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ImageBind: One Embedding Space To Bind Them All | Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra | We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together. ImageBind can leverage recent large scale vision-language models, and extends their zero-shot capabilities to new modalities just by using their natural pairing with images. It enables novel emergent applications 'out-of-the-box' including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. The emergent capabilities improve with the strength of the image encoder and we set a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Finally, we show strong few-shot recognition results outperforming prior work, and that ImageBind serves as a new way to evaluate vision models for visual and non-visual tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Girdhar_ImageBind_One_Embedding_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.05665 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023_paper.html | CVPR 2023 | null |
Orthogonal Annotation Benefits Barely-Supervised Medical Image Segmentation | Heng Cai, Shumeng Li, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao | Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation. Specifically, we firstly propose the orthogonal annotation by only labeling two orthogonal slices in a labeled volume, which significantly relieves the burden of annotation. Then, we perform registration to obtain the initial pseudo labels for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks. Experimental results on three benchmark datasets validated our effectiveness in performance and efficiency in annotation. For example, with only 10 annotated slices, our method reaches a Dice up to 86.93% on KiTS19 dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_Orthogonal_Annotation_Benefits_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13090 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation | Kun Zhou, Wenbo Li, Xiaoguang Han, Jiangbo Lu | For video frame interpolation(VFI), existing deep-learning-based approaches strongly rely on the ground-truth (GT) intermediate frames, which sometimes ignore the non-unique nature of motion judging from the given adjacent frames. As a result, these methods tend to produce averaged solutions that are not clear enough. To alleviate this issue, we propose to relax the requirement of reconstructing an intermediate frame as close to the GT as possible. Towards this end, we develop a texture consistency loss (TCL) upon the assumption that the interpolated content should maintain similar structures with their counterparts in the given frames. Predictions satisfying this constraint are encouraged, though they may differ from the predefined GT. Without the bells and whistles, our plug-and-play TCL is capable of improving the performance of existing VFI frameworks consistently. On the other hand, previous methods usually adopt the cost volume or correlation map to achieve more accurate image or feature warping. However, the O(N^2) (N refers to the pixel count) computational complexity makes it infeasible for high-resolution cases. In this work, we design a simple, efficient O(N) yet powerful guided cross-scale pyramid alignment(GCSPA) module, where multi-scale information is highly exploited. Extensive experiments justify the efficiency and effectiveness of the proposed strategy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Exploring_Motion_Ambiguity_and_Alignment_for_High-Quality_Video_Frame_Interpolation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Exploring_Motion_Ambiguity_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2203.10291 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Exploring_Motion_Ambiguity_and_Alignment_for_High-Quality_Video_Frame_Interpolation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Exploring_Motion_Ambiguity_and_Alignment_for_High-Quality_Video_Frame_Interpolation_CVPR_2023_paper.html | CVPR 2023 | null |
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions | Shuxuan Guo, Yinlin Hu, Jose M. Alvarez, Mathieu Salzmann | Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we introduce the first knowledge distillation method driven by the 6D pose estimation task. To this end, we observe that most modern 6D pose estimation frameworks output local predictions, such as sparse 2D keypoints or dense representations, and that the compact student network typically struggles to predict such local quantities precisely. Therefore, instead of imposing prediction-to-prediction supervision from the teacher to the student, we propose to distill the teacher's distribution of local predictions into the student network, facilitating its training. Our experiments on several benchmarks show that our distillation method yields state-of-the-art results with different compact student models and for both keypoint-based and dense prediction-based architectures. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Knowledge_Distillation_for_6D_Pose_Estimation_by_Aligning_Distributions_of_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Knowledge_Distillation_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.14971 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Knowledge_Distillation_for_6D_Pose_Estimation_by_Aligning_Distributions_of_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Knowledge_Distillation_for_6D_Pose_Estimation_by_Aligning_Distributions_of_CVPR_2023_paper.html | CVPR 2023 | null |
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning | Yun-Hao Cao, Peiqin Sun, Shuchang Zhou | We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different devices. We observe that direct adaptation of self-supervised learning (SSL) to universally slimmable networks misbehaves as the training process frequently collapses. We then discover that temporal consistent guidance is the key to the success of SSL for universally slimmable networks, and we propose three guidelines for the loss design to ensure this temporal consistency from a unified gradient perspective. Moreover, we propose dynamic sampling and group regularization strategies to simultaneously improve training efficiency and accuracy. Our US3L method has been empirically validated on both convolutional neural networks and vision transformers. With only once training and one copy of weights, our method outperforms various state-of-the-art methods (individually trained or not) on benchmarks including recognition, object detection and instance segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Three_Guidelines_You_Should_Know_for_Universally_Slimmable_Self-Supervised_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Three_Guidelines_You_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06870 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Three_Guidelines_You_Should_Know_for_Universally_Slimmable_Self-Supervised_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Three_Guidelines_You_Should_Know_for_Universally_Slimmable_Self-Supervised_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Annealing for Robust Geometric Estimation | Chitturi Sidhartha, Lalit Manam, Venu Madhav Govindu | Geometric estimation problems in vision are often solved via minimization of statistical loss functions which account for the presence of outliers in the observations. The corresponding energy landscape often has many local minima. Many approaches attempt to avoid local minima by annealing the scale parameter of loss functions using methods such as graduated non-convexity (GNC). However, little attention has been paid to the annealing schedule, which is often carried out in a fixed manner, resulting in a poor speed-accuracy trade-off and unreliable convergence to the global minimum. In this paper, we propose a principled approach for adaptively annealing the scale for GNC by tracking the positive-definiteness (i.e. local convexity) of the Hessian of the cost function. We illustrate our approach using the classic problem of registering 3D correspondences in the presence of noise and outliers. We also develop approximations to the Hessian that significantly speeds up our method. The effectiveness of our approach is validated by comparing its performance with state-of-the-art 3D registration approaches on a number of synthetic and real datasets. Our approach is accurate and efficient and converges to the global solution more reliably than the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sidhartha_Adaptive_Annealing_for_Robust_Geometric_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sidhartha_Adaptive_Annealing_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sidhartha_Adaptive_Annealing_for_Robust_Geometric_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sidhartha_Adaptive_Annealing_for_Robust_Geometric_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding From Object Detection | Wenda Zhao, Shigeng Xie, Fan Zhao, You He, Huchuan Lu | Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more attention. However, the feature gap between these two different-level tasks hinders the progress. Addressing this issue, this paper proposes an infrared and visible image fusion via meta-feature embedding from object detection. The core idea is that meta-feature embedding model is designed to generate object semantic features according to fusion network ability, and thus the semantic features are naturally compatible with fusion features. It is optimized by simulating a meta learning. Moreover, we further implement a mutual promotion learning between fusion and detection tasks to improve their performances. Comprehensive experiments on three public datasets demonstrate the effectiveness of our method. Code and model are available at: https://github.com/wdzhao123/MetaFusion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_MetaFusion_Infrared_and_Visible_Image_Fusion_via_Meta-Feature_Embedding_From_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_MetaFusion_Infrared_and_Visible_Image_Fusion_via_Meta-Feature_Embedding_From_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_MetaFusion_Infrared_and_Visible_Image_Fusion_via_Meta-Feature_Embedding_From_CVPR_2023_paper.html | CVPR 2023 | null |
Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising | Miaoyu Li, Ji Liu, Ying Fu, Yulun Zhang, Dejing Dou | Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing the great power of deep learning, existing HSI denoising methods suffer from limitations in capturing the non-local self-similarity. Transformers have shown potential in capturing long-range dependencies, but few attempts have been made with specifically designed Transformer to model the spatial and spectral correlation in HSIs. In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise, while enabling the interactions among non-overlapping spatial rectangles. Extensive experiments have been conducted on both synthetic noisy HSIs and real noisy HSIs, showing the effectiveness of our proposed method in terms of both objective metric and subjective visual quality. The code is available at https://github.com/MyuLi/SERT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Spectral_Enhanced_Rectangle_Transformer_for_Hyperspectral_Image_Denoising_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Spectral_Enhanced_Rectangle_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00844 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Spectral_Enhanced_Rectangle_Transformer_for_Hyperspectral_Image_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Spectral_Enhanced_Rectangle_Transformer_for_Hyperspectral_Image_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
End-to-End Vectorized HD-Map Construction With Piecewise Bezier Curve | Limeng Qiao, Wenjie Ding, Xi Qiu, Chi Zhang | Vectorized high-definition map (HD-map) construction, which focuses on the perception of centimeter-level environmental information, has attracted significant research interest in the autonomous driving community. Most existing approaches first obtain rasterized map with the segmentation-based pipeline and then conduct heavy post-processing for downstream-friendly vectorization. In this paper, by delving into parameterization-based methods, we pioneer a concise and elegant scheme that adopts unified piecewise Bezier curve. In order to vectorize changeful map elements end-to-end, we elaborate a simple yet effective architecture, named Piecewise Bezier HD-map Network (BeMapNet), which is formulated as a direct set prediction paradigm and postprocessing-free. Concretely, we first introduce a novel IPM-PE Align module to inject 3D geometry prior into BEV features through common position encoding in Transformer. Then a well-designed Piecewise Bezier Head is proposed to output the details of each map element, including the coordinate of control points and the segment number of curves. In addition, based on the progressively restoration of Bezier curve, we also present an efficient Point-Curve-Region Loss for supervising more robust and precise HD-map modeling. Extensive comparisons show that our method is remarkably superior to other existing SOTAs by 18.0 mAP at least. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qiao_End-to-End_Vectorized_HD-Map_Construction_With_Piecewise_Bezier_Curve_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qiao_End-to-End_Vectorized_HD-Map_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qiao_End-to-End_Vectorized_HD-Map_Construction_With_Piecewise_Bezier_Curve_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qiao_End-to-End_Vectorized_HD-Map_Construction_With_Piecewise_Bezier_Curve_CVPR_2023_paper.html | CVPR 2023 | null |
PointListNet: Deep Learning on 3D Point Lists | Hehe Fan, Linchao Zhu, Yi Yang, Mohan Kankanhalli | Deep neural networks on regular 1D lists (e.g., natural languages) and irregular 3D sets (e.g., point clouds) have made tremendous achievements. The key to natural language processing is to model words and their regular order dependency in texts. For point cloud understanding, the challenge is to understand the geometry via irregular point coordinates, in which point-feeding orders do not matter. However, there are a few kinds of data that exhibit both regular 1D list and irregular 3D set structures, such as proteins and non-coding RNAs. In this paper, we refer to them as 3D point lists and propose a Transformer-style PointListNet to model them. First, PointListNet employs non-parametric distance-based attention because we find sometimes it is the distance, instead of the feature or type, that mainly determines how much two points, e.g., amino acids, are correlated in the micro world. Second, different from the vanilla Transformer that directly performs a simple linear transformation on inputs to generate values and does not explicitly model relative relations, our PointListNet integrates the 1D order and 3D Euclidean displacements into values. We conduct experiments on protein fold classification and enzyme reaction classification. Experimental results show the effectiveness of the proposed PointListNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_PointListNet_Deep_Learning_on_3D_Point_Lists_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fan_PointListNet_Deep_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_PointListNet_Deep_Learning_on_3D_Point_Lists_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fan_PointListNet_Deep_Learning_on_3D_Point_Lists_CVPR_2023_paper.html | CVPR 2023 | null |
On Data Scaling in Masked Image Modeling | Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Yixuan Wei, Qi Dai, Han Hu | Scaling properties have been one of the central issues in self-supervised pre-training, especially the data scalability, which has successfully motivated the large-scale self-supervised pre-trained language models and endowed them with significant modeling capabilities. However, scaling properties seem to be unintentionally neglected in the recent trending studies on masked image modeling (MIM), and some arguments even suggest that MIM cannot benefit from large-scale data. In this work, we try to break down these preconceptions and systematically study the scaling behaviors of MIM through extensive experiments, with data ranging from 10% of ImageNet-1K to full ImageNet-22K, model parameters ranging from 49-million to one-billion, and training length ranging from 125K to 500K iterations. And our main findings can be summarized in two folds: 1) masked image modeling remains demanding large-scale data in order to scale up computes and model parameters; 2) masked image modeling cannot benefit from more data under a non-overfitting scenario, which diverges from the previous observations in self-supervised pre-trained language models or supervised pre-trained vision models. In addition, we reveal several intriguing properties in MIM, such as high sample efficiency in large MIM models and strong correlation between pre-training validation loss and transfer performance. We hope that our findings could deepen the understanding of masked image modeling and facilitate future developments on large-scale vision models. Code and models will be available at https://github.com/microsoft/SimMIM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_On_Data_Scaling_in_Masked_Image_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_On_Data_Scaling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.04664 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_On_Data_Scaling_in_Masked_Image_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_On_Data_Scaling_in_Masked_Image_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Upcycling Models Under Domain and Category Shift | Sanqing Qu, Tianpei Zou, Florian Röhrbein, Cewu Lu, Guang Chen, Dacheng Tao, Changjun Jiang | Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. How to upcycle DNNs and adapt them to the target task remains an important open problem. Unsupervised Domain Adaptation (UDA), especially recently proposed Source-free Domain Adaptation (SFDA), has become a promising technology to address this issue. Nevertheless, most existing SFDA methods require that the source domain and target domain share the same label space, consequently being only applicable to the vanilla closed-set setting. In this paper, we take one step further and explore the Source-free Universal Domain Adaptation (SF-UniDA). The goal is to identify "known" data samples under both domain and category shift, and reject those "unknown" data samples (not present in source classes), with only the knowledge from standard pre-trained source model. To this end, we introduce an innovative global and local clustering learning technique (GLC). Specifically, we design a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes and introduce a local k-NN clustering strategy to alleviate negative transfer. We examine the superiority of our GLC on multiple benchmarks with different category shift scenarios, including partial-set, open-set, and open-partial-set DA. More remarkably, in the most challenging open-partial-set DA scenario, GLC outperforms UMAD by 14.8% on the VisDA benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qu_Upcycling_Models_Under_Domain_and_Category_Shift_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qu_Upcycling_Models_Under_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_Upcycling_Models_Under_Domain_and_Category_Shift_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_Upcycling_Models_Under_Domain_and_Category_Shift_CVPR_2023_paper.html | CVPR 2023 | null |
Single Domain Generalization for LiDAR Semantic Segmentation | Hyeonseong Kim, Yoonsu Kang, Changgyoon Oh, Kuk-Jin Yoon | With the success of the 3D deep learning models, various perception technologies for autonomous driving have been developed in the LiDAR domain. While these models perform well in the trained source domain, they struggle in unseen domains with a domain gap. In this paper, we propose a single domain generalization method for LiDAR semantic segmentation (DGLSS) that aims to ensure good performance not only in the source domain but also in the unseen domain by learning only on the source domain. We mainly focus on generalizing from a dense source domain and target the domain shift from different LiDAR sensor configurations and scene distributions. To this end, we augment the domain to simulate the unseen domains by randomly subsampling the LiDAR scans. With the augmented domain, we introduce two constraints for generalizable representation learning: sparsity invariant feature consistency (SIFC) and semantic correlation consistency (SCC). The SIFC aligns sparse internal features of the source domain with the augmented domain based on the feature affinity. For SCC, we constrain the correlation between class prototypes to be similar for every LiDAR scan. We also establish a standardized training and evaluation setting for DGLSS. With the proposed evaluation setting, our method showed improved performance in the unseen domains compared to other baselines. Even without access to the target domain, our method performed better than the domain adaptation method. The code is available at https://github.com/gzgzys9887/DGLSS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Single_Domain_Generalization_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Single_Domain_Generalization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Single_Domain_Generalization_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Single_Domain_Generalization_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Balanced Energy Regularization Loss for Out-of-Distribution Detection | Hyunjun Choi, Hawook Jeong, Jin Young Choi | In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a general imbalance in the distribution of the auxiliary OOD data across classes. We propose a balanced energy regularization loss that is simple but generally effective for a variety of tasks. Our balanced energy regularization loss utilizes class-wise different prior probabilities for auxiliary data to address the class imbalance in OOD data. The main concept is to regularize auxiliary samples from majority classes, more heavily than those from minority classes. Our approach performs better for OOD detection in semantic segmentation, long-tailed image classification, and image classification than the prior energy regularization loss. Furthermore, our approach achieves state-of-the-art performance in two tasks: OOD detection in semantic segmentation and long-tailed image classification. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_Balanced_Energy_Regularization_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
3D-Aware Face Swapping | Yixuan Li, Chao Ma, Yichao Yan, Wenhan Zhu, Xiaokang Yang | Face swapping is an important research topic in computer vision with wide applications in entertainment and privacy protection. Existing methods directly learn to swap 2D facial images, taking no account of the geometric information of human faces. In the presence of large pose variance between the source and the target faces, there always exist undesirable artifacts on the swapped face. In this paper, we present a novel 3D-aware face swapping method that generates high-fidelity and multi-view-consistent swapped faces from single-view source and target images. To achieve this, we take advantage of the strong geometry and texture prior of 3D human faces, where the 2D faces are projected into the latent space of a 3D generative model. By disentangling the identity and attribute features in the latent space, we succeed in swapping faces in a 3D-aware manner, being robust to pose variations while transferring fine-grained facial details. Extensive experiments demonstrate the superiority of our 3D-aware face swapping framework in terms of visual quality, identity similarity, and multi-view consistency. Code is available at https://lyx0208.github.io/3dSwap. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_3D-Aware_Face_Swapping_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_3D-Aware_Face_Swapping_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_3D-Aware_Face_Swapping_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_3D-Aware_Face_Swapping_CVPR_2023_paper.html | CVPR 2023 | null |
UMat: Uncertainty-Aware Single Image High Resolution Material Capture | Carlos Rodriguez-Pardo, Henar Domínguez-Elvira, David Pascual-Hernández, Elena Garces | We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. In contrast, in this work, we propose a novel capture approach that leverages a generative network with attention and a U-Net discriminator, which shows outstanding performance integrating global information at reduced computational complexity. We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method. Additionally, because the problem might be ill-posed --more than a single diffuse image might be needed to disambiguate the specular reflection-- or because the training dataset is not representative enough of the real distribution, we propose a novel framework to quantify the model's confidence about its prediction at test time. Our method is the first one to deal with the problem of modeling uncertainty in material digitization, increasing the trustworthiness of the process and enabling more intelligent strategies for dataset creation, as we demonstrate with an active learning experiment. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rodriguez-Pardo_UMat_Uncertainty-Aware_Single_Image_High_Resolution_Material_Capture_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rodriguez-Pardo_UMat_Uncertainty-Aware_Single_Image_High_Resolution_Material_Capture_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rodriguez-Pardo_UMat_Uncertainty-Aware_Single_Image_High_Resolution_Material_Capture_CVPR_2023_paper.html | CVPR 2023 | null |
Similarity Maps for Self-Training Weakly-Supervised Phrase Grounding | Tal Shaharabany, Lior Wolf | A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state-of-the-art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which the input image is given without any text. Our code is available as supplementary. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shaharabany_Similarity_Maps_for_Self-Training_Weakly-Supervised_Phrase_Grounding_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shaharabany_Similarity_Maps_for_Self-Training_Weakly-Supervised_Phrase_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shaharabany_Similarity_Maps_for_Self-Training_Weakly-Supervised_Phrase_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow | Itai Lang, Dror Aiger, Forrester Cole, Shai Avidan, Michael Rubinstein | Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. Recently, there have been efforts to compute the scene flow from 3D point clouds. A common approach is to train a regression model that consumes source and target point clouds and outputs the per-point translation vector. An alternative is to learn point matches between the point clouds concurrently with regressing a refinement of the initial correspondence flow. In both cases, the learning task is very challenging since the flow regression is done in the free 3D space, and a typical solution is to resort to a large annotated synthetic dataset. We introduce SCOOP, a new method for scene flow estimation that can be learned on a small amount of data without employing ground-truth flow supervision. In contrast to previous work, we train a pure correspondence model focused on learning point feature representation and initialize the flow as the difference between a source point and its softly corresponding target point. Then, in the run-time phase, we directly optimize a flow refinement component with a self-supervised objective, which leads to a coherent and accurate flow field between the point clouds. Experiments on widespread datasets demonstrate the performance gains achieved by our method compared to existing leading techniques while using a fraction of the training data. Our code is publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lang_SCOOP_Self-Supervised_Correspondence_and_Optimization-Based_Scene_Flow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lang_SCOOP_Self-Supervised_Correspondence_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14020 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lang_SCOOP_Self-Supervised_Correspondence_and_Optimization-Based_Scene_Flow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lang_SCOOP_Self-Supervised_Correspondence_and_Optimization-Based_Scene_Flow_CVPR_2023_paper.html | CVPR 2023 | null |
SLACK: Stable Learning of Augmentations With Cold-Start and KL Regularization | Juliette Marrie, Michael Arbel, Diane Larlus, Julien Mairal | Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed manually. Automatic data augmentation aims at automating this process. However, most recent approaches still rely on some prior information; they start from a small pool of manually-selected default transformations that are either used to pretrain the network or forced to be part of the policy learned by the automatic data augmentation algorithm. In this paper, we propose to directly learn the augmentation policy without leveraging such prior knowledge. The resulting bilevel optimization problem becomes more challenging due to the larger search space and the inherent instability of bilevel optimization algorithms. To mitigate these issues (i) we follow a successive cold-start strategy with a Kullback-Leibler regularization, and (ii) we parameterize magnitudes as continuous distributions. Our approach leads to competitive results on standard benchmarks despite a more challenging setting, and generalizes beyond natural images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Marrie_SLACK_Stable_Learning_of_Augmentations_With_Cold-Start_and_KL_Regularization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Marrie_SLACK_Stable_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Marrie_SLACK_Stable_Learning_of_Augmentations_With_Cold-Start_and_KL_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Marrie_SLACK_Stable_Learning_of_Augmentations_With_Cold-Start_and_KL_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization | Xingxuan Zhang, Renzhe Xu, Han Yu, Hao Zou, Peng Cui | Recently, flat minima are proven to be effective for improving generalization and sharpness-aware minimization (SAM) achieves state-of-the-art performance. Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i.e., the worst-case loss within a perturbation radius). We show that the zeroth-order flatness can be insufficient to discriminate minima with low generalization error from those with high generalization error both when there is a single minimum or multiple minima within the given perturbation radius. Thus we present first-order flatness, a stronger measure of flatness focusing on the maximal gradient norm within a perturbation radius which bounds both the maximal eigenvalue of Hessian at local minima and the regularization function of SAM. We also present a novel training procedure named Gradient norm Aware Minimization (GAM) to seek minima with uniformly small curvature across all directions. Experimental results show that GAM improves the generalization of models trained with current optimizers such as SGD and AdamW on various datasets and networks. Furthermore, we show that GAM can help SAM find flatter minima and achieve better generalization. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Gradient_Norm_Aware_Minimization_Seeks_First-Order_Flatness_and_Improves_Generalization_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.03108 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Gradient_Norm_Aware_Minimization_Seeks_First-Order_Flatness_and_Improves_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Gradient_Norm_Aware_Minimization_Seeks_First-Order_Flatness_and_Improves_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Phone2Proc: Bringing Robust Robots Into Our Chaotic World | Matt Deitke, Rose Hendrix, Ali Farhadi, Kiana Ehsani, Aniruddha Kembhavi | Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to generalize to real-world environments. In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan and conditional procedural generation to create a distribution of training scenes that are semantically similar to the target environment. The generated scenes are conditioned on the wall layout and arrangement of large objects from the scan, while also sampling lighting, clutter, surface textures, and instances of smaller objects with randomized placement and materials. Leveraging just a simple RGB camera, training with Phone2Proc shows massive improvements from 34.7% to 70.7% success rate in sim-to-real ObjectNav performance across a test suite of over 200 trials in diverse real-world environments, including homes, offices, and RoboTHOR. Furthermore, Phone2Proc's diverse distribution of generated scenes makes agents remarkably robust to changes in the real world, such as human movement, object rearrangement, lighting changes, or clutter. | https://openaccess.thecvf.com/content/CVPR2023/papers/Deitke_Phone2Proc_Bringing_Robust_Robots_Into_Our_Chaotic_World_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Deitke_Phone2Proc_Bringing_Robust_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.04819 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deitke_Phone2Proc_Bringing_Robust_Robots_Into_Our_Chaotic_World_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deitke_Phone2Proc_Bringing_Robust_Robots_Into_Our_Chaotic_World_CVPR_2023_paper.html | CVPR 2023 | null |
Latency Matters: Real-Time Action Forecasting Transformer | Harshayu Girase, Nakul Agarwal, Chiho Choi, Karttikeya Mangalam | We present RAFTformer, a real-time action forecasting transformer for latency aware real-world action forecasting applications. RAFTformer is a two-stage fully transformer based architecture which consists of a video transformer backbone that operates on high resolution, short range clips and a head transformer encoder that temporally aggregates information from multiple short range clips to span a long-term horizon. Additionally, we propose a self-supervised shuffled causal masking scheme to improve model generalization during training. Finally, we also propose a real-time evaluation setting that directly couples model inference latency to overall forecasting performance and brings forth an hitherto overlooked trade-off between latency and action forecasting performance. Our parsimonious network design facilitates RAFTformer inference latency to be 9x smaller than prior works at the same forecasting accuracy. Owing to its two-staged design, RAFTformer uses 94% less training compute and 90% lesser training parameters to outperform prior state-of-the-art baselines by 4.9 points on EGTEA Gaze+ and by 1.4 points on EPIC-Kitchens-100 dataset, as measured by Top-5 recall (T5R) in the offline setting. In the real-time setting, RAFTformer outperforms prior works by an even greater margin of upto 4.4 T5R points on the EPIC-Kitchens-100 dataset. Project Webpage: https://karttikeya.github.io/publication/RAFTformer/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Girase_Latency_Matters_Real-Time_Action_Forecasting_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Girase_Latency_Matters_Real-Time_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Girase_Latency_Matters_Real-Time_Action_Forecasting_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Girase_Latency_Matters_Real-Time_Action_Forecasting_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
HierVL: Learning Hierarchical Video-Language Embeddings | Kumar Ashutosh, Rohit Girdhar, Lorenzo Torresani, Kristen Grauman | Video-language embeddings are a promising avenue for injecting semantics into visual representations, but existing methods capture only short-term associations between seconds-long video clips and their accompanying text. We propose HierVL, a novel hierarchical video-language embedding that simultaneously accounts for both long-term and short-term associations. As training data, we take videos accompanied by timestamped text descriptions of human actions, together with a high-level text summary of the activity throughout the long video (as are available in Ego4D). We introduce a hierarchical contrastive training objective that encourages text-visual alignment at both the clip level and video level. While the clip-level constraints use the step-by-step descriptions to capture what is happening in that instant, the video-level constraints use the summary text to capture why it is happening, i.e., the broader context for the activity and the intent of the actor. Our hierarchical scheme yields a clip representation that outperforms its single-level counterpart, as well as a long-term video representation that achieves SotA results on tasks requiring long-term video modeling. HierVL successfully transfers to multiple challenging downstream tasks (in EPIC-KITCHENS-100, Charades-Ego, HowTo100M) in both zero-shot and fine-tuned settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ashutosh_HierVL_Learning_Hierarchical_Video-Language_Embeddings_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ashutosh_HierVL_Learning_Hierarchical_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02311 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ashutosh_HierVL_Learning_Hierarchical_Video-Language_Embeddings_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ashutosh_HierVL_Learning_Hierarchical_Video-Language_Embeddings_CVPR_2023_paper.html | CVPR 2023 | null |
GraVoS: Voxel Selection for 3D Point-Cloud Detection | Oren Shrout, Yizhak Ben-Shabat, Ayellet Tal | 3D object detection within large 3D scenes is challenging not only due to the sparse and irregular 3D point clouds, but also due to both the extreme foreground-background scene imbalance and class imbalance. A common approach is to add ground-truth objects from other scenes. Differently, we propose to modify the scenes by removing elements (voxels), rather than adding ones. Our approach selects the "meaningful" voxels, in a manner that addresses both types of dataset imbalance. The approach is general and can be applied to any voxel-based detector, yet the meaningfulness of a voxel is network-dependent. Our voxel selection is shown to improve the performance of several prominent 3D detection methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shrout_GraVoS_Voxel_Selection_for_3D_Point-Cloud_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shrout_GraVoS_Voxel_Selection_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.08780 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shrout_GraVoS_Voxel_Selection_for_3D_Point-Cloud_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shrout_GraVoS_Voxel_Selection_for_3D_Point-Cloud_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Articulated Shape With Keypoint Pseudo-Labels From Web Images | Anastasis Stathopoulos, Georgios Pavlakos, Ligong Han, Dimitris N. Metaxas | This paper shows that it is possible to learn models for monocular 3D reconstruction of articulated objects (e.g. horses, cows, sheep), using as few as 50-150 images labeled with 2D keypoints. Our proposed approach involves training category-specific keypoint estimators, generating 2D keypoint pseudo-labels on unlabeled web images, and using both the labeled and self-labeled sets to train 3D reconstruction models. It is based on two key insights: (1) 2D keypoint estimation networks trained on as few as 50-150 images of a given object category generalize well and generate reliable pseudo-labels; (2) a data selection mechanism can automatically create a "curated" subset of the unlabeled web images that can be used for training -- we evaluate four data selection methods. Coupling these two insights enables us to train models that effectively utilize web images, resulting in improved 3D reconstruction performance for several articulated object categories beyond the fully-supervised baseline. Our approach can quickly bootstrap a model and requires only a few images labeled with 2D keypoints. This requirement can be easily satisfied for any new object category. To showcase the practicality of our approach for predicting the 3D shape of arbitrary object categories, we annotate 2D keypoints on 250 giraffe and bear images from COCO in just 2.5 hours per category. | https://openaccess.thecvf.com/content/CVPR2023/papers/Stathopoulos_Learning_Articulated_Shape_With_Keypoint_Pseudo-Labels_From_Web_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Stathopoulos_Learning_Articulated_Shape_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Stathopoulos_Learning_Articulated_Shape_With_Keypoint_Pseudo-Labels_From_Web_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Stathopoulos_Learning_Articulated_Shape_With_Keypoint_Pseudo-Labels_From_Web_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective | Yuanbiao Gou, Peng Hu, Jiancheng Lv, Hongyuan Zhu, Xi Peng | Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer from a machine learning perspective, i.e., the twin fitting problem caused by the long-tailed pixel distribution in natural images. With this explanation, we reformulate image super resolution (SR) as a long-tailed distribution learning problem and solve it by bridging the gaps of the problem between in low- and high-level vision tasks. As a result, we design a long-tailed distribution learning solution, that rebalances the gradients from the pixels in the low- and high-frequency region, by introducing a static and a learnable structure prior. The learned SR model achieves better balance on the fitting of the low- and high-frequency region so that the overall performance is improved. In the experiments, we evaluate the solution on four CNN- and one Transformer-based SR models w.r.t. six datasets and three tasks, and experimental results demonstrate its superiority. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gou_Rethinking_Image_Super_Resolution_From_Long-Tailed_Distribution_Learning_Perspective_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gou_Rethinking_Image_Super_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gou_Rethinking_Image_Super_Resolution_From_Long-Tailed_Distribution_Learning_Perspective_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gou_Rethinking_Image_Super_Resolution_From_Long-Tailed_Distribution_Learning_Perspective_CVPR_2023_paper.html | CVPR 2023 | null |
RobustNeRF: Ignoring Distractors With Robust Losses | Sara Sabour, Suhani Vora, Daniel Duckworth, Ivan Krasin, David J. Fleet, Andrea Tagliasacchi | Neural radiance fields (NeRF) excel at synthesizing new views given multi-view, calibrated images of a static scene. When scenes include distractors, which are not persistent during image capture (moving objects, lighting variations, shadows), artifacts appear as view-dependent effects or 'floaters'. To cope with distractors, we advocate a form of robust estimation for NeRF training, modeling distractors in training data as outliers of an optimization problem. Our method successfully removes outliers from a scene and improves upon our baselines, on synthetic and real-world scenes. Our technique is simple to incorporate in modern NeRF frameworks, with few hyper-parameters. It does not assume a priori knowledge of the types of distractors, and is instead focused on the optimization problem rather than pre-processing or modeling transient objects. More results on our page https://robustnerf.github.io/public. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sabour_RobustNeRF_Ignoring_Distractors_With_Robust_Losses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sabour_RobustNeRF_Ignoring_Distractors_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.00833 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sabour_RobustNeRF_Ignoring_Distractors_With_Robust_Losses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sabour_RobustNeRF_Ignoring_Distractors_With_Robust_Losses_CVPR_2023_paper.html | CVPR 2023 | null |
Spherical Transformer for LiDAR-Based 3D Recognition | Xin Lai, Yukang Chen, Fanbin Lu, Jianhui Liu, Jiaya Jia | LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. We design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows. It overcomes the disconnection issue and enlarges the receptive field smoothly and dramatically, which significantly boosts the performance of sparse distant points. Moreover, to fit the narrow and long windows, we propose exponential splitting to yield fine-grained position encoding and dynamic feature selection to increase model representation ability. Notably, our method ranks 1st on both nuScenes and SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU, respectively. Also, we achieve the 3rd place on nuScenes object detection benchmark with 72.8% NDS and 68.5% mAP. Code is available at https://github.com/dvlab-research/SphereFormer.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lai_Spherical_Transformer_for_LiDAR-Based_3D_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lai_Spherical_Transformer_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12766 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lai_Spherical_Transformer_for_LiDAR-Based_3D_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lai_Spherical_Transformer_for_LiDAR-Based_3D_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes | Xuan Ju, Ailing Zeng, Jianan Wang, Qiang Xu, Lei Zhang | Humans have long been recorded in a variety of forms since antiquity. For example, sculptures and paintings were the primary media for depicting human beings before the invention of cameras. However, most current human-centric computer vision tasks like human pose estimation and human image generation focus exclusively on natural images in the real world. Artificial humans, such as those in sculptures, paintings, and cartoons, are commonly neglected, making existing models fail in these scenarios. As an abstraction of life, art incorporates humans in both natural and artificial scenes. We take advantage of it and introduce the Human-Art dataset to bridge related tasks in natural and artificial scenarios. Specifically, Human-Art contains 50k high-quality images with over 123k person instances from 5 natural and 15 artificial scenarios, which are annotated with bounding boxes, keypoints, self-contact points, and text information for humans represented in both 2D and 3D. It is, therefore, comprehensive and versatile for various downstream tasks. We also provide a rich set of baseline results and detailed analyses for related tasks, including human detection, 2D and 3D human pose estimation, image generation, and motion transfer. As a challenging dataset, we hope Human-Art can provide insights for relevant research and open up new research questions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ju_Human-Art_A_Versatile_Human-Centric_Dataset_Bridging_Natural_and_Artificial_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ju_Human-Art_A_Versatile_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ju_Human-Art_A_Versatile_Human-Centric_Dataset_Bridging_Natural_and_Artificial_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ju_Human-Art_A_Versatile_Human-Centric_Dataset_Bridging_Natural_and_Artificial_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
Watch or Listen: Robust Audio-Visual Speech Recognition With Visual Corruption Modeling and Reliability Scoring | Joanna Hong, Minsu Kim, Jeongsoo Choi, Yong Man Ro | This paper deals with Audio-Visual Speech Recognition (AVSR) under multimodal input corruption situation where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean visual inputs with the assumption of the availability of clean visual inputs. However, in real life, the clean visual inputs are not always accessible and can even be corrupted by occluded lip region or with noises. Thus, we firstly analyze that the previous AVSR models are not indeed robust to the corruption of multimodal input streams, the audio and the visual inputs, compared to uni-modal models. Then, we design multimodal input corruption modeling to develop robust AVSR models. Lastly, we propose a novel AVSR framework, namely Audio-Visual Reliability Scoring module (AV-RelScore), that is robust to the corrupted multimodal inputs. The AV-RelScore can determine which input modal stream is reliable or not for the prediction and also can exploit the more reliable streams in prediction. The effectiveness of the proposed method is evaluated with comprehensive experiments on popular benchmark databases, LRS2 and LRS3. We also show that the reliability scores obtained by AV-RelScore well reflect the degree of corruption and make the proposed model focus on the reliable multimodal representations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hong_Watch_or_Listen_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.08536 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023_paper.html | CVPR 2023 | null |
Turning a CLIP Model Into a Scene Text Detector | Wenwen Yu, Yuliang Liu, Wei Hua, Deqiang Jiang, Bo Ren, Xiang Bai | The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection. In contrast to these works, this paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process. We demonstrate the advantages of the proposed TCM as follows: (1) The underlying principle of our framework can be applied to improve existing scene text detector. (2) It facilitates the few-shot training capability of existing methods, e.g., by using 10% of labeled data, we significantly improve the performance of the baseline method with an average of 22% in terms of the F-measure on 4 benchmarks. (3) By turning the CLIP model into existing scene text detection methods, we further achieve promising domain adaptation ability. The code will be publicly released at https://github.com/wenwenyu/TCM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Turning_a_CLIP_Model_Into_a_Scene_Text_Detector_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Turning_a_CLIP_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14338 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Turning_a_CLIP_Model_Into_a_Scene_Text_Detector_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Turning_a_CLIP_Model_Into_a_Scene_Text_Detector_CVPR_2023_paper.html | CVPR 2023 | null |
VisFusion: Visibility-Aware Online 3D Scene Reconstruction From Videos | Huiyu Gao, Wei Mao, Miaomiao Liu | We propose VisFusion, a visibility-aware online 3D scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric features. Unlike previous reconstruction methods which aggregate features for each voxel from input views without considering its visibility, we aim to improve the feature fusion by explicitly inferring its visibility from a similarity matrix, computed from its projected features in each image pair. Following previous works, our model is a coarse-to-fine pipeline including a volume sparsification process. Different from their works which sparsify voxels globally with a fixed occupancy threshold, we perform the sparsification on a local feature volume along each visual ray to preserve at least one voxel per ray for more fine details. The sparse local volume is then fused with a global one for online reconstruction. We further propose to predict TSDF in a coarse-to-fine manner by learning its residuals across scales leading to better TSDF predictions. Experimental results on benchmarks show that our method can achieve superior performance with more scene details. Code is available at: https://github.com/huiyu-gao/VisFusion | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_VisFusion_Visibility-Aware_Online_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.10687 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
SCOTCH and SODA: A Transformer Video Shadow Detection Framework | Lihao Liu, Jean Prost, Lei Zhu, Nicolas Papadakis, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero | Shadows in videos are difficult to detect because of the large shadow deformation between frames. In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method. To this end, we introduce the shadow deformation attention trajectory (SODA), a new type of video self-attention module, specially designed to handle the large shadow deformations in videos. Moreover, we present a new shadow contrastive learning mechanism (SCOTCH) which aims at guiding the network to learn a unified shadow representation from massive positive shadow pairs across different videos. We demonstrate empirically the effectiveness of our two contributions in an ablation study. Furthermore, we show that SCOTCH and SODA significantly outperforms existing techniques for video shadow detection. Code is available at the project page: https://lihaoliu-cambridge.github.io/scotch_and_soda/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_SCOTCH_and_SODA_A_Transformer_Video_Shadow_Detection_Framework_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.06885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SCOTCH_and_SODA_A_Transformer_Video_Shadow_Detection_Framework_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SCOTCH_and_SODA_A_Transformer_Video_Shadow_Detection_Framework_CVPR_2023_paper.html | CVPR 2023 | null |
RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion | Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, Baining Guo | This paper presents a 3D diffusion model that automatically generates 3D digital avatars represented as neural radiance fields (NeRFs). A significant challenge for 3D diffusion is that the memory and processing costs are prohibitive for producing high-quality results with rich details. To tackle this problem, we propose the roll-out diffusion network (RODIN), which takes a 3D NeRF model represented as multiple 2D feature maps and rolls out them onto a single 2D feature plane within which we perform 3D-aware diffusion. The RODIN model brings much-needed computational efficiency while preserving the integrity of 3D diffusion by using 3D-aware convolution that attends to projected features in the 2D plane according to their original relationships in 3D. We also use latent conditioning to orchestrate the feature generation with global coherence, leading to high-fidelity avatars and enabling semantic editing based on text prompts. Finally, we use hierarchical synthesis to further enhance details. The 3D avatars generated by our model compare favorably with those produced by existing techniques. We can generate highly detailed avatars with realistic hairstyles and facial hair. We also demonstrate 3D avatar generation from image or text, as well as text-guided editability. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_RODIN_A_Generative_Model_for_Sculpting_3D_Digital_Avatars_Using_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_RODIN_A_Generative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06135 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_RODIN_A_Generative_Model_for_Sculpting_3D_Digital_Avatars_Using_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_RODIN_A_Generative_Model_for_Sculpting_3D_Digital_Avatars_Using_CVPR_2023_paper.html | CVPR 2023 | null |
On the Pitfall of Mixup for Uncertainty Calibration | Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang | By simply taking convex combinations between pairs of samples and their labels, mixup training has been shown to easily improve predictive accuracy. It has been recently found that models trained with mixup also perform well on uncertainty calibration. However, in this study, we found that mixup training usually makes models less calibratable than vanilla empirical risk minimization, which means that it would harm uncertainty estimation when post-hoc calibration is considered. By decomposing the mixup process into data transformation and random perturbation, we suggest that the confidence penalty nature of the data transformation is the reason of calibration degradation. To mitigate this problem, we first investigate the mixup inference strategy and found that despite it improves calibration on mixup, this ensemble-like strategy does not necessarily outperform simple ensemble. Then, we propose a general strategy named mixup inference in training, which adopts a simple decoupling principle for recovering the outputs of raw samples at the end of forward network pass. By embedding the mixup inference, models can be learned from the original one-hot labels and hence avoid the negative impact of confidence penalty. Our experiments show this strategy properly solves mixup's calibration issue without sacrificing the predictive performance, while even improves accuracy than vanilla mixup. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_On_the_Pitfall_of_Mixup_for_Uncertainty_Calibration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_On_the_Pitfall_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_On_the_Pitfall_of_Mixup_for_Uncertainty_Calibration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_On_the_Pitfall_of_Mixup_for_Uncertainty_Calibration_CVPR_2023_paper.html | CVPR 2023 | null |
Feature Shrinkage Pyramid for Camouflaged Object Detection With Transformers | Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, Huan Xiong | Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggregation in decoders, which are not conducive to camouflaged object detection that explores subtle cues from indistinguishable backgrounds. To address these issues, in this paper, we propose a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrinking for camouflaged object detection. Specifically, we propose a non-local token enhancement module (NL-TEM) that employs the non-local mechanism to interact neighboring tokens and explore graph-based high-order relations within tokens to enhance local representations of transformers. Moreover, we design a feature shrinkage decoder (FSD) with adjacent interaction modules (AIM), which progressively aggregates adjacent transformer features through a layer-by-layer shrinkage pyramid to accumulate imperceptible but effective cues as much as possible for object information decoding. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing 24 competitors on three challenging COD benchmark datasets under six widely-used evaluation metrics. Our code is publicly available at https://github.com/ZhouHuang23/FSPNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Feature_Shrinkage_Pyramid_for_Camouflaged_Object_Detection_With_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Feature_Shrinkage_Pyramid_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14816 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Feature_Shrinkage_Pyramid_for_Camouflaged_Object_Detection_With_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Feature_Shrinkage_Pyramid_for_Camouflaged_Object_Detection_With_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose Estimation | Min Shi, Zihao Huang, Xianzheng Ma, Xiaowei Hu, Zhiguo Cao | Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary categories given support images with keypoint annotations. Existing approaches match the keypoints across the image for localization. However, such a one-stage matching paradigm shows inferior accuracy: the prediction heavily relies on the matching results, which can be noisy due to the open set nature in CAPE. For example, two mirror-symmetric keypoints (e.g., left and right eyes) in the query image can both trigger high similarity on certain support keypoints (eyes), which leads to duplicated or opposite predictions. To calibrate the inaccurate matching results, we introduce a two-stage framework, where matched keypoints from the first stage are viewed as similarity-aware position proposals. Then, the model learns to fetch relevant features to correct the initial proposals in the second stage. We instantiate the framework with a transformer model tailored for CAPE. The transformer encoder incorporates specific designs to improve the representation and similarity modeling in the first matching stage. In the second stage, similarity-aware proposals are packed as queries in the decoder for refinement via cross-attention. Our method surpasses the previous best approach by large margins on CAPE benchmark MP-100 on both accuracy and efficiency. Code available at https://github.com/flyinglynx/CapeFormer | https://openaccess.thecvf.com/content/CVPR2023/papers/Shi_Matching_Is_Not_Enough_A_Two-Stage_Framework_for_Category-Agnostic_Pose_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shi_Matching_Is_Not_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Matching_Is_Not_Enough_A_Two-Stage_Framework_for_Category-Agnostic_Pose_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Matching_Is_Not_Enough_A_Two-Stage_Framework_for_Category-Agnostic_Pose_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity Guided Image Synthesis With Latent Diffusion Models | Jaskirat Singh, Stephen Gould, Liang Zheng | Controllable image synthesis with user scribbles has gained huge public interest with the recent advent of text-conditioned latent diffusion models. The user scribbles control the color composition while the text prompt provides control over the overall image semantics. However, we find that prior works suffer from an intrinsic domain shift problem wherein the generated outputs often lack details and resemble simplistic representations of the target domain. In this paper, we propose a novel guided image synthesis framework, which addresses this problem by modeling the output image as the solution of a constrained optimization problem. We show that while computing an exact solution to the optimization is infeasible, an approximation of the same can be achieved while just requiring a single pass of the reverse diffusion process. Additionally, we show that by simply defining a cross-attention based correspondence between the input text tokens and the user stroke-painting, the user is also able to control the semantics of different painted regions without requiring any conditional training or finetuning. Human user study results show that the proposed approach outperforms the previous state-of-the-art by over 85.32% on the overall user satisfaction scores. Project page for our paper is available at https://1jsingh.github.io/gradop. | https://openaccess.thecvf.com/content/CVPR2023/papers/Singh_High-Fidelity_Guided_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Singh_High-Fidelity_Guided_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.17084 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_High-Fidelity_Guided_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_High-Fidelity_Guided_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
CodeTalker: Speech-Driven 3D Facial Animation With Discrete Motion Prior | Jinbo Xing, Menghan Xia, Yuechen Zhang, Xiaodong Cun, Jue Wang, Tien-Tsin Wong | Speech-driven 3D facial animation has been widely studied, yet there is still a gap to achieving realism and vividness due to the highly ill-posed nature and scarcity of audio-visual data. Existing works typically formulate the cross-modal mapping into a regression task, which suffers from the regression-to-mean problem leading to over-smoothed facial motions. In this paper, we propose to cast speech-driven facial animation as a code query task in a finite proxy space of the learned codebook, which effectively promotes the vividness of the generated motions by reducing the cross-modal mapping uncertainty. The codebook is learned by self-reconstruction over real facial motions and thus embedded with realistic facial motion priors. Over the discrete motion space, a temporal autoregressive model is employed to sequentially synthesize facial motions from the input speech signal, which guarantees lip-sync as well as plausible facial expressions. We demonstrate that our approach outperforms current state-of-the-art methods both qualitatively and quantitatively. Also, a user study further justifies our superiority in perceptual quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xing_CodeTalker_Speech-Driven_3D_Facial_Animation_With_Discrete_Motion_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xing_CodeTalker_Speech-Driven_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02379 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xing_CodeTalker_Speech-Driven_3D_Facial_Animation_With_Discrete_Motion_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xing_CodeTalker_Speech-Driven_3D_Facial_Animation_With_Discrete_Motion_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Transferable Targeted Adversarial Examples | Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, Kui Ren | Transferability of adversarial examples is critical for black-box deep learning model attacks. While most existing studies focus on enhancing the transferability of untargeted adversarial attacks, few of them studied how to generate transferable targeted adversarial examples that can mislead models into predicting a specific class. Moreover, existing transferable targeted adversarial attacks usually fail to sufficiently characterize the target class distribution, thus suffering from limited transferability. In this paper, we propose the Transferable Targeted Adversarial Attack (TTAA), which can capture the distribution information of the target class from both label-wise and feature-wise perspectives, to generate highly transferable targeted adversarial examples. To this end, we design a generative adversarial training framework consisting of a generator to produce targeted adversarial examples, and feature-label dual discriminators to distinguish the generated adversarial examples from the target class images. Specifically, we design the label discriminator to guide the adversarial examples to learn label-related distribution information about the target class. Meanwhile, we design a feature discriminator, which extracts the feature-wise information with strong cross-model consistency, to enable the adversarial examples to learn the transferable distribution information. Furthermore, we introduce the random perturbation dropping to further enhance the transferability by augmenting the diversity of adversarial examples used in the training process. Experiments demonstrate that our method achieves excellent performance on the transferability of targeted adversarial examples. The targeted fooling rate reaches 95.13% when transferred from VGG-19 to DenseNet-121, which significantly outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Towards_Transferable_Targeted_Adversarial_Examples_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Transferable_Targeted_Adversarial_Examples_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Transferable_Targeted_Adversarial_Examples_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised Parametric Real-World Image Harmonization | Ke Wang, Michaël Gharbi, He Zhang, Zhihao Xia, Eli Shechtman | Learning-based image harmonization techniques are usually trained to undo synthetic global transformations, applied to a masked foreground in a single ground truth photo. This simulated data does not model many important appearance mismatches (illumination, object boundaries, etc.) between foreground and background in real composites, leading to models that do not generalize well and cannot model complex local changes. We propose a new semi-supervised training strategy that addresses this problem and lets us learn complex local appearance harmonization from unpaired real composites, where foreground and background come from different images. Our model is fully parametric. It uses RGB curves to correct the global colors and tone and a shading map to model local variations. Our approach outperforms previous work on established benchmarks and real composites, as shown in a user study, and processes high-resolution images interactively. The code and project page is available at https://kewang0622.github.io/sprih/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Semi-Supervised_Parametric_Real-World_Image_Harmonization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Semi-Supervised_Parametric_Real-World_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00157 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Semi-Supervised_Parametric_Real-World_Image_Harmonization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Semi-Supervised_Parametric_Real-World_Image_Harmonization_CVPR_2023_paper.html | CVPR 2023 | null |
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation | Nazmul Karim, Niluthpol Chowdhury Mithun, Abhinav Rajvanshi, Han-pang Chiu, Supun Samarasekera, Nazanin Rahnavard | Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a labeled source domain to an unlabeled target domain. In contrast to UDA, source-free domain adaptation (SFDA) is a more practical setup as access to source data is no longer required during adaptation. Recent state-of-the-art (SOTA) methods on SFDA mostly focus on pseudo-label refinement based self-training which generally suffers from two issues: i) inevitable occurrence of noisy pseudo-labels that could lead to early training time memorization, ii) refinement process requires maintaining a memory bank which creates a significant burden in resource constraint scenarios. To address these concerns, we propose C-SFDA, a curriculum learning aided self-training framework for SFDA that adapts efficiently and reliably to changes across domains based on selective pseudo-labeling. Specifically, we employ a curriculum learning scheme to promote learning from a restricted amount of pseudo labels selected based on their reliabilities. This simple yet effective step successfully prevents label noise propagation during different stages of adaptation and eliminates the need for costly memory-bank based label refinement. Our extensive experimental evaluations on both image recognition and semantic segmentation tasks confirm the effectiveness of our method. C-SFDA is also applicable to online test-time domain adaptation and outperforms previous SOTA methods in this task. | https://openaccess.thecvf.com/content/CVPR2023/papers/Karim_C-SFDA_A_Curriculum_Learning_Aided_Self-Training_Framework_for_Efficient_Source_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Karim_C-SFDA_A_Curriculum_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Karim_C-SFDA_A_Curriculum_Learning_Aided_Self-Training_Framework_for_Efficient_Source_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Karim_C-SFDA_A_Curriculum_Learning_Aided_Self-Training_Framework_for_Efficient_Source_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting | Ruichen Zheng, Peng Li, Haoqian Wang, Tao Yu | Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field--it not only resolves occlusion ambiguity in multiview feature aggregation, but can also be used to evaluate light attenuation for self-shadowed relighting. To enhance its training viability and efficiency, we discretize visibility onto a fixed set of sample directions and supply it with coupled geometric 3D depth feature and local 2D image feature. We further propose a novel rendering-inspired loss, namely TransferLoss, to implicitly enforce the alignment between visibility and occupancy field, enabling end-to-end joint training. Results and extensive experiments demonstrate the effectiveness of the proposed method, as it surpasses state-of-the-art in terms of reconstruction accuracy while achieving comparably accurate relighting to ray-traced ground truth. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_Learning_Visibility_Field_for_Detailed_3D_Human_Reconstruction_and_Relighting_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.11900 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Learning_Visibility_Field_for_Detailed_3D_Human_Reconstruction_and_Relighting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Learning_Visibility_Field_for_Detailed_3D_Human_Reconstruction_and_Relighting_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Zero-Shot Generalization and Robustness of Multi-Modal Models | Yunhao Ge, Jie Ren, Andrew Gallagher, Yuxiao Wang, Ming-Hsuan Yang, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao | Multi-modal image-text models such as CLIP and LiT have demonstrated impressive performance on image classification benchmarks and their zero-shot generalization ability is particularly exciting. While the top-5 zero-shot accuracies of these models are very high, the top-1 accuracies are much lower (over 25% gap in some cases). We investigate the reasons for this performance gap and find that many of the failure cases are caused by ambiguity in the text prompts. First, we develop a simple and efficient zero-shot post-hoc method to identify images whose top-1 prediction is likely to be incorrect, by measuring consistency of the predictions w.r.t. multiple prompts and image transformations. We show that our procedure better predicts mistakes, outperforming the popular max logit baseline on selective prediction tasks. Next, we propose a simple and efficient way to improve accuracy on such uncertain images by making use of the WordNet hierarchy; specifically we augment the original class by incorporating its parent and children from the semantic label hierarchy, and plug the augmentation into text prompts. We conduct experiments on both CLIP and LiT models with five different ImageNet- based datasets. For CLIP, our method improves the top-1 accuracy by 17.13% on the uncertain subset and 3.6% on the entire ImageNet validation set. We also show that our method improves across ImageNet shifted datasets, four other datasets, and other model architectures such as LiT. Our proposed method is hyperparameter-free, requires no additional model training and can be easily scaled to other large multi-modal architectures. Code is available at https://github.com/gyhandy/Hierarchy-CLIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ge_Improving_Zero-Shot_Generalization_and_Robustness_of_Multi-Modal_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ge_Improving_Zero-Shot_Generalization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.01758 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ge_Improving_Zero-Shot_Generalization_and_Robustness_of_Multi-Modal_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ge_Improving_Zero-Shot_Generalization_and_Robustness_of_Multi-Modal_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Robustness of Vision Transformers by Reducing Sensitivity To Patch Corruptions | Yong Guo, David Stutz, Bernt Schiele | Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur. Indeed, we find that the vulnerability mainly stems from the unstable self-attention mechanism, which is inherently built upon patch-based inputs and often becomes overly sensitive to the corruptions across patches. For example, when we only occlude a small number of patches with random noise (e.g., 10%), these patch corruptions would lead to severe accuracy drops and greatly distract intermediate attention layers. To address this, we propose a new training method that improves the robustness of transformers from a new perspective -- reducing sensitivity to patch corruptions (RSPC). Specifically, we first identify and occlude/corrupt the most vulnerable patches and then explicitly reduce sensitivity to them by aligning the intermediate features between clean and corrupted examples. We highlight that the construction of patch corruptions is learned adversarially to the following feature alignment process, which is particularly effective and essentially different from existing methods. In experiments, our RSPC greatly improves the stability of attention layers and consistently yields better robustness on various benchmarks, including CIFAR-10/100-C, ImageNet-A, ImageNet-C, and ImageNet-P. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Improving_Robustness_of_Vision_Transformers_by_Reducing_Sensitivity_To_Patch_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Improving_Robustness_of_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Improving_Robustness_of_Vision_Transformers_by_Reducing_Sensitivity_To_Patch_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Improving_Robustness_of_Vision_Transformers_by_Reducing_Sensitivity_To_Patch_CVPR_2023_paper.html | CVPR 2023 | null |
VecFontSDF: Learning To Reconstruct and Synthesize High-Quality Vector Fonts via Signed Distance Functions | Zeqing Xia, Bojun Xiong, Zhouhui Lian | Font design is of vital importance in the digital content design and modern printing industry. Developing algorithms capable of automatically synthesizing vector fonts can significantly facilitate the font design process. However, existing methods mainly concentrate on raster image generation, and only a few approaches can directly synthesize vector fonts. This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts using signed distance functions (SDFs). Specifically, based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves, which can be precisely converted to quadratic Bezier curves that are widely used in vector font products. In this manner, most image generation methods can be easily extended to synthesize vector fonts. Qualitative and quantitative experiments conducted on a publicly-available dataset demonstrate that our method obtains high-quality results on several tasks, including vector font reconstruction, interpolation, and few-shot vector font synthesis, markedly outperforming the state of the art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xia_VecFontSDF_Learning_To_Reconstruct_and_Synthesize_High-Quality_Vector_Fonts_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xia_VecFontSDF_Learning_To_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12675 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_VecFontSDF_Learning_To_Reconstruct_and_Synthesize_High-Quality_Vector_Fonts_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_VecFontSDF_Learning_To_Reconstruct_and_Synthesize_High-Quality_Vector_Fonts_via_CVPR_2023_paper.html | CVPR 2023 | null |
MSF: Motion-Guided Sequential Fusion for Efficient 3D Object Detection From Point Cloud Sequences | Chenhang He, Ruihuang Li, Yabin Zhang, Shuai Li, Lei Zhang | Point cloud sequences are commonly used to accurately detect 3D objects in applications such as autonomous driving. Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame. However, this inevitably leads to redundant computation since adjacent frames are highly correlated. In this paper, we propose an efficient Motion-guided Sequential Fusion (MSF) method, which exploits the continuity of object motion to mine useful sequential contexts for object detection in the current frame. We first generate 3D proposals on the current frame and propagate them to preceding frames based on the estimated velocities. The points-of-interest are then pooled from the sequence and encoded as proposal features. A novel Bidirectional Feature Aggregation (BiFA) module is further proposed to facilitate the interactions of proposal features across frames. Besides, we optimize the point cloud pooling by a voxel-based sampling technique so that millions of points can be processed in several milliseconds. The proposed MSF method achieves not only better efficiency than other multi-frame detectors but also leading accuracy, with 83.12% and 78.30% mAP on the LEVEL1 and LEVEL2 test sets of Waymo Open Dataset, respectively. Codes can be found at https://github.com/skyhehe123/MSF. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_MSF_Motion-Guided_Sequential_Fusion_for_Efficient_3D_Object_Detection_From_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08316 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_MSF_Motion-Guided_Sequential_Fusion_for_Efficient_3D_Object_Detection_From_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_MSF_Motion-Guided_Sequential_Fusion_for_Efficient_3D_Object_Detection_From_CVPR_2023_paper.html | CVPR 2023 | null |
Modeling the Distributional Uncertainty for Salient Object Detection Models | Xinyu Tian, Jing Zhang, Mochu Xiang, Yuchao Dai | Most of the existing salient object detection (SOD) models focus on improving the overall model performance, without explicitly explaining the discrepancy between the training and testing distributions. In this paper, we investigate a particular type of epistemic uncertainty, namely distributional uncertainty, for salient object detection. Specifically, for the first time, we explore the existing class-aware distribution gap exploration techniques, i.e. long-tail learning, single-model uncertainty modeling and test-time strategies, and adapt them to model the distributional uncertainty for our class-agnostic task. We define test sample that is dissimilar to the training dataset as being "out-of-distribution" (OOD) samples. Different from the conventional OOD definition, where OOD samples are those not belonging to the closed-world training categories, OOD samples for SOD are those break the basic priors of saliency, i.e. center prior, color contrast prior, compactness prior and etc., indicating OOD as being "continuous" instead of being discrete for our task. We've carried out extensive experimental results to verify effectiveness of existing distribution gap modeling techniques for SOD, and conclude that both train-time single-model uncertainty estimation techniques and weight-regularization solutions that preventing model activation from drifting too much are promising directions for modeling distributional uncertainty for SOD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_Modeling_the_Distributional_Uncertainty_for_Salient_Object_Detection_Models_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Modeling_the_Distributional_Uncertainty_for_Salient_Object_Detection_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Modeling_the_Distributional_Uncertainty_for_Salient_Object_Detection_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Kernel Aware Resampler | Michael Bernasconi, Abdelaziz Djelouah, Farnood Salehi, Markus Gross, Christopher Schroers | Deep learning based methods for super-resolution have become state-of-the-art and outperform traditional approaches by a significant margin. From the initial models designed for fixed integer scaling factors (e.g. x2 or x4), efforts were made to explore different directions such as modeling blur kernels or addressing non-integer scaling factors. However, existing works do not provide a sound framework to handle them jointly. In this paper we propose a framework for generic image resampling that not only addresses all the above mentioned issues but extends the sets of possible transforms from upscaling to generic transforms. A key aspect to unlock these capabilities is the faithful modeling of image warping and changes of the sampling rate during the training data preparation. This allows a localized representation of the implicit image degradation that takes into account the reconstruction kernel, the local geometric distortion and the anti-aliasing kernel. Using this spatially variant degradation map as conditioning for our resampling model, we can address with the same model both global transformations, such as upscaling or rotation, and locally varying transformations such lens distortion or undistortion. Another important contribution is the automatic estimation of the degradation map in this more complex resampling setting (i.e. blind image resampling). Finally, we show that state-of-the-art results can be achieved by predicting kernels to apply on the input image instead of direct color prediction. This renders our model applicable for different types of data not seen during the training such as normals. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bernasconi_Kernel_Aware_Resampler_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bernasconi_Kernel_Aware_Resampler_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bernasconi_Kernel_Aware_Resampler_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bernasconi_Kernel_Aware_Resampler_CVPR_2023_paper.html | CVPR 2023 | null |
LaserMix for Semi-Supervised LiDAR Semantic Segmentation | Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu | Densely annotating LiDAR point clouds is costly, which often restrains the scalability of fully-supervised learning methods. In this work, we study the underexplored semi-supervised learning (SSL) in LiDAR semantic segmentation. Our core idea is to leverage the strong spatial cues of LiDAR point clouds to better exploit unlabeled data. We propose LaserMix to mix laser beams from different LiDAR scans and then encourage the model to make consistent and confident predictions before and after mixing. Our framework has three appealing properties. 1) Generic: LaserMix is agnostic to LiDAR representations (e.g., range view and voxel), and hence our SSL framework can be universally applied. 2) Statistically grounded: We provide a detailed analysis to theoretically explain the applicability of the proposed framework. 3) Effective: Comprehensive experimental analysis on popular LiDAR segmentation datasets (nuScenes, SemanticKITTI, and ScribbleKITTI) demonstrates our effectiveness and superiority. Notably, we achieve competitive results over fully-supervised counterparts with 2x to 5x fewer labels and improve the supervised-only baseline significantly by relatively 10.8%. We hope this concise yet high-performing framework could facilitate future research in semi-supervised LiDAR segmentation. Code is publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kong_LaserMix_for_Semi-Supervised_LiDAR_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kong_LaserMix_for_Semi-Supervised_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.00026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_LaserMix_for_Semi-Supervised_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_LaserMix_for_Semi-Supervised_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning | James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla, Donghyun Kim, Assaf Arbelle, Rameswar Panda, Rogerio Feris, Zsolt Kira | Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data. Typical solutions for this continual learning problem require extensive rehearsal of previously seen data, which increases memory costs and may violate data privacy. Recently, the emergence of large-scale pre-trained vision transformer models has enabled prompting approaches as an alternative to data-rehearsal. These approaches rely on a key-query mechanism to generate prompts and have been found to be highly resistant to catastrophic forgetting in the well-established rehearsal-free continual learning setting. However, the key mechanism of these methods is not trained end-to-end with the task sequence. Our experiments show that this leads to a reduction in their plasticity, hence sacrificing new task accuracy, and inability to benefit from expanded parameter capacity. We instead propose to learn a set of prompt components which are assembled with input-conditioned weights to produce input-conditioned prompts, resulting in a novel attention-based end-to-end key-query scheme. Our experiments show that we outperform the current SOTA method DualPrompt on established benchmarks by as much as 4.5% in average final accuracy. We also outperform the state of art by as much as 4.4% accuracy on a continual learning benchmark which contains both class-incremental and domain-incremental task shifts, corresponding to many practical settings. Our code is available at https://github.com/GT-RIPL/CODA-Prompt | https://openaccess.thecvf.com/content/CVPR2023/papers/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Smith_CODA-Prompt_COntinual_Decomposed_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
HypLiLoc: Towards Effective LiDAR Pose Regression With Hyperbolic Fusion | Sijie Wang, Qiyu Kang, Rui She, Wei Wang, Kai Zhao, Yang Song, Wee Peng Tay | LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high computation storage costs and can lead to globally inaccurate pose estimations if the database is too sparse. On the other hand, pose regression methods take images or point clouds as inputs and directly regress global poses in an end-to-end manner. They do not perform database matching and are more computationally efficient than retrieval techniques. We propose HypLiLoc, a new model for LiDAR pose regression. We use two branched backbones to extract 3D features and 2D projection features, respectively. We consider multi-modal feature fusion in both Euclidean and hyperbolic spaces to obtain more effective feature representations. Experimental results indicate that HypLiLoc achieves state-of-the-art performance in both outdoor and indoor datasets. We also conduct extensive ablation studies on the framework design, which demonstrate the effectiveness of multi-modal feature extraction and multi-space embedding. Our code is released at: https://github.com/sijieaaa/HypLiLoc | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_HypLiLoc_Towards_Effective_LiDAR_Pose_Regression_With_Hyperbolic_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_HypLiLoc_Towards_Effective_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_HypLiLoc_Towards_Effective_LiDAR_Pose_Regression_With_Hyperbolic_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_HypLiLoc_Towards_Effective_LiDAR_Pose_Regression_With_Hyperbolic_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
Complementary Intrinsics From Neural Radiance Fields and CNNs for Outdoor Scene Relighting | Siqi Yang, Xuanning Cui, Yongjie Zhu, Jiajun Tang, Si Li, Zhaofei Yu, Boxin Shi | Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multi-view stereo. With neural radiance fields (NeRFs), editing the appearance code could produce more realistic results without explicitly interpreting the outdoor scene image formation. This paper proposes to complement the intrinsic estimation from volume rendering using NeRFs and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Complementary_Intrinsics_From_Neural_Radiance_Fields_and_CNNs_for_Outdoor_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Complementary_Intrinsics_From_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Complementary_Intrinsics_From_Neural_Radiance_Fields_and_CNNs_for_Outdoor_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Complementary_Intrinsics_From_Neural_Radiance_Fields_and_CNNs_for_Outdoor_CVPR_2023_paper.html | CVPR 2023 | null |
Real-Time Multi-Person Eyeblink Detection in the Wild for Untrimmed Video | Wenzheng Zeng, Yang Xiao, Sicheng Wei, Jinfang Gan, Xintao Zhang, Zhiguo Cao, Zhiwen Fang, Joey Tianyi Zhou | Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from unconstrained films to reveal "in the wild" characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counterparts, our proposition runs in a one-stage spatio-temporal way with an end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_Real-Time_Multi-Person_Eyeblink_Detection_in_the_Wild_for_Untrimmed_Video_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.16053 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Real-Time_Multi-Person_Eyeblink_Detection_in_the_Wild_for_Untrimmed_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Real-Time_Multi-Person_Eyeblink_Detection_in_the_Wild_for_Untrimmed_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Category Query Learning for Human-Object Interaction Classification | Chi Xie, Fangao Zeng, Yue Hu, Shuang Liang, Yichen Wei | Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning. Such queries are explicitly associated to interaction categories, converted to image specific category representation via a transformer decoder, and learnt via an auxiliary image-level classification task. This idea is motivated by an earlier multi-label image classification method, but is for the first time applied for the challenging human-object interaction classification task. Our method is simple, general and effective. It is validated on three representative HOI baselines and achieves new state-of-the-art results on two benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Category_Query_Learning_for_Human-Object_Interaction_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Category_Query_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14005 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Category_Query_Learning_for_Human-Object_Interaction_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Category_Query_Learning_for_Human-Object_Interaction_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
MDQE: Mining Discriminative Query Embeddings To Segment Occluded Instances on Challenging Videos | Minghan Li, Shuai Li, Wangmeng Xiang, Lei Zhang | While impressive progress has been achieved, video instance segmentation (VIS) methods with per-clip input often fail on challenging videos with occluded objects and crowded scenes. This is mainly because instance queries in these methods cannot encode well the discriminative embeddings of instances, making the query-based segmenter difficult to distinguish those 'hard' instances. To address these issues, we propose to mine discriminative query embeddings (MDQE) to segment occluded instances on challenging videos. First, we initialize the positional embeddings and content features of object queries by considering their spatial contextual information and the inter-frame object motion. Second, we propose an inter-instance mask repulsion loss to distance each instance from its nearby non-target instances. The proposed MDQE is the first VIS method with per-clip input that achieves state-of-the-art results on challenging videos and competitive performance on simple videos. In specific, MDQE with ResNet50 achieves 33.0% and 44.5% mask AP on OVIS and YouTube-VIS 2021, respectively. Code of MDQE can be found at https://github.com/MinghanLi/MDQE_CVPR2023. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_MDQE_Mining_Discriminative_Query_Embeddings_To_Segment_Occluded_Instances_on_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_MDQE_Mining_Discriminative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14395 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MDQE_Mining_Discriminative_Query_Embeddings_To_Segment_Occluded_Instances_on_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MDQE_Mining_Discriminative_Query_Embeddings_To_Segment_Occluded_Instances_on_CVPR_2023_paper.html | CVPR 2023 | null |
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark | Xiaofeng Wang, Zheng Zhu, Yunpeng Zhang, Guan Huang, Yun Ye, Wenbo Xu, Ziwei Chen, Xingang Wang | In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal researches and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the online evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. The ASAP benchmark will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Are_We_Ready_for_Vision-Centric_Driving_Streaming_Perception_The_ASAP_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Are_We_Ready_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08914 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Are_We_Ready_for_Vision-Centric_Driving_Streaming_Perception_The_ASAP_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Are_We_Ready_for_Vision-Centric_Driving_Streaming_Perception_The_ASAP_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Model-Based Face Reconstruction Through Weakly-Supervised Outlier Segmentation | Chunlu Li, Andreas Morel-Forster, Thomas Vetter, Bernhard Egger, Adam Kortylewski | In this work, we aim to enhance model-based face reconstruction by avoiding fitting the model to outliers, i.e. regions that cannot be well-expressed by the model such as occluders or make-up. The core challenge for localizing outliers is that they are highly variable and difficult to annotate. To overcome this challenging problem, we introduce a joint Face-autoencoder and outlier segmentation approach (FOCUS).In particular, we exploit the fact that the outliers cannot be fitted well by the face model and hence can be localized well given a high-quality model fitting. The main challenge is that the model fitting and the outlier segmentation are mutually dependent on each other, and need to be inferred jointly. We resolve this chicken-and-egg problem with an EM-type training strategy, where a face autoencoder is trained jointly with an outlier segmentation network. This leads to a synergistic effect, in which the segmentation network prevents the face encoder from fitting to the outliers, enhancing the reconstruction quality. The improved 3D face reconstruction, in turn, enables the segmentation network to better predict the outliers. To resolve the ambiguity between outliers and regions that are difficult to fit, such as eyebrows, we build a statistical prior from synthetic data that measures the systematic bias in model fitting. Experiments on the NoW testset demonstrate that FOCUS achieves SOTA 3D face reconstruction performance among all baselines that are trained without 3D annotation. Moreover, our results on CelebA-HQ and the AR database show that the segmentation network can localize occluders accurately despite being trained without any segmentation annotation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Robust_Model-Based_Face_Reconstruction_Through_Weakly-Supervised_Outlier_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Robust_Model-Based_Face_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2106.09614 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Robust_Model-Based_Face_Reconstruction_Through_Weakly-Supervised_Outlier_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Robust_Model-Based_Face_Reconstruction_Through_Weakly-Supervised_Outlier_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation | Mengqi Huang, Zhendong Mao, Quan Wang, Yongdong Zhang | Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Not_All_Image_Regions_Matter_Masked_Vector_Quantization_for_Autoregressive_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Not_All_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Not_All_Image_Regions_Matter_Masked_Vector_Quantization_for_Autoregressive_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Not_All_Image_Regions_Matter_Masked_Vector_Quantization_for_Autoregressive_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-Supervised Video Representation Learning | Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Lu Yuan, Yu-Gang Jiang | Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations from scratch through reconstructing low-level features like raw pixel values. In this paper, we propose masked video distillation (MVD), a simple yet effective two-stage masked feature modeling framework for video representation learning: firstly we pretrain an image (or video) model by recovering low-level features of masked patches, then we use the resulting features as targets for masked feature modeling. For the choice of teacher models, we observe that students taught by video teachers perform better on temporally-heavy video tasks, while image teachers transfer stronger spatial representations for spatially-heavy video tasks. Visualization analysis also indicates different teachers produce different learned patterns for students. To leverage the advantage of different teachers, we design a spatial-temporal co-teaching method for MVD. Specifically, we distill student models from both video teachers and image teachers by masked feature modeling. Extensive experimental results demonstrate that video transformers pretrained with spatial-temporal co-teaching outperform models distilled with a single teacher on a multitude of video datasets. Our MVD with vanilla ViT achieves state-of-the-art performance compared with previous methods on several challenging video downstream tasks. For example, with the ViT-Large model, our MVD achieves 86.4% and 76.7% Top-1 accuracy on Kinetics-400 and Something-Something-v2, outperforming VideoMAE by 1.2% and 2.4% respectively. When a larger ViT-Huge model is adopted, MVD achieves the state-of-the-art performance with 77.3% Top-1 accuracy on Something-Something-v2. Code will be available at https://github.com/ruiwang2021/mvd. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Masked_Video_Distillation_Rethinking_Masked_Feature_Modeling_for_Self-Supervised_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Masked_Video_Distillation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04500 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Masked_Video_Distillation_Rethinking_Masked_Feature_Modeling_for_Self-Supervised_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Masked_Video_Distillation_Rethinking_Masked_Feature_Modeling_for_Self-Supervised_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Transformer-Based Unified Recognition of Two Hands Manipulating Objects | Hoseong Cho, Chanwoo Kim, Jihyeon Kim, Seongyeong Lee, Elkhan Ismayilzada, Seungryul Baek | Understanding the hand-object interactions from an egocentric video has received a great attention recently. So far, most approaches are based on the convolutional neural network (CNN) features combined with the temporal encoding via the long short-term memory (LSTM) or graph convolution network (GCN) to provide the unified understanding of two hands, an object and their interactions. In this paper, we propose the Transformer-based unified framework that provides better understanding of two hands manipulating objects. In our framework, we insert the whole image depicting two hands, an object and their interactions as input and jointly estimate 3 information from each frame: poses of two hands, pose of an object and object types. Afterwards, the action class defined by the hand-object interactions is predicted from the entire video based on the estimated information combined with the contact map that encodes the interaction between two hands and an object. Experiments are conducted on H2O and FPHA benchmark datasets and we demonstrated the superiority of our method achieving the state-of-the-art accuracy. Ablative studies further demonstrate the effectiveness of each proposed module. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_Transformer-Based_Unified_Recognition_of_Two_Hands_Manipulating_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cho_Transformer-Based_Unified_Recognition_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Transformer-Based_Unified_Recognition_of_Two_Hands_Manipulating_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Transformer-Based_Unified_Recognition_of_Two_Hands_Manipulating_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Azimuth Super-Resolution for FMCW Radar in Autonomous Driving | Yu-Jhe Li, Shawn Hunt, Jinhyung Park, Matthew O’Toole, Kris Kitani | We tackle the task of Azimuth (angular dimension) super-resolution for Frequency Modulated Continuous Wave (FMCW) multiple-input multiple-output (MIMO) radar. FMCW MIMO radar is widely used in autonomous driving alongside Lidar and RGB cameras. However, compared to Lidar, MIMO radar is usually of low resolution due to hardware size restrictions. For example, achieving 1-degree azimuth resolution requires at least 100 receivers, but a single MIMO device usually supports at most 12 receivers. Having limitations on the number of receivers is problematic since a high-resolution measurement of azimuth angle is essential for estimating the location and velocity of objects. To improve the azimuth resolution of MIMO radar, we propose a light, yet efficient, Analog-to-Digital super-resolution model (ADC-SR) that predicts or hallucinates additional radar signals using signals from only a few receivers. Compared with the baseline models that are applied to processed radar Range-Azimuth-Doppler (RAD) maps, we show that our ADC-SR method that processes raw ADC signals achieves comparable performance with 98% (50 times) fewer parameters. We also propose a hybrid super-resolution model (Hybrid-SR) combining our ADC-SR with a standard RAD super-resolution model, and show that performance can be improved by a large margin. Experiments on our City-Radar dataset and the RADIal dataset validate the importance of leveraging raw radar ADC signals. To assess the value of our super-resolution model for autonomous driving, we also perform object detection on the results of our super-resolution model and find that our super-resolution model improves detection performance by around 4% in mAP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Azimuth_Super-Resolution_for_FMCW_Radar_in_Autonomous_Driving_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Azimuth_Super-Resolution_for_FMCW_Radar_in_Autonomous_Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Azimuth_Super-Resolution_for_FMCW_Radar_in_Autonomous_Driving_CVPR_2023_paper.html | CVPR 2023 | null |
PDPP:Projected Diffusion for Procedure Planning in Instructional Videos | Hanlin Wang, Yilu Wu, Sheng Guo, Limin Wang | In this paper, we study the problem of procedure planning in instructional videos, which aims to make goal-directed plans given the current visual observations in unstructured real-life videos. Previous works cast this problem as a sequence planning problem and leverage either heavy intermediate visual observations or natural language instructions as supervision, resulting in complex learning schemes and expensive annotation costs. In contrast, we treat this problem as a distribution fitting problem. In this sense, we model the whole intermediate action sequence distribution with a diffusion model (PDPP), and thus transform the planning problem to a sampling process from this distribution. In addition, we remove the expensive intermediate supervision, and simply use task labels from instructional videos as supervision instead. Our model is a U-Net based diffusion model, which directly samples action sequences from the learned distribution with the given start and end observations. Furthermore, we apply an efficient projection method to provide accurate conditional guides for our model during the learning and sampling process. Experiments on three datasets with different scales show that our PDPP model can achieve the state-of-the-art performance on multiple metrics, even without the task supervision. Code and trained models are available at https://github.com/MCG-NJU/PDPP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_PDPPProjected_Diffusion_for_Procedure_Planning_in_Instructional_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_PDPPProjected_Diffusion_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14676 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PDPPProjected_Diffusion_for_Procedure_Planning_in_Instructional_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PDPPProjected_Diffusion_for_Procedure_Planning_in_Instructional_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving | Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, Renaud Marlet | Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usually benefit from fast computations and, when combined with techniques which use other point cloud representations, achieve state-of-the-art results. Today, projection-based methods leverage 2D CNNs but recent advances in computer vision show that vision transformers (ViTs) have achieved state-of-the-art results in many image-based benchmarks. In this work, we question if projection-based methods for 3D semantic segmentation can benefit from these latest improvements on ViTs. We answer positively but only after combining them with three key ingredients: (a) ViTs are notoriously hard to train and require a lot of training data to learn powerful representations. By preserving the same backbone architecture as for RGB images, we can exploit the knowledge from long training on large image collections that are much cheaper to acquire and annotate than point clouds. We reach our best results with pre-trained ViTs on large image datasets. (b) We compensate ViTs' lack of inductive bias by substituting a tailored convolutional stem for the classical linear embedding layer. (c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder. With these ingredients, we show that our method, called RangeViT, outperforms existing projection-based methods on nuScenes and SemanticKITTI. The code is available at https://github.com/valeoai/rangevit. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ando_RangeViT_Towards_Vision_Transformers_for_3D_Semantic_Segmentation_in_Autonomous_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ando_RangeViT_Towards_Vision_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.10222 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ando_RangeViT_Towards_Vision_Transformers_for_3D_Semantic_Segmentation_in_Autonomous_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ando_RangeViT_Towards_Vision_Transformers_for_3D_Semantic_Segmentation_in_Autonomous_CVPR_2023_paper.html | CVPR 2023 | null |
ProTeGe: Untrimmed Pretraining for Video Temporal Grounding by Video Temporal Grounding | Lan Wang, Gaurav Mittal, Sandra Sajeev, Ye Yu, Matthew Hall, Vishnu Naresh Boddeti, Mei Chen | Video temporal grounding (VTG) is the task of localizing a given natural language text query in an arbitrarily long untrimmed video. While the task involves untrimmed videos, all existing VTG methods leverage features from video backbones pretrained on trimmed videos. This is largely due to the lack of large-scale well-annotated VTG dataset to perform pretraining. As a result, the pretrained features lack a notion of temporal boundaries leading to the video-text alignment being less distinguishable between correct and incorrect locations. We present ProTeGe as the first method to perform VTG-based untrimmed pretraining to bridge the gap between trimmed pretrained backbones and downstream VTG tasks. ProTeGe reconfigures the HowTo100M dataset, with noisily correlated video-text pairs, into a VTG dataset and introduces a novel Video-Text Similarity-based Grounding Module and a pretraining objective to make pretraining robust to noise in HowTo100M. Extensive experiments on multiple datasets across downstream tasks with all variations of supervision validate that pretrained features from ProTeGe can significantly outperform features from trimmed pretrained backbones on VTG. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_ProTeGe_Untrimmed_Pretraining_for_Video_Temporal_Grounding_by_Video_Temporal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_ProTeGe_Untrimmed_Pretraining_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ProTeGe_Untrimmed_Pretraining_for_Video_Temporal_Grounding_by_Video_Temporal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ProTeGe_Untrimmed_Pretraining_for_Video_Temporal_Grounding_by_Video_Temporal_CVPR_2023_paper.html | CVPR 2023 | null |
VQACL: A Novel Visual Question Answering Continual Learning Setting | Xi Zhang, Feifei Zhang, Changsheng Xu | Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_VQACL_A_Novel_Visual_Question_Answering_Continual_Learning_Setting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_VQACL_A_Novel_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_VQACL_A_Novel_Visual_Question_Answering_Continual_Learning_Setting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_VQACL_A_Novel_Visual_Question_Answering_Continual_Learning_Setting_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Map Sparsification Based on 2D and 3D Discretized Grids | Xiaoyu Zhang, Yun-Hui Liu | Localization in a pre-built map is a basic technique for robot autonomous navigation. Existing mapping and localization methods commonly work well in small-scale environments. As a map grows larger, however, more memory is required and localization becomes inefficient. To solve these problems, map sparsification becomes a practical necessity to acquire a subset of the original map for localization. Previous map sparsification methods add a quadratic term in mixed-integer programming to enforce a uniform distribution of selected landmarks, which requires high memory capacity and heavy computation. In this paper, we formulate map sparsification in an efficient linear form and select uniformly distributed landmarks based on 2D discretized grids. Furthermore, to reduce the influence of different spatial distributions between the mapping and query sequences, which is not considered in previous methods, we also introduce a space constraint term based on 3D discretized grids. The exhaustive experiments in different datasets demonstrate the superiority of the proposed methods in both efficiency and localization performance. The relevant codes will be released at https://github.com/fishmarch/SLAM_Map_Compression. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Efficient_Map_Sparsification_Based_on_2D_and_3D_Discretized_Grids_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Efficient_Map_Sparsification_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10882 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Efficient_Map_Sparsification_Based_on_2D_and_3D_Discretized_Grids_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Efficient_Map_Sparsification_Based_on_2D_and_3D_Discretized_Grids_CVPR_2023_paper.html | CVPR 2023 | null |
High-Res Facial Appearance Capture From Polarized Smartphone Images | Dejan Azinović, Olivier Maury, Christophe Hery, Matthias Nießner, Justus Thies | We propose a novel method for high-quality facial texture reconstruction from RGB images using a novel capturing routine based on a single smartphone which we equip with an inexpensive polarization foil. Specifically, we turn the flashlight into a polarized light source and add a polarization filter on top of the camera. Leveraging this setup, we capture the face of a subject with cross-polarized and parallel-polarized light. For each subject, we record two short sequences in a dark environment under flash illumination with different light polarization using the modified smartphone. Based on these observations, we reconstruct an explicit surface mesh of the face using structure from motion. We then exploit the camera and light co-location within a differentiable renderer to optimize the facial textures using an analysis-by-synthesis approach. Our method optimizes for high-resolution normal textures, diffuse albedo, and specular albedo using a coarse-to-fine optimization scheme. We show that the optimized textures can be used in a standard rendering pipeline to synthesize high-quality photo-realistic 3D digital humans in novel environments. | https://openaccess.thecvf.com/content/CVPR2023/papers/Azinovic_High-Res_Facial_Appearance_Capture_From_Polarized_Smartphone_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Azinovic_High-Res_Facial_Appearance_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Azinovic_High-Res_Facial_Appearance_Capture_From_Polarized_Smartphone_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Azinovic_High-Res_Facial_Appearance_Capture_From_Polarized_Smartphone_Images_CVPR_2023_paper.html | CVPR 2023 | null |
JAWS: Just a Wild Shot for Cinematic Transfer in Neural Radiance Fields | Xi Wang, Robin Courant, Jinglei Shi, Eric Marchand, Marc Christie | This paper presents JAWS, an optimzation-driven approach that achieves the robust transfer of visual cinematic features from a reference in-the-wild video clip to a newly generated clip. To this end, we rely on an implicit-neural-representation (INR) in a way to compute a clip that shares the same cinematic features as the reference clip. We propose a general formulation of a camera optimization problem in an INR that computes extrinsic and intrinsic camera parameters as well as timing. By leveraging the differentiability of neural representations, we can back-propagate our designed cinematic losses measured on proxy estimators through a NeRF network to the proposed cinematic parameters directly. We also introduce specific enhancements such as guidance maps to improve the overall quality and efficiency. Results display the capacity of our system to replicate well known camera sequences from movies, adapting the framing, camera parameters and timing of the generated video clip to maximize the similarity with the reference clip. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_JAWS_Just_a_Wild_Shot_for_Cinematic_Transfer_in_Neural_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_JAWS_Just_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_JAWS_Just_a_Wild_Shot_for_Cinematic_Transfer_in_Neural_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_JAWS_Just_a_Wild_Shot_for_Cinematic_Transfer_in_Neural_CVPR_2023_paper.html | CVPR 2023 | null |
Class Attention Transfer Based Knowledge Distillation | Ziyao Guo, Haonan Yan, Hui Li, Xiaodong Lin | Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network. In this work, we focus on proposing a knowledge distillation method that has both high interpretability and competitive performance. We first revisit the structure of mainstream CNN models and reveal that possessing the capacity of identifying class discriminative regions of input is critical for CNN to perform classification. Furthermore, we demonstrate that this capacity can be obtained and enhanced by transferring class activation maps. Based on our findings, we propose class attention transfer based knowledge distillation (CAT-KD). Different from previous KD methods, we explore and present several properties of the knowledge transferred by our method, which not only improve the interpretability of CAT-KD but also contribute to a better understanding of CNN. While having high interpretability, CAT-KD achieves state-of-the-art performance on multiple benchmarks. Code is available at: https://github.com/GzyAftermath/CAT-KD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Class_Attention_Transfer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
EfficientSCI: Densely Connected Network With Space-Time Factorization for Large-Scale Video Snapshot Compressive Imaging | Lishun Wang, Miao Cao, Xin Yuan | Video snapshot compressive imaging (SCI) uses a two-dimensional detector to capture consecutive video frames during a single exposure time. Following this, an efficient reconstruction algorithm needs to be designed to reconstruct the desired video frames. Although recent deep learning-based state-of-the-art (SOTA) reconstruction algorithms have achieved good results in most tasks, they still face the following challenges due to excessive model complexity and GPU memory limitations: 1) these models need high computational cost, and 2) they are usually unable to reconstruct large-scale video frames at high compression ratios. To address these issues, we develop an efficient network for video SCI by using dense connections and space-time factorization mechanism within a single residual block, dubbed EfficientSCI. The EfficientSCI network can well establish spatial-temporal correlation by using convolution in the spatial domain and Transformer in the temporal domain, respectively. We are the first time to show that an UHD color video with high compression ratio can be reconstructed from a snapshot 2D measurement using a single end-to-end deep learning model with PSNR above 32 dB. Extensive results on both simulation and real data show that our method significantly outperforms all previous SOTA algorithms with better real-time performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_EfficientSCI_Densely_Connected_Network_With_Space-Time_Factorization_for_Large-Scale_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_EfficientSCI_Densely_Connected_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_EfficientSCI_Densely_Connected_Network_With_Space-Time_Factorization_for_Large-Scale_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_EfficientSCI_Densely_Connected_Network_With_Space-Time_Factorization_for_Large-Scale_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring Incompatible Knowledge Transfer in Few-Shot Image Generation | Yunqing Zhao, Chao Du, Milad Abdollahzadeh, Tianyu Pang, Min Lin, Shuicheng Yan, Ngai-Man Cheung | Few-shot image generation (FSIG) learns to generate diverse and high-fidelity images from a target domain using a few (e.g., 10) reference samples. Existing FSIG methods select, preserve and transfer prior knowledge from a source generator (pretrained on a related domain) to learn the target generator. In this work, we investigate an underexplored issue in FSIG, dubbed as incompatible knowledge transfer, which would significantly degrade the realisticness of synthetic samples. Empirical observations show that the issue stems from the least significant filters from the source generator. To this end, we propose knowledge truncation to mitigate this issue in FSIG, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method. Extensive experiments show that knowledge truncation is simple and effective, consistently achieving state-of-the-art performance, including challenging setups where the source and target domains are more distant. Project Page: https://yunqing-me.github.io/RICK. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Exploring_Incompatible_Knowledge_Transfer_in_Few-Shot_Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Exploring_Incompatible_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.07574 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Exploring_Incompatible_Knowledge_Transfer_in_Few-Shot_Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Exploring_Incompatible_Knowledge_Transfer_in_Few-Shot_Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Temporally Consistent Online Depth Estimation Using Point-Based Fusion | Numair Khan, Eric Penner, Douglas Lanman, Lei Xiao | Depth estimation is an important step in many computer vision problems such as 3D reconstruction, novel view synthesis, and computational photography. Most existing work focuses on depth estimation from single frames. When applied to videos, the result lacks temporal consistency, showing flickering and swimming artifacts. In this paper we aim to estimate temporally consistent depth maps of video streams in an online setting. This is a difficult problem as future frames are not available and the method must choose between enforcing consistency and correcting errors from previous estimations. The presence of dynamic objects further complicates the problem. We propose to address these challenges by using a global point cloud that is dynamically updated each frame, along with a learned fusion approach in image space. Our approach encourages consistency while simultaneously allowing updates to handle errors and dynamic objects. Qualitative and quantitative results show that our method achieves state-of-the-art quality for consistent video depth estimation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Khan_Temporally_Consistent_Online_Depth_Estimation_Using_Point-Based_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Khan_Temporally_Consistent_Online_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.07435 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Khan_Temporally_Consistent_Online_Depth_Estimation_Using_Point-Based_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Khan_Temporally_Consistent_Online_Depth_Estimation_Using_Point-Based_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
Generalizable Implicit Neural Representations via Instance Pattern Composers | Chiheon Kim, Doyup Lee, Saehoon Kim, Minsu Cho, Wook-Shin Han | Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based MLP to represent complex data instances by modulating only a small set of weights in an early MLP layer as an instance pattern composer; the remaining MLP weights learn pattern composition rules to learn common representations across instances. Our generalizable INR framework is fully compatible with existing meta-learning and hypernetworks in learning to predict the modulated weight for unseen instances. Extensive experiments demonstrate that our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Generalizable_Implicit_Neural_Representations_via_Instance_Pattern_Composers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Generalizable_Implicit_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13223 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Generalizable_Implicit_Neural_Representations_via_Instance_Pattern_Composers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Generalizable_Implicit_Neural_Representations_via_Instance_Pattern_Composers_CVPR_2023_paper.html | CVPR 2023 | null |
MotionTrack: Learning Robust Short-Term and Long-Term Motions for Multi-Object Tracking | Zheng Qin, Sanping Zhou, Le Wang, Jinghai Duan, Gang Hua, Wei Tang | The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous trajectory for each target. Existing methods often learn reliable motion patterns to match the same target between adjacent frames and discriminative appearance features to re-identify the lost targets after a long period. However, the reliability of motion prediction and the discriminability of appearances can be easily hurt by dense crowds and extreme occlusions in the tracking process. In this paper, we propose a simple yet effective multi-object tracker, i.e., MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection. Our Interaction Module and Refind Module are embedded in the well-known tracking-by-detection paradigm, which can work in tandem to maintain superior performance. Extensive experimental results on MOT17 and MOT20 datasets demonstrate the superiority of our approach in challenging scenarios, and it achieves state-of-the-art performances at various MOT metrics. We will make the code and trained models publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_MotionTrack_Learning_Robust_Short-Term_and_Long-Term_Motions_for_Multi-Object_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_MotionTrack_Learning_Robust_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10404 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_MotionTrack_Learning_Robust_Short-Term_and_Long-Term_Motions_for_Multi-Object_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_MotionTrack_Learning_Robust_Short-Term_and_Long-Term_Motions_for_Multi-Object_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
3D Registration With Maximal Cliques | Xiyu Zhang, Jiaqi Yang, Shikun Zhang, Yanning Zhang | As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration method with maximal cliques (MAC). The key insight is to loosen the previous maximum clique constraint, and to mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each of which represents a consensus set. We perform node-guided clique selection then, where each node corresponds to the maximal clique with the greatest graph weight. 3) Transformation hypotheses are computed for the selected cliques by SVD algorithm and the best hypothesis is used to perform registration. Extensive experiments on U3M, 3DMatch, 3DLoMatch and KITTI demonstrate that MAC effectively increases registration accuracy, outperforms various state-of-the-art methods and boosts the performance of deep-learned methods. MAC combined with deep-learned methods achieves state-of-the-art registration recall of 95.7% / 78.9% on the 3DMatch / 3DLoMatch. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_3D_Registration_With_Maximal_Cliques_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_3D_Registration_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_3D_Registration_With_Maximal_Cliques_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_3D_Registration_With_Maximal_Cliques_CVPR_2023_paper.html | CVPR 2023 | null |
What Can Human Sketches Do for Object Detection? | Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Subhadeep Koley, Tao Xiang, Yi-Zhe Song | Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundamental vision task of object detection. The end result is a sketch-enabled object detection framework that detects based on what you sketch -- that "zebra" (e.g., one that is eating the grass) in a herd of zebras (instance-aware detection), and only the part (e.g., "head" of a "zebra") that you desire (part-aware detection). We further dictate that our model works without (i) knowing which category to expect at testing (zero-shot) and (ii) not requiring additional bounding boxes (as per fully supervised) and class labels (as per weakly supervised). Instead of devising a model from the ground up, we show an intuitive synergy between foundation models (e.g., CLIP) and existing sketch models build for sketch-based image retrieval (SBIR), which can already elegantly solve the task -- CLIP to provide model generalisation, and SBIR to bridge the (sketch->photo) gap. In particular, we first perform independent prompting on both sketch and photo branches of an SBIR model to build highly generalisable sketch and photo encoders on the back of the generalisation ability of CLIP. We then devise a training paradigm to adapt the learned encoders for object detection, such that the region embeddings of detected boxes are aligned with the sketch and photo embeddings from SBIR. Evaluating our framework on standard object detection datasets like PASCAL-VOC and MS-COCO outperforms both supervised (SOD) and weakly-supervised object detectors (WSOD) on zero-shot setups. Project Page: https://pinakinathc.github.io/sketch-detect | https://openaccess.thecvf.com/content/CVPR2023/papers/Chowdhury_What_Can_Human_Sketches_Do_for_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chowdhury_What_Can_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chowdhury_What_Can_Human_Sketches_Do_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chowdhury_What_Can_Human_Sketches_Do_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Identity-Preserving Talking Face Generation With Landmark and Appearance Priors | Weizhi Zhong, Chaowei Fang, Yinqi Cai, Pengxu Wei, Gangming Zhao, Liang Lin, Guanbin Li | Generating talking face videos from audio attracts lots of research interest. A few person-specific methods can generate vivid videos but require the target speaker's videos for training or fine-tuning. Existing person-generic methods have difficulty in generating realistic and lip-synced videos while preserving identity information. To tackle this problem, we propose a two-stage framework consisting of audio-to-landmark generation and landmark-to-video rendering procedures. First, we devise a novel Transformer-based landmark generator to infer lip and jaw landmarks from the audio. Prior landmark characteristics of the speaker's face are employed to make the generated landmarks coincide with the facial outline of the speaker. Then, a video rendering model is built to translate the generated landmarks into face images. During this stage, prior appearance information is extracted from the lower-half occluded target face and static reference images, which helps generate realistic and identity-preserving visual content. For effectively exploring the prior information of static reference images, we align static reference images with the target face's pose and expression based on motion fields. Moreover, auditory features are reused to guarantee that the generated face images are well synchronized with the audio. Extensive experiments demonstrate that our method can produce more realistic, lip-synced, and identity-preserving videos than existing person-generic talking face generation methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhong_Identity-Preserving_Talking_Face_Generation_With_Landmark_and_Appearance_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhong_Identity-Preserving_Talking_Face_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.08293 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhong_Identity-Preserving_Talking_Face_Generation_With_Landmark_and_Appearance_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhong_Identity-Preserving_Talking_Face_Generation_With_Landmark_and_Appearance_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
All-in-One Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations | Dongwon Park, Byung Hyun Lee, Se Young Chun | Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time. To mitigate this issue, image restorations for known and unknown multiple degradations have recently been investigated, showing promising results, but require large networks or have sub-optimal architectures for potential interference among different degradations. Here, inspired by the filter attribution integrated gradients (FAIG), we propose an adaptive discriminative filter-based model for specific degradations (ADMS) to restore images with unknown degradations. Our method allows the network to contain degradation-dedicated filters only for about 3% of all network parameters per each degradation and to apply them adaptively via degradation classification (DC) to explicitly disentangle the network for multiple degradations. Our proposed method has demonstrated its effectiveness in comparison studies and achieved state-of-the-art performance in all-in-one image restoration benchmark datasets of both Rain-Noise-Blur and Rain-Snow-Haze. | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_All-in-One_Image_Restoration_for_Unknown_Degradations_Using_Adaptive_Discriminative_Filters_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_All-in-One_Image_Restoration_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_All-in-One_Image_Restoration_for_Unknown_Degradations_Using_Adaptive_Discriminative_Filters_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_All-in-One_Image_Restoration_for_Unknown_Degradations_Using_Adaptive_Discriminative_Filters_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Segmentation With Point Annotations for Histopathology Images via Contrast-Based Variational Model | Hongrun Zhang, Liam Burrows, Yanda Meng, Declan Sculthorpe, Abhik Mukherjee, Sarah E. Coupland, Ke Chen, Yalin Zheng | Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled 'novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models. Code is available at: https://github.com/hrzhang1123/CVM_WS_Segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Weakly_Supervised_Segmentation_With_Point_Annotations_for_Histopathology_Images_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Weakly_Supervised_Segmentation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03572 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Weakly_Supervised_Segmentation_With_Point_Annotations_for_Histopathology_Images_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Weakly_Supervised_Segmentation_With_Point_Annotations_for_Histopathology_Images_via_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient RGB-T Tracking via Cross-Modality Distillation | Tianlu Zhang, Hongyuan Guo, Qiang Jiao, Qiang Zhang, Jungong Han | Most current RGB-T trackers adopt a two-stream structure to extract unimodal RGB and thermal features and complex fusion strategies to achieve multi-modal feature fusion, which require a huge number of parameters, thus hindering their real-life applications. On the other hand, a compact RGB-T tracker may be computationally efficient but encounter non-negligible performance degradation, due to the weakening of feature representation ability. To remedy this situation, a cross-modality distillation framework is presented to bridge the performance gap between a compact tracker and a powerful tracker. Specifically, a specific-common feature distillation module is proposed to transform the modality-common information as well as the modality-specific information from a deeper two-stream network to a shallower single-stream network. In addition, a multi-path selection distillation module is proposed to instruct a simple fusion module to learn more accurate multi-modal information from a well-designed fusion mechanism by using multiple paths. We validate the effectiveness of our method with extensive experiments on three RGB-T benchmarks, which achieves state-of-the-art performance but consumes much less computational resources. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Efficient_RGB-T_Tracking_via_Cross-Modality_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Efficient_RGB-T_Tracking_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Efficient_RGB-T_Tracking_via_Cross-Modality_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Efficient_RGB-T_Tracking_via_Cross-Modality_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
MetaPortrait: Identity-Preserving Talking Head Generation With Fast Personalized Adaptation | Bowen Zhang, Chenyang Qi, Pan Zhang, Bo Zhang, HsiangTao Wu, Dong Chen, Qifeng Chen, Yong Wang, Fang Wen | In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects. First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. Second, inspired by face-swapping methods, we adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait. Although the proposed model surpasses prior generation fidelity on established benchmarks, personalized fine-tuning is still needed to further make the talking head generation qualified for real usage. However, this process is rather computationally demanding that is unaffordable to standard users. To alleviate this, we propose a fast adaptation model using a meta-learning approach. The learned model can be adapted to a high-quality personalized model as fast as 30 seconds. Last but not least, a spatial-temporal enhancement module is proposed to improve the fine details while ensuring temporal coherency. Extensive experiments prove the significant superiority of our approach over the state of the arts in both one-shot and personalized settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_MetaPortrait_Identity-Preserving_Talking_Head_Generation_With_Fast_Personalized_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_MetaPortrait_Identity-Preserving_Talking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08062 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MetaPortrait_Identity-Preserving_Talking_Head_Generation_With_Fast_Personalized_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_MetaPortrait_Identity-Preserving_Talking_Head_Generation_With_Fast_Personalized_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
UniHCP: A Unified Model for Human-Centric Perceptions | Yuanzheng Ci, Yizhou Wang, Meilin Chen, Shixiang Tang, Lei Bai, Feng Zhu, Rui Zhao, Fengwei Yu, Donglian Qi, Wanli Ouyang | Human-centric perceptions (e.g., pose estimation, human parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual models. While specific human-centric tasks have their own relevant semantic aspect to focus on, they also share the same underlying semantic structure of the human body. However, few works have attempted to exploit such homogeneity and design a general-propose model for human-centric tasks. In this work, we revisit a broad range of human-centric tasks and unify them in a minimalist manner. We propose UniHCP, a Unified Model for Human-Centric Perceptions, which unifies a wide range of human-centric tasks in a simplified end-to-end manner with the plain vision transformer architecture. With large-scale joint training on 33 humancentric datasets, UniHCP can outperform strong baselines on several in-domain and downstream tasks by direct evaluation. When adapted to a specific task, UniHCP achieves new SOTAs on a wide range of human-centric tasks, e.g., 69.8 mIoU on CIHP for human parsing, 86.18 mA on PA-100K for attribute prediction, 90.3 mAP on Market1501 for ReID, and 85.8 JI on CrowdHuman for pedestrian detection, performing better than specialized models tailored for each task. The code and pretrained model are available at https://github.com/OpenGVLab/UniHCP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ci_UniHCP_A_Unified_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02936 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023_paper.html | CVPR 2023 | null |
Passive Micron-Scale Time-of-Flight With Sunlight Interferometry | Alankar Kotwal, Anat Levin, Ioannis Gkioulekas | We introduce an interferometric technique for passive time-of-flight imaging and depth sensing at micrometer axial resolutions. Our technique uses a full-field Michelson interferometer, modified to use sunlight as the only light source. The large spectral bandwidth of sunlight makes it possible to acquire micrometer-resolution time-resolved scene responses, through a simple axial scanning operation. Additionally, the angular bandwidth of sunlight makes it possible to capture time-of-flight measurements insensitive to indirect illumination effects, such as interreflections and subsurface scattering. We build an experimental prototype that we operate outdoors, under direct sunlight, and in adverse environment conditions such as machine vibrations and vehicle traffic. We use this prototype to demonstrate, for the first time, passive imaging capabilities such as micrometer-scale depth sensing robust to indirect illumination, direct-only imaging, and imaging through diffusers. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kotwal_Passive_Micron-Scale_Time-of-Flight_With_Sunlight_Interferometry_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kotwal_Passive_Micron-Scale_Time-of-Flight_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10732 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kotwal_Passive_Micron-Scale_Time-of-Flight_With_Sunlight_Interferometry_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kotwal_Passive_Micron-Scale_Time-of-Flight_With_Sunlight_Interferometry_CVPR_2023_paper.html | CVPR 2023 | null |
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking | Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia | 3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we instead propose VoxelNext for fully sparse 3D object detection. Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies. Our strong sparse convolutional network VoxelNeXt detects and tracks 3D objects through voxel features entirely. It is an elegant and efficient framework, with no need for sparse-to-dense conversion or NMS post-processing. Our method achieves a better speed-accuracy trade-off than other mainframe detectors on the nuScenes dataset. For the first time, we show that a fully sparse voxel-based representation works decently for LIDAR 3D object detection and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2 benchmarks validate the effectiveness of our approach. Without bells and whistles, our model outperforms all existing LIDAR methods on the nuScenes tracking test benchmark. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_VoxelNeXt_Fully_Sparse_VoxelNet_for_3D_Object_Detection_and_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_VoxelNeXt_Fully_Sparse_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11301 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_VoxelNeXt_Fully_Sparse_VoxelNet_for_3D_Object_Detection_and_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_VoxelNeXt_Fully_Sparse_VoxelNet_for_3D_Object_Detection_and_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Behavioral Analysis of Vision-and-Language Navigation Agents | Zijiao Yang, Arjun Majumdar, Stefan Lee | To be successful, Vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on their surroundings. In this work, we develop a methodology to study agent behavior on a skill-specific basis -- examining how well existing agents ground instructions about stopping, turning, and moving towards specified objects or rooms. Our approach is based on generating skill-specific interventions and measuring changes in agent predictions. We present a detailed case study analyzing the behavior of a recent agent and then compare multiple agents in terms of skill-specific competency scores. This analysis suggests that biases from training have lasting effects on agent behavior and that existing models are able to ground simple referring expressions. Our comparisons between models show that skill-specific scores correlate with improvements in overall VLN task performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Behavioral_Analysis_of_Vision-and-Language_Navigation_Agents_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Behavioral_Analysis_of_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Behavioral_Analysis_of_Vision-and-Language_Navigation_Agents_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Behavioral_Analysis_of_Vision-and-Language_Navigation_Agents_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Generative Model Adaptation via Image-Specific Prompt Learning | Jiayi Guo, Chaofei Wang, You Wu, Eric Zhang, Kai Wang, Xingqian Xu, Shiji Song, Humphrey Shi, Gao Huang | Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain. It does not require any target-domain samples but only the textual domain labels. The training is highly efficient, e.g., a few minutes. However, existing methods still have some limitations in the quality of generated images and may suffer from the mode collapse issue. A key reason is that a fixed adaptation direction is applied for all cross-domain image pairs, which leads to identical supervision signals. To address this issue, we propose an Image-specific Prompt Learning (IPL) method, which learns specific prompt vectors for each source-domain image. This produces a more precise adaptation direction for every cross-domain image pair, endowing the target-domain generator with greatly enhanced flexibility. Qualitative and quantitative evaluations on various domains demonstrate that IPL effectively improves the quality and diversity of synthesized images and alleviates the mode collapse. Moreover, IPL is independent of the structure of the generative model, such as generative adversarial networks or diffusion models. Code is available at https://github.com/Picsart-AI-Research/IPL-Zero-Shot-Generative-Model-Adaptation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Zero-Shot_Generative_Model_Adaptation_via_Image-Specific_Prompt_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Zero-Shot_Generative_Model_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.03119 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Zero-Shot_Generative_Model_Adaptation_via_Image-Specific_Prompt_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Zero-Shot_Generative_Model_Adaptation_via_Image-Specific_Prompt_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
CelebV-Text: A Large-Scale Facial Text-Video Dataset | Jianhui Yu, Hao Zhu, Liming Jiang, Chen Change Loy, Weidong Cai, Wayne Wu | Text-driven generation models are flourishing in video generation and editing. However, face-centric text-to-video generation remains a challenge due to the lack of a suitable dataset containing high-quality videos and highly relevant texts. This paper presents CelebV-Text, a large-scale, diverse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to-video generation tasks. CelebV-Text comprises 70,000 in-the-wild face video clips with diverse visual content, each paired with 20 texts generated using the proposed semi-automatic text generation strategy. The provided texts are of high quality, describing both static and dynamic attributes precisely. The superiority of CelebV-Text over other datasets is demonstrated via comprehensive statistical analysis of the videos, texts, and text-video relevance. The effectiveness and potential of CelebV-Text are further shown through extensive self-evaluation. A benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task. All data and models are publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_CelebV-Text_A_Large-Scale_Facial_Text-Video_Dataset_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_CelebV-Text_A_Large-Scale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_CelebV-Text_A_Large-Scale_Facial_Text-Video_Dataset_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_CelebV-Text_A_Large-Scale_Facial_Text-Video_Dataset_CVPR_2023_paper.html | CVPR 2023 | null |
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures | Eugenia Iofinova, Alexandra Peste, Dan Alistarh | Pruning - that is, setting a significant subset of the parameters of a neural network to zero - is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or exacerbate bias in the output of the compressed model. Despite existing evidence for this phenomenon, the relationship between neural network pruning and induced bias is not well-understood. In this work, we systematically investigate and characterize this phenomenon in Convolutional Neural Networks for computer vision. First, we show that it is in fact possible to obtain highly-sparse models, e.g. with less than 10% remaining weights, which do not decrease in accuracy nor substantially increase in bias when compared to dense models. At the same time, we also find that, at higher sparsities, pruned models exhibit higher uncertainty in their outputs, as well as increased correlations, which we directly link to increased bias. We propose easy-to-use criteria which, based only on the uncompressed model, establish whether bias will increase with pruning, and identify the samples most susceptible to biased predictions post-compression. | https://openaccess.thecvf.com/content/CVPR2023/papers/Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Iofinova_Bias_in_Pruned_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12622 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023_paper.html | CVPR 2023 | null |
AttentionShift: Iteratively Estimated Part-Based Attention Map for Pointly Supervised Instance Segmentation | Mingxiang Liao, Zonghao Guo, Yuze Wang, Peng Yuan, Bailan Feng, Fang Wan | Pointly supervised instance segmentation (PSIS) learns to segment objects using a single point within the object extent as supervision. Challenged by the non-negligible semantic variance between object parts, however, the single supervision point causes semantic bias and false segmentation. In this study, we propose an AttentionShift method, to solve the semantic bias issue by iteratively decomposing the instance attention map to parts and estimating fine-grained semantics of each part. AttentionShift consists of two modules plugged on the vision transformer backbone: (i) token querying for pointly supervised attention map generation, and (ii) key-point shift, which re-estimates part-based attention maps by key-point filtering in the feature space. These two steps are iteratively performed so that the part-based attention maps are optimized spatially as well as in the feature space to cover full object extent. Experiments on PASCAL VOC and MS COCO 2017 datasets show that AttentionShift respectively improves the state-of-the-art of by 7.7% and 4.8% under [email protected], setting a solid PSIS baseline using vision transformer. Code is enclosed in the supplementary material. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liao_AttentionShift_Iteratively_Estimated_Part-Based_Attention_Map_for_Pointly_Supervised_Instance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liao_AttentionShift_Iteratively_Estimated_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_AttentionShift_Iteratively_Estimated_Part-Based_Attention_Map_for_Pointly_Supervised_Instance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_AttentionShift_Iteratively_Estimated_Part-Based_Attention_Map_for_Pointly_Supervised_Instance_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Volumetric Animation | Aliaksandr Siarohin, Willi Menapace, Ivan Skorokhodov, Kyle Olszewski, Jian Ren, Hsin-Ying Lee, Menglei Chai, Sergey Tulyakov | We propose a novel approach for unsupervised 3D animation of non-rigid deformable objects. Our method learns the 3D structure and dynamics of objects solely from single-view RGB videos, and can decompose them into semantically meaningful parts that can be tracked and animated. Using a 3D autodecoder framework, paired with a keypoint estimator via a differentiable PnP algorithm, our model learns the underlying object geometry and parts decomposition in an entirely unsupervised manner. This allows it to perform 3D segmentation, 3D keypoint estimation, novel view synthesis, and animation. We primarily evaluate the framework on two video datasets: VoxCeleb 256^2 and TEDXPeople 256^2. In addition, on the Cats 256^2 dataset, we show that it learns compelling 3D geometry even from raw image data. Finally, we show that our model can obtain animatable 3D objects from a singe or a few images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Siarohin_Unsupervised_Volumetric_Animation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Siarohin_Unsupervised_Volumetric_Animation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.11326 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Siarohin_Unsupervised_Volumetric_Animation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Siarohin_Unsupervised_Volumetric_Animation_CVPR_2023_paper.html | CVPR 2023 | null |
Hard Patches Mining for Masked Image Modeling | Haochen Wang, Kaiyou Song, Junsong Fan, Yuxi Wang, Jin Xie, Zhaoxiang Zhang | Masked image modeling (MIM) has attracted much research attention due to its promising potential for learning scalable visual representations. In typical approaches, models usually focus on predicting specific contents of masked patches, and their performances are highly related to pre-defined mask strategies. Intuitively, this procedure can be considered as training a student (the model) on solving given problems (predict masked patches). However, we argue that the model should not only focus on solving given problems, but also stand in the shoes of a teacher to produce a more challenging problem by itself. To this end, we propose Hard Patches Mining (HPM), a brand-new framework for MIM pre-training. We observe that the reconstruction loss can naturally be the metric of the difficulty of the pre-training task. Therefore, we introduce an auxiliary loss predictor, predicting patch-wise losses first and deciding where to mask next. It adopts a relative relationship learning strategy to prevent overfitting to exact reconstruction loss values. Experiments under various settings demonstrate the effectiveness of HPM in constructing masked images. Furthermore, we empirically find that solely introducing the loss prediction objective leads to powerful representations, verifying the efficacy of the ability to be aware of where is hard to reconstruct. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Hard_Patches_Mining_for_Masked_Image_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Hard_Patches_Mining_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05919 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Hard_Patches_Mining_for_Masked_Image_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Hard_Patches_Mining_for_Masked_Image_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
PlaneDepth: Self-Supervised Depth Estimation via Orthogonal Planes | Ruoyu Wang, Zehao Yu, Shenghua Gao | Multiple near frontal-parallel planes based depth representation demonstrated impressive results in self-supervised monocular depth estimation (MDE). Whereas, such a representation would cause the discontinuity of the ground as it is perpendicular to the frontal-parallel planes, which is detrimental to the identification of drivable space in autonomous driving. In this paper, we propose the PlaneDepth, a novel orthogonal planes based presentation, including vertical planes and ground planes. PlaneDepth estimates the depth distribution using a Laplacian Mixture Model based on orthogonal planes for an input image. These planes are used to synthesize a reference view to provide the self-supervision signal. Further, we find that the widely used resizing and cropping data augmentation breaks the orthogonality assumptions, leading to inferior plane predictions. We address this problem by explicitly constructing the resizing cropping transformation to rectify the predefined planes and predicted camera pose. Moreover, we propose an augmented self-distillation loss supervised with a bilateral occlusion mask to boost the robustness of orthogonal planes representation for occlusions. Thanks to our orthogonal planes representation, we can extract the ground plane in an unsupervised manner, which is important for autonomous driving. Extensive experiments on the KITTI dataset demonstrate the effectiveness and efficiency of our method. The code is available at https://github.com/svip-lab/PlaneDepth. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_PlaneDepth_Self-Supervised_Depth_Estimation_via_Orthogonal_Planes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_PlaneDepth_Self-Supervised_Depth_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.01612 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PlaneDepth_Self-Supervised_Depth_Estimation_via_Orthogonal_Planes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_PlaneDepth_Self-Supervised_Depth_Estimation_via_Orthogonal_Planes_CVPR_2023_paper.html | CVPR 2023 | null |
Diffusion-SDF: Text-To-Shape via Voxelized Diffusion | Muheng Li, Yueqi Duan, Jie Zhou, Jiwen Lu | With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e.g. text) has become a hot issue. In this paper, we propose a new generative 3D modeling framework called Diffusion-SDF for the challenging task of text-to-shape synthesis. Previous approaches lack flexibility in both 3D data representation and shape generation, thereby failing to generate highly diversified 3D shapes conforming to the given text descriptions. To address this, we propose a SDF autoencoder together with the Voxelized Diffusion model to learn and generate representations for voxelized signed distance fields (SDFs) of 3D shapes. Specifically, we design a novel UinU-Net architecture that implants a local-focused inner network inside the standard U-Net architecture, which enables better reconstruction of patch-independent SDF representations. We extend our approach to further text-to-shape tasks including text-conditioned shape completion and manipulation. Experimental results show that Diffusion-SDF generates both higher quality and more diversified 3D shapes that conform well to given text descriptions when compared to previous approaches. Code is available at: https://github.com/ttlmh/Diffusion-SDF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Diffusion-SDF_Text-To-Shape_via_Voxelized_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Diffusion-SDF_Text-To-Shape_via_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Diffusion-SDF_Text-To-Shape_via_Voxelized_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Diffusion-SDF_Text-To-Shape_via_Voxelized_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation | Ju He, Jieneng Chen, Ming-Xian Lin, Qihang Yu, Alan L. Yuille | In this work, we present a robust approach for joint part and object segmentation. Specifically, we reformulate object and part segmentation as an optimization problem and build a hierarchical feature representation including pixel, part, and object-level embeddings to solve it in a bottom-up clustering manner. Pixels are grouped into several clusters where the part-level embeddings serve as cluster centers. Afterwards, object masks are obtained by compositing the part proposals. This bottom-up interaction is shown to be effective in integrating information from lower semantic levels to higher semantic levels. Based on that, our novel approach Compositor produces part and object segmentation masks simultaneously while improving the mask quality. Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0.9% and 1.3% on PartImageNet, 0.4% and 1.7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4.4% and 7.1% on part and object respectively. | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Compositor_Bottom-Up_Clustering_and_Compositing_for_Robust_Part_and_Object_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Compositor_Bottom-Up_Clustering_and_Compositing_for_Robust_Part_and_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Compositor_Bottom-Up_Clustering_and_Compositing_for_Robust_Part_and_Object_CVPR_2023_paper.html | CVPR 2023 | null |
Semantic-Conditional Diffusion Networks for Image Captioning | Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Jianlin Feng, Hongyang Chao, Tao Mei | Recent advances on text-to-image generation have witnessed the rise of diffusion models which act as powerful generative models. Nevertheless, it is not trivial to exploit such latent variable models to capture the dependency among discrete words and meanwhile pursue complex visual-language alignment in image captioning. In this paper, we break the deeply rooted conventions in learning Transformer-based encoder-decoder, and propose a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net). Technically, for each input image, we first search the semantically relevant sentences via cross-modal retrieval model to convey the comprehensive semantic information. The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process. In SCD-Net, multiple Diffusion Transformer structures are stacked to progressively strengthen the output sentence with better visional-language alignment and linguistical coherence in a cascaded manner. Furthermore, to stabilize the diffusion process, a new self-critical sequence training strategy is designed to guide the learning of SCD-Net with the knowledge of a standard autoregressive Transformer model. Extensive experiments on COCO dataset demonstrate the promising potential of using diffusion models in the challenging image captioning task. Source code is available at https://github.com/YehLi/xmodaler/tree/master/configs/image_caption/scdnet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Semantic-Conditional_Diffusion_Networks_for_Image_Captioning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.03099 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Semantic-Conditional_Diffusion_Networks_for_Image_Captioning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Semantic-Conditional_Diffusion_Networks_for_Image_Captioning_CVPR_2023_paper.html | CVPR 2023 | null |
Unite and Conquer: Plug & Play Multi-Modal Synthesis Using Diffusion Models | Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel | Generating photos satisfying multiple constraints finds broad utility in the content creation industry. A key hurdle to accomplishing this task is the need for paired data consisting of all modalities (i.e., constraints) and their corresponding output. Moreover, existing methods need retraining using paired data across all modalities to introduce a new condition. This paper proposes a solution to this problem based on denoising diffusion probabilistic models (DDPMs). Our motivation for choosing diffusion models over other generative models comes from the flexible internal structure of diffusion models. Since each sampling step in the DDPM follows a Gaussian distribution, we show that there exists a closed-form solution for generating an image given various constraints. Our method can utilize a single diffusion model trained on multiple sub-tasks and improve the combined task through our proposed sampling strategy. We also introduce a novel reliability parameter that allows using different off-the-shelf diffusion models trained across various datasets during sampling time alone to guide it to the desired outcome satisfying multiple constraints. We perform experiments on various standard multimodal tasks to demonstrate the effectiveness of our approach. More details can be found at: https://nithin-gk.github.io/projectpages/Multidiff | https://openaccess.thecvf.com/content/CVPR2023/papers/Nair_Unite_and_Conquer_Plug__Play_Multi-Modal_Synthesis_Using_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nair_Unite_and_Conquer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00793 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nair_Unite_and_Conquer_Plug__Play_Multi-Modal_Synthesis_Using_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nair_Unite_and_Conquer_Plug__Play_Multi-Modal_Synthesis_Using_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning With Structure-Trajectory Prompted Reconstruction for Person Re-Identification | Haocong Rao, Chunyan Miao | Person re-identification (re-ID) via 3D skeleton data is an emerging topic with prominent advantages. Existing methods usually design skeleton descriptors with raw body joints or perform skeleton sequence representation learning. However, they typically cannot concurrently model different body-component relations, and rarely explore useful semantics from fine-grained representations of body joints. In this paper, we propose a generic Transformer-based Skeleton Graph prototype contrastive learning (TranSG) approach with structure-trajectory prompted reconstruction to fully capture skeletal relations and valuable spatial-temporal semantics from skeleton graphs for person re-ID. Specifically, we first devise the Skeleton Graph Transformer (SGT) to simultaneously learn body and motion relations within skeleton graphs, so as to aggregate key correlative node features into graph representations. Then, we propose the Graph Prototype Contrastive learning (GPC) to mine the most typical graph features (graph prototypes) of each identity, and contrast the inherent similarity between graph representations and different prototypes from both skeleton and sequence levels to learn discriminative graph representations. Last, a graph Structure-Trajectory Prompted Reconstruction (STPR) mechanism is proposed to exploit the spatial and temporal contexts of graph nodes to prompt skeleton graph reconstruction, which facilitates capturing more valuable patterns and graph semantics for person re-ID. Empirical evaluations demonstrate that TranSG significantly outperforms existing state-of-the-art methods. We further show its generality under different graph modeling, RGB-estimated skeletons, and unsupervised scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rao_TranSG_Transformer-Based_Skeleton_Graph_Prototype_Contrastive_Learning_With_Structure-Trajectory_Prompted_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rao_TranSG_Transformