally, we present a set of baselines and in-depth analysis on the two benchmarks and provide insights about the query tasks. We view our integrated framework and benchmarks as a step towards comprehensive, multi-step reasoning in long videos, and believe it will facilitate the development of next generations of video understanding models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Relational_Space-Time_Query_in_Long-Form_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Relational_Space-Time_Query_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Relational_Space-Time_Query_in_Long-Form_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Relational_Space-Time_Query_in_Long-Form_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
LargeKernel3D: Scaling Up Kernels in 3D Sparse CNNs | Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia | Recent advance in 2D CNNs has revealed that large kernels are important. However, when directly applying large convolutional kernels in 3D CNNs, severe difficulties are met, where those successful module designs in 2D become surprisingly ineffective on 3D networks, including the popular depth-wise convolution. To address this vital challenge, we instead propose the spatial-wise partition convolution and its large-kernel module. As a result, it avoids the optimization and efficiency issues of naive 3D large kernels. Our large-kernel 3D CNN network, LargeKernel3D, yields notable improvement in 3D tasks of semantic segmentation and object detection. It achieves 73.9% mIoU on the ScanNetv2 semantic segmentation and 72.8% NDS nuScenes object detection benchmarks, ranking 1st on the nuScenes LIDAR leaderboard. The performance further boosts to 74.2% NDS with a simple multi-modal fusion. In addition, LargeKernel3D can be scaled to 17x17x17 kernel size on Waymo 3D object detection. For the first time, we show that large kernels are feasible and essential for 3D visual tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_LargeKernel3D_Scaling_Up_Kernels_in_3D_Sparse_CNNs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_LargeKernel3D_Scaling_Up_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.10555 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_LargeKernel3D_Scaling_Up_Kernels_in_3D_Sparse_CNNs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_LargeKernel3D_Scaling_Up_Kernels_in_3D_Sparse_CNNs_CVPR_2023_paper.html | CVPR 2023 | null |
Video Dehazing via a Multi-Range Temporal Alignment Network With Physical Prior | Jiaqi Xu, Xiaowei Hu, Lei Zhu, Qi Dou, Jifeng Dai, Yu Qiao, Pheng-Ann Heng | Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a multi-range scene radiance recovery module to capture space-time dependencies in multiple space-time ranges, which helps to effectively aggregate temporal information from adjacent frames. Moreover, we construct the first large-scale outdoor video dehazing benchmark dataset, which contains videos in various real-world scenarios. Experimental results on both synthetic and real conditions show the superiority of our proposed method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Video_Dehazing_via_a_Multi-Range_Temporal_Alignment_Network_With_Physical_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Video_Dehazing_via_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09757 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Video_Dehazing_via_a_Multi-Range_Temporal_Alignment_Network_With_Physical_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Video_Dehazing_via_a_Multi-Range_Temporal_Alignment_Network_With_Physical_CVPR_2023_paper.html | CVPR 2023 | null |
3D Concept Learning and Reasoning From Multi-View Images | Yining Hong, Chunru Lin, Yilun Du, Zhenfang Chen, Joshua B. Tenenbaum, Chuang Gan | Humans are able to accurately reason in 3D by gathering multi-view observations of the surrounding world. Inspired by this insight, we introduce a new large-scale benchmark for 3D multi-view visual question answering (3DMV-VQA). This dataset is collected by an embodied agent actively moving and capturing RGB images in an environment using the Habitat simulator. In total, it consists of approximately 5k scenes, 600k images, paired with 50k questions. We evaluate various state-of-the-art models for visual reasoning on our benchmark and find that they all perform poorly. We suggest that a principled approach for 3D reasoning from multi-view images should be to infer a compact 3D representation of the world from the multi-view images, which is further grounded on open-vocabulary semantic concepts, and then to execute reasoning on these 3D representations. As the first step towards this approach, we propose a novel 3D concept learning and reasoning (3D-CLR) framework that seamlessly combines these components via neural fields, 2D pre-trained vision-language models, and neural reasoning operators. Experimental results suggest that our framework outperforms baseline models by a large margin, but the challenge remains largely unsolved. We further perform an in-depth analysis of the challenges and highlight potential future directions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hong_3D_Concept_Learning_and_Reasoning_From_Multi-View_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hong_3D_Concept_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11327 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hong_3D_Concept_Learning_and_Reasoning_From_Multi-View_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hong_3D_Concept_Learning_and_Reasoning_From_Multi-View_Images_CVPR_2023_paper.html | CVPR 2023 | null |
BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation | Junheum Park, Jintae Kim, Chang-Su Kim | A novel 4K video frame interpolator based on bilateral transformer (BiFormer) is proposed in this paper, which performs three steps: global motion estimation, local motion refinement, and frame synthesis. First, in global motion estimation, we predict symmetric bilateral motion fields at a coarse scale. To this end, we propose BiFormer, the first transformer-based bilateral motion estimator. Second, we refine the global motion fields efficiently using blockwise bilateral cost volumes (BBCVs). Third, we warp the input frames using the refined motion fields and blend them to synthesize an intermediate frame. Extensive experiments demonstrate that the proposed BiFormer algorithm achieves excellent interpolation performance on 4K datasets. The source codes are available at https://github.com/JunHeum/BiFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_BiFormer_Learning_Bilateral_Motion_Estimation_via_Bilateral_Transformer_for_4K_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_BiFormer_Learning_Bilateral_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.02225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_BiFormer_Learning_Bilateral_Motion_Estimation_via_Bilateral_Transformer_for_4K_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_BiFormer_Learning_Bilateral_Motion_Estimation_via_Bilateral_Transformer_for_4K_CVPR_2023_paper.html | CVPR 2023 | null |
Integrally Pre-Trained Transformer Pyramid Networks | Yunjie Tian, Lingxi Xie, Zhaozhi Wang, Longhui Wei, Xiaopeng Zhang, Jianbin Jiao, Yaowei Wang, Qi Tian, Qixiang Ye | In this paper, we present an integral pre-training framework based on masked image modeling (MIM). We advocate for pre-training the backbone and neck jointly so that the transfer gap between MIM and downstream recognition tasks is minimal. We make two technical contributions. First, we unify the reconstruction and recognition necks by inserting a feature pyramid into the pre-training stage. Second, we complement mask image modeling (MIM) with masked feature modeling (MFM) that offers multi-stage supervision to the feature pyramid. The pre-trained models, termed integrally pre-trained transformer pyramid networks (iTPNs), serve as powerful foundation models for visual recognition. In particular, the base/large-level iTPN achieves an 86.2%/87.8% top-1 accuracy on ImageNet-1K, a 53.2%/55.6% box AP on COCO object detection with 1x training schedule using Mask-RCNN, and a 54.7%/57.7% mIoU on ADE20K semantic segmentation using UPerHead -- all these results set new records. Our work inspires the community to work on unifying upstream pre-training and downstream fine-tuning tasks. Code is available at https://github.com/sunsmarterjie/iTPN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_Integrally_Pre-Trained_Transformer_Pyramid_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_Integrally_Pre-Trained_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12735 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Integrally_Pre-Trained_Transformer_Pyramid_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Integrally_Pre-Trained_Transformer_Pyramid_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Soft Augmentation for Image Classification | Yang Liu, Shen Yan, Laura Leal-Taixé, James Hays, Deva Ramanan | Modern neural networks are over-parameterized and thus rely on strong regularization such as data augmentation and weight decay to reduce overfitting and improve generalization. The dominant form of data augmentation applies invariant transforms, where the learning target of a sample is invariant to the transform applied to that sample. We draw inspiration from human visual classification studies and propose generalizing augmentation with invariant transforms to soft augmentation where the learning target softens non-linearly as a function of the degree of the transform applied to the sample: e.g., more aggressive image crop augmentations produce less confident learning targets. We demonstrate that soft targets allow for more aggressive data augmentation, offer more robust performance boosts, work with other augmentation policies, and interestingly, produce better calibrated models (since they are trained to be less confident on aggressively cropped/occluded examples). Combined with existing aggressive augmentation strategies, soft targets 1) double the top-1 accuracy boost across Cifar-10, Cifar-100, ImageNet-1K, and ImageNet-V2, 2) improve model occlusion performance by up to 4x, and 3) half the expected calibration error (ECE). Finally, we show that soft augmentation generalizes to self-supervised classification tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Soft_Augmentation_for_Image_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Soft_Augmentation_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Soft_Augmentation_for_Image_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Soft_Augmentation_for_Image_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Learning From Unique Perspectives: User-Aware Saliency Modeling | Shi Chen, Nachiappan Valliappan, Shaolei Shen, Xinyu Ye, Kai Kohlhoff, Junfeng He | Everyone is unique. Given the same visual stimuli, people's attention is driven by both salient visual cues and their own inherent preferences. Knowledge of visual preferences not only facilitates understanding of fine-grained attention patterns of diverse users, but also has the potential of benefiting the development of customized applications. Nevertheless, existing saliency models typically limit their scope to attention as it applies to the general population and ignore the variability between users' behaviors. In this paper, we identify the critical roles of visual preferences in attention modeling, and for the first time study the problem of user-aware saliency modeling. Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model; (2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner; and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences. Experimental results on diverse stimuli, including naturalistic images and web pages, demonstrate the advantages of our method in capturing the distinct visual behaviors of different users and the general saliency of visual stimuli. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Learning_From_Unique_Perspectives_User-Aware_Saliency_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Learning_From_Unique_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_From_Unique_Perspectives_User-Aware_Saliency_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Learning_From_Unique_Perspectives_User-Aware_Saliency_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
PREIM3D: 3D Consistent Precise Image Attribute Editing From a Single Image | Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu, Zhengyi Wang, Zihao Xiao, Kaiwen Zheng, Jun Zhu | We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator's latent space or by per-image latent code optimization and then edited images in the latent space. Despite their promising results near the input view, they still suffer from the 3D inconsistency of produced images at large camera poses and imprecise image attribute editing, like affecting unspecified attributes during editing. For more efficient image inversion, we train a shared encoder for all images. To alleviate 3D inconsistency at large camera poses, we propose two novel methods, an alternating training scheme and a multi-view identity loss, to maintain 3D consistency and subject identity. As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images. We compare the latent space and inversion manifold of GAN models and demonstrate that editing in the inversion manifold can achieve better results in both quantitative and qualitative evaluations. Extensive experiments show that our method produces more 3D consistent images and achieves more precise image editing than previous work. Source code and pretrained models can be found on our project page: https://mybabyyh.github.io/Preim3D. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_PREIM3D_3D_Consistent_Precise_Image_Attribute_Editing_From_a_Single_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_PREIM3D_3D_Consistent_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.10263 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_PREIM3D_3D_Consistent_Precise_Image_Attribute_Editing_From_a_Single_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_PREIM3D_3D_Consistent_Precise_Image_Attribute_Editing_From_a_Single_CVPR_2023_paper.html | CVPR 2023 | null |
MaskSketch: Unpaired Structure-Guided Masked Image Generation | Dina Bashkirova, José Lezama, Kihyuk Sohn, Kate Saenko, Irfan Essa | Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level of control over the generation result. In this paper, we introduce MaskSketch, an image generation method that allows spatial conditioning of the generation result using a guiding sketch as an extra conditioning signal during sampling. MaskSketch utilizes a pre-trained masked generative transformer, requiring no model training or paired supervision, and works with input sketches of different levels of abstraction. We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation. Our results show that MaskSketch achieves high image realism and fidelity to the guiding structure. Evaluated on standard benchmark datasets, MaskSketch outperforms state-of-the-art methods for sketch-to-image translation, as well as unpaired image-to-image translation approaches. The code can be found on our project website: https://masksketch.github.io/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Bashkirova_MaskSketch_Unpaired_Structure-Guided_Masked_Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bashkirova_MaskSketch_Unpaired_Structure-Guided_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.05496 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bashkirova_MaskSketch_Unpaired_Structure-Guided_Masked_Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bashkirova_MaskSketch_Unpaired_Structure-Guided_Masked_Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Vocabulary Point-Cloud Object Detection Without 3D Annotation | Yuheng Lu, Chenfeng Xu, Xiaobao Wei, Xiaodong Xie, Masayoshi Tomizuka, Kurt Keutzer, Shanghang Zhang | The goal of open-vocabulary detection is to identify novel objects based on arbitrary textual descriptions. In this paper, we address open-vocabulary 3D point-cloud detection by a dividing-and-conquering strategy, which involves: 1) developing a point-cloud detector that can learn a general representation for localizing various objects, and 2) connecting textual and point-cloud representations to enable the detector to classify novel object categories based on text prompting. Specifically, we resort to rich image pre-trained models, by which the point-cloud detector learns localizing objects under the supervision of predicted 2D bounding boxes from 2D pre-trained detectors. Moreover, we propose a novel de-biased triplet cross-modal contrastive learning to connect the modalities of image, point-cloud and text, thereby enabling the point-cloud detector to benefit from vision-language pre-trained models, i.e., CLIP. The novel use of image and vision-language pre-trained models for point-cloud detectors allows for open-vocabulary 3D object detection without the need for 3D annotations. Experiments demonstrate that the proposed method improves at least 3.03 points and 7.47 points over a wide range of baselines on the ScanNet and SUN RGB-D datasets, respectively. Furthermore, we provide a comprehensive analysis to explain why our approach works. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Open-Vocabulary_Point-Cloud_Object_Detection_Without_3D_Annotation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.00788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Open-Vocabulary_Point-Cloud_Object_Detection_Without_3D_Annotation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Open-Vocabulary_Point-Cloud_Object_Detection_Without_3D_Annotation_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity | Dongping Liao, Xitong Gao, Yiren Zhao, Cheng-Zhong Xu | Owing to the non-i.i.d. nature of client data, channel neurons in federated-learned models may specialize to distinct features for different clients. Yet, existing channel-sparse federated learning (FL) algorithms prescribe fixed sparsity strategies for client models, and may thus prevent clients from training channel neurons collaboratively. To minimize the impact of sparsity on FL convergence, we propose Flado to improve the alignment of client model update trajectories by tailoring the sparsities of individual neurons in each client. Empirical results show that while other sparse methods are surprisingly impactful to convergence, Flado can not only attain the highest task accuracies with unlimited budget across a range of datasets, but also significantly reduce the amount of FLOPs required for training more than by 10x under the same communications budget, and push the Pareto frontier of communication/computation trade-off notably further than competing FL algorithms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liao_Adaptive_Channel_Sparsity_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.html | CVPR 2023 | null |
Detecting Backdoors in Pre-Trained Encoders | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Detecting_Backdoors_in_Pre-Trained_Encoders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Detecting_Backdoors_in_Pre-Trained_Encoders_CVPR_2023_paper.html | CVPR 2023 | null |
Sequential Training of GANs Against GAN-Classifiers Reveals Correlated "Knowledge Gaps" Present Among Independently Trained GAN Instances | Arkanath Pathak, Nicholas Dufour | Modern Generative Adversarial Networks (GANs) generate realistic images remarkably well. Previous work has demonstrated the feasibility of "GAN-classifiers" that are distinct from the co-trained discriminator, and operate on images generated from a frozen GAN. That such classifiers work at all affirms the existence of "knowledge gaps" (out-of-distribution artifacts across samples) present in GAN training. We iteratively train GAN-classifiers and train GANs that "fool" the classifiers (in an attempt to fill the knowledge gaps), and examine the effect on GAN training dynamics, output quality, and GAN-classifier generalization. We investigate two settings, a small DCGAN architecture trained on low dimensional images (MNIST), and StyleGAN2, a SOTA GAN architecture trained on high dimensional images (FFHQ). We find that the DCGAN is unable to effectively fool a held-out GAN-classifier without compromising the output quality. However, StyleGAN2 can fool held-out classifiers with no change in output quality, and this effect persists over multiple rounds of GAN/classifier training which appears to reveal an ordering over optima in the generator parameter space. Finally, we study different classifier architectures and show that the architecture of the GAN-classifier has a strong influence on the set of its learned artifacts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pathak_Sequential_Training_of_GANs_Against_GAN-Classifiers_Reveals_Correlated_Knowledge_Gaps_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pathak_Sequential_Training_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15533 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pathak_Sequential_Training_of_GANs_Against_GAN-Classifiers_Reveals_Correlated_Knowledge_Gaps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pathak_Sequential_Training_of_GANs_Against_GAN-Classifiers_Reveals_Correlated_Knowledge_Gaps_CVPR_2023_paper.html | CVPR 2023 | null |
Lookahead Diffusion Probabilistic Models for Refining Mean Estimation | Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn | We propose lookahead diffusion probabilistic models (LA-DPMs) to exploit the correlation in the outputs of the deep neural networks (DNNs) over subsequent timesteps in diffusion probabilistic models (DPMs) to refine the mean estimation of the conditional Gaussian distributions in the backward process. A typical DPM first obtains an estimate of the original data sample x by feeding the most recent state z_i and index i into the DNN model and then computes the mean vector of the conditional Gaussian distribution for z_ i-1 . We propose to calculate a more accurate estimate for x by performing extrapolation on the two estimates of x that are obtained by feeding (z_ i+1 , i+1) and (z_i, i) into the DNN model. The extrapolation can be easily integrated into the backward process of existing DPMs by introducing an additional connection over two consecutive timesteps, and fine-tuning is not required. Extensive experiments showed that plugging in the additional connection into DDPM, DDIM, DEIS, S-PNDM, and high-order DPM-Solvers leads to a significant performance gain in terms of Frechet inception distance (FID) score. Our implementation is available at https://github.com/guoqiangzhang-x/LA-DPM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Lookahead_Diffusion_Probabilistic_Models_for_Refining_Mean_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Lookahead_Diffusion_Probabilistic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Lookahead_Diffusion_Probabilistic_Models_for_Refining_Mean_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Lookahead_Diffusion_Probabilistic_Models_for_Refining_Mean_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
TensoIR: Tensorial Inverse Rendering | Haian Jin, Isabella Liu, Peijia Xu, Xiaoshuai Zhang, Songfang Han, Sai Bi, Xiaowei Zhou, Zexiang Xu, Hao Su | We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus suffering from low capacity and high computation costs, we extend TensoRF, a state-of-the-art approach for radiance field modeling, to estimate scene geometry, surface reflectance, and environment illumination from multi-view images captured under unknown lighting conditions. Our approach jointly achieves radiance field reconstruction and physically-based model estimation, leading to photo-realistic novel view synthesis and relighting. Benefiting from the efficiency and extensibility of the TensoRF-based representation, our method can accurately model secondary shading effects (like shadows and indirect lighting) and generally support input images captured under a single or multiple unknown lighting conditions. The low-rank tensor representation allows us to not only achieve fast and compact reconstruction but also better exploit shared information under an arbitrary number of capturing lighting conditions. We demonstrate the superiority of our method to baseline methods qualitatively and quantitatively on various challenging synthetic and real-world scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_TensoIR_Tensorial_Inverse_Rendering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_TensoIR_Tensorial_Inverse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12461 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_TensoIR_Tensorial_Inverse_Rendering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_TensoIR_Tensorial_Inverse_Rendering_CVPR_2023_paper.html | CVPR 2023 | null |
NIPQ: Noise Proxy-Based Integrated Pseudo-Quantization | Juncheol Shin, Junhyuk So, Sein Park, Seungyeop Kang, Sungjoo Yoo, Eunhyeok Park | Straight-through estimator (STE), which enables the gradient flow over the non-differentiable function via approximation, has been favored in studies related to quantization-aware training (QAT). However, STE incurs unstable convergence during QAT, resulting in notable quality degradation in low-precision representation. Recently, pseudo-quantization training has been proposed as an alternative approach to updating the learnable parameters using the pseudo-quantization noise instead of STE. In this study, we propose a novel noise proxy-based integrated pseudo-quantization (NIPQ) that enables unified support of pseudo-quantization for both activation and weight with minimal error by integrating the idea of truncation on the pseudo-quantization framework. NIPQ updates all of the quantization parameters (e.g., bit-width and truncation boundary) as well as the network parameters via gradient descent without STE instability, resulting in greatly-simplified but reliable precision allocation without human intervention. Our extensive experiments show that NIPQ outperforms existing quantization algorithms in various vision and language applications by a large margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shin_NIPQ_Noise_Proxy-Based_Integrated_Pseudo-Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shin_NIPQ_Noise_Proxy-Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_NIPQ_Noise_Proxy-Based_Integrated_Pseudo-Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_NIPQ_Noise_Proxy-Based_Integrated_Pseudo-Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Primitive Generation and Semantic-Related Alignment for Universal Zero-Shot Segmentation | Shuting He, Henghui Ding, Wei Jiang | We study universal zero-shot segmentation in this work to achieve panoptic, instance, and semantic segmentation for novel categories without any training samples. Such zero-shot segmentation ability relies on inter-class relationships in semantic space to transfer the visual knowledge learned from seen categories to unseen ones. Thus, it is desired to well bridge semantic-visual spaces and apply the semantic relationships to visual feature learning. We introduce a generative model to synthesize features for unseen categories, which links semantic and visual spaces as well as address the issue of lack of unseen training data. Furthermore, to mitigate the domain gap between semantic and visual spaces, firstly, we enhance the vanilla generator with learned primitives, each of which contains fine-grained attributes related to categories, and synthesize unseen features by selectively assembling these primitives. Secondly, we propose to disentangle the visual feature into the semantic-related part and the semantic-unrelated part that contains useful visual classification clues but is less relevant to semantic representation. The inter-class relationships of semantic-related visual features are then required to be aligned with those in semantic space, thereby transferring semantic knowledge to visual feature learning. The proposed approach achieves impressively state-of-the-art performance on zero-shot panoptic segmentation, instance segmentation, and semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Primitive_Generation_and_Semantic-Related_Alignment_for_Universal_Zero-Shot_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Primitive_Generation_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Primitive_Generation_and_Semantic-Related_Alignment_for_Universal_Zero-Shot_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Primitive_Generation_and_Semantic-Related_Alignment_for_Universal_Zero-Shot_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Long Range Pooling for 3D Large-Scale Scene Understanding | Xiang-Li Li, Meng-Hao Guo, Tai-Jiang Mu, Ralph R. Martin, Shi-Min Hu | Inspired by the success of recent vision transformers and large kernel design in convolutional neural networks (CNNs), in this paper, we analyze and explore essential reasons for their success. We claim two factors that are critical for 3D large-scale scene understanding: a larger receptive field and operations with greater non-linearity. The former is responsible for providing long range contexts and the latter can enhance the capacity of the network. To achieve the above properties, we propose a simple yet effective long range pooling (LRP) module using dilation max pooling, which provides a network with a large adaptive receptive field. LRP has few parameters, and can be readily added to current CNNs. Also, based on LRP, we present an entire network architecture, LRPNet, for 3D understanding. Ablation studies are presented to support our claims, and show that the LRP module achieves better results than large kernel convolution yet with reduced computation, due to its non-linearity. We also demonstrate the superiority of LRPNet on various benchmarks: LRPNet performs the best on ScanNet and surpasses other CNN-based methods on S3DIS and Matterport3D. Code will be avalible at https://github.com/li-xl/LRPNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Long_Range_Pooling_for_3D_Large-Scale_Scene_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Long_Range_Pooling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.06962 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Long_Range_Pooling_for_3D_Large-Scale_Scene_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Long_Range_Pooling_for_3D_Large-Scale_Scene_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Object-Goal Visual Navigation via Effective Exploration of Relations Among Historical Navigation States | Heming Du, Lincheng Li, Zi Huang, Xin Yu | Object-goal visual navigation aims at steering an agent toward an object via a series of moving steps. Previous works mainly focus on learning informative visual representations for navigation, but overlook the impacts of navigation states on the effectiveness and efficiency of navigation. We observe that high relevance among navigation states will cause navigation inefficiency or failure for existing methods. In this paper, we present a History-inspired Navigation Policy Learning (HiNL) framework to estimate navigation states effectively by exploring relationships among historical navigation states. In HiNL, we propose a History-aware State Estimation (HaSE) module to alleviate the impacts of dominant historical states on the current state estimation. Meanwhile, HaSE also encourages an agent to be alert to the current observation changes, thus enabling the agent to make valid actions. Furthermore, we design a History-based State Regularization (HbSR) to explicitly suppress the correlation among navigation states in training. As a result, our agent can update states more effectively while reducing the correlations among navigation states. Experiments on the artificial platform AI2-THOR (i.e.,, iTHOR and RoboTHOR) demonstrate that HiNL significantly outperforms state-of-the-art methods on both Success Rate and SPL in unseen testing environments. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Object-Goal_Visual_Navigation_via_Effective_Exploration_of_Relations_Among_Historical_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Object-Goal_Visual_Navigation_via_Effective_Exploration_of_Relations_Among_Historical_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Object-Goal_Visual_Navigation_via_Effective_Exploration_of_Relations_Among_Historical_CVPR_2023_paper.html | CVPR 2023 | null |
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction | Xiang Li, Xuelin Qian, Litian Liang, Lingjie Kong, Qiaole Dong, Jiejun Chen, Dingxia Liu, Xiuzhong Yao, Yanwei Fu | Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvious image patterns of reflecting the OS. To this end, we propose a causal inference system by leveraging the intraoperative attributes and the correlation among them, as an intermediate supervision to bridge the gap between the images and the final OS. Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction. We present a novel Causally-aware Intraoperative Imputation Model (CAWIM) that can sequentially predict each attribute using its parent nodes in the estimated causal graph. To determine the causal directions, we propose a splitting-voting mechanism, which votes for the direction for each pair of adjacent nodes among multiple predictions obtained via causal discovery from heterogeneity. The practicability and effectiveness of our method are demonstrated by the promising result on liver cancer dataset of 361 patients with long-term observations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Causally-Aware_Intraoperative_Imputation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Probabilistic Knowledge Distillation of Face Ensembles | Jianqing Xu, Shen Li, Ailin Deng, Miao Xiong, Jiaying Wu, Jiaxiang Wu, Shouhong Ding, Bryan Hooi | Mean ensemble (i.e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model. We formalize it as feature alignment for ensemble in open-set face recognition and generalize it into Bayesian Ensemble Averaging (BEA) through the lens of probabilistic modeling. This generalization brings up two practical benefits that existing methods could not provide: (1) the uncertainty of a face image can be evaluated and further decomposed into aleatoric uncertainty and epistemic uncertainty, the latter of which can be used as a measure for out-of-distribution detection of faceness; (2) a BEA statistic provably reflects the aleatoric uncertainty of a face image, acting as a measure for face image quality to improve recognition performance. To inherit the uncertainty estimation capability from BEA without the loss of inference efficiency, we propose BEA-KD, a student model to distill knowledge from BEA. BEA-KD mimics the overall behavior of ensemble members and consistently outperforms SOTA knowledge distillation methods on various challenging benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Probabilistic_Knowledge_Distillation_of_Face_Ensembles_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Probabilistic_Knowledge_Distillation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Probabilistic_Knowledge_Distillation_of_Face_Ensembles_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Probabilistic_Knowledge_Distillation_of_Face_Ensembles_CVPR_2023_paper.html | CVPR 2023 | null |
Twin Contrastive Learning With Noisy Labels | Zhizhong Huang, Junping Zhang, Hongming Shan | Learning from noisy data is a challenging task that significantly degenerates the model performance. In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification. Specifically, we construct a Gaussian mixture model (GMM) over the representations by injecting the supervised model predictions into GMM to link label-free latent variables in GMM with label-noisy annotations. Then, TCL detects the examples with wrong labels as the out-of-distribution examples by another two-component GMM, taking into account the data distribution. We further propose a cross-supervision with an entropy regularization loss that bootstraps the true targets from model predictions to handle the noisy labels. As a result, TCL can learn discriminative representations aligned with estimated labels through mixup and contrastive learning. Extensive experimental results on several standard benchmarks and real-world datasets demonstrate the superior performance of TCL. In particular, TCL achieves 7.5% improvements on CIFAR-10 with 90% noisy label---an extremely noisy scenario. The source code is available at https://github.com/Hzzone/TCL. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Twin_Contrastive_Learning_With_Noisy_Labels_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Twin_Contrastive_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06930 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Twin_Contrastive_Learning_With_Noisy_Labels_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Twin_Contrastive_Learning_With_Noisy_Labels_CVPR_2023_paper.html | CVPR 2023 | null |
TriVol: Point Cloud Rendering via Triple Volumes | Tao Hu, Xiaogang Xu, Ruihang Chu, Jiaya Jia | Existing learning-based methods for point cloud rendering adopt various 3D representations and feature querying mechanisms to alleviate the sparsity problem of point clouds. However, artifacts still appear in the rendered images, due to the challenges in extracting continuous and discriminative 3D features from point clouds. In this paper, we present a dense while lightweight 3D representation, named TriVol, that can be combined with NeRF to render photo-realistic images from point clouds. Our TriVol consists of triple slim volumes, each of which is encoded from the input point cloud. Our representation has two advantages. First, it fuses the respective fields at different scales and thus extracts local and non-local features for discriminative representation. Second, since the volume size is greatly reduced, our 3D decoder can be efficiently inferred, allowing us to increase the resolution of the 3D space to render more point details. Extensive experiments on different benchmarks with varying kinds of scenes/objects demonstrate our framework's effectiveness compared with current approaches. Moreover, our framework has excellent generalization ability to render a category of scenes or objects without fine-tuning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_TriVol_Point_Cloud_Rendering_via_Triple_Volumes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_TriVol_Point_Cloud_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16485 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_TriVol_Point_Cloud_Rendering_via_Triple_Volumes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_TriVol_Point_Cloud_Rendering_via_Triple_Volumes_CVPR_2023_paper.html | CVPR 2023 | null |
(ML)$^2$P-Encoder: On Exploration of Channel-Class Correlation for Multi-Label Zero-Shot Learning | Ziming Liu, Song Guo, Xiaocheng Lu, Jingcai Guo, Jiewei Zhang, Yue Zeng, Fushuo Huo | Recent studies usually approach multi-label zero-shot learning (MLZSL) with visual-semantic mapping on spatial-class correlation, which can be computationally costly, and worse still, fails to capture fine-grained class-specific semantics. We observe that different channels may usually have different sensitivities on classes, which can correspond to specific semantics. Such an intrinsic channel-class correlation suggests a potential alternative for the more accurate and class-harmonious feature representations. In this paper, our interest is to fully explore the power of channel-class correlation as the unique base for MLZSL. Specifically, we propose a light yet efficient Multi-Label Multi-Layer Perceptron-based Encoder, dubbed (ML)^2P-Encoder, to extract and preserve channel-wise semantics. We reorganize the generated feature maps into several groups, of which each of them can be trained independently with (ML)^2P-Encoder. On top of that, a global group-wise attention module is further designed to build the multi-label specific class relationships among different classes, which eventually fulfills a novel Channel-Class Correlation MLZSL framework (C^3-MLZSL). Extensive experiments on large-scale MLZSL benchmarks including NUS-WIDE and Open-Images-V4 demonstrate the superiority of our model against other representative state-of-the-art models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_ML2P-Encoder_On_Exploration_of_Channel-Class_Correlation_for_Multi-Label_Zero-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_ML2P-Encoder_On_Exploration_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_ML2P-Encoder_On_Exploration_of_Channel-Class_Correlation_for_Multi-Label_Zero-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_ML2P-Encoder_On_Exploration_of_Channel-Class_Correlation_for_Multi-Label_Zero-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
MeMaHand: Exploiting Mesh-Mano Interaction for Single Image Two-Hand Reconstruction | Congyi Wang, Feida Zhu, Shilei Wen | Existing methods proposed for hand reconstruction tasks usually parameterize a generic 3D hand model or predict hand mesh positions directly. The parametric representations consisting of hand shapes and rotational poses are more stable, while the non-parametric methods can predict more accurate mesh positions. In this paper, we propose to reconstruct meshes and estimate MANO parameters of two hands from a single RGB image simultaneously to utilize the merits of two kinds of hand representations. To fulfill this target, we propose novel Mesh-Mano interaction blocks (MMIBs), which take mesh vertices positions and MANO parameters as two kinds of query tokens. MMIB consists of one graph residual block to aggregate local information and two transformer encoders to model long-range dependencies. The transformer encoders are equipped with different asymmetric attention masks to model the intra-hand and inter-hand attention, respectively. Moreover, we introduce the mesh alignment refinement module to further enhance the mesh-image alignment. Extensive experiments on the InterHand2.6M benchmark demonstrate promising results over the state-of-the-art hand reconstruction methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MeMaHand_Exploiting_Mesh-Mano_Interaction_for_Single_Image_Two-Hand_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_MeMaHand_Exploiting_Mesh-Mano_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15718 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MeMaHand_Exploiting_Mesh-Mano_Interaction_for_Single_Image_Two-Hand_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MeMaHand_Exploiting_Mesh-Mano_Interaction_for_Single_Image_Two-Hand_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Asymmetric Feature Fusion for Image Retrieval | Hui Wu, Min Wang, Wengang Zhou, Zhenbo Lu, Houqiang Li | In asymmetric retrieval systems, models with different capacities are deployed on platforms with different computational and storage resources. Despite the great progress, existing approaches still suffer from a dilemma between retrieval efficiency and asymmetric accuracy due to the low capacity of the lightweight query model. In this work, we propose an Asymmetric Feature Fusion (AFF) paradigm, which advances existing asymmetric retrieval systems by considering the complementarity among different features just at the gallery side. Specifically, it first embeds each gallery image into various features, e.g., local features and global features. Then, a dynamic mixer is introduced to aggregate these features into a compact embedding for efficient search. On the query side, only a single lightweight model is deployed for feature extraction. The query model and dynamic mixer are jointly trained by sharing a momentum-updated classifier. Notably, the proposed paradigm boosts the accuracy of asymmetric retrieval without introducing any extra overhead to the query side. Exhaustive experiments on various landmark retrieval datasets demonstrate the superiority of our paradigm. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Asymmetric_Feature_Fusion_for_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Asymmetric_Feature_Fusion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Asymmetric_Feature_Fusion_for_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Asymmetric_Feature_Fusion_for_Image_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
CREPE: Can Vision-Language Foundation Models Reason Compositionally? | Zixian Ma, Jerry Hong, Mustafa Omer Gul, Mona Gandhi, Irena Gao, Ranjay Krishna | A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that--across 7 architectures trained with 4 algorithms on massive datasets--they struggle at compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark, CREPE, which measures two important aspects of compositionality identified by cognitive science literature: systematicity and productivity. To measure systematicity, CREPE consists of a test dataset containing over 370K image-text pairs and three different seen-unseen splits. The three splits are designed to test models trained on three popular training datasets: CC-12M, YFCC-15M, and LAION-400M. We also generate 325K, 316K, and 309K hard negative captions for a subset of the pairs. To test productivity, CREPE contains 17K image-text pairs with nine different complexities plus 278K hard negative captions with atomic, swapping, and negation foils. The datasets are generated by repurposing the Visual Genome scene graphs and region descriptions and applying handcrafted templates and GPT-3. For systematicity, we find that model performance decreases consistently when novel compositions dominate the retrieval set, with Recall@1 dropping by up to 9%. For productivity, models' retrieval success decays as complexity increases, frequently nearing random chance at high complexity. These results hold regardless of model and training dataset size. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_CREPE_Can_Vision-Language_Foundation_Models_Reason_Compositionally_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_CREPE_Can_Vision-Language_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07796 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_CREPE_Can_Vision-Language_Foundation_Models_Reason_Compositionally_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_CREPE_Can_Vision-Language_Foundation_Models_Reason_Compositionally_CVPR_2023_paper.html | CVPR 2023 | null |
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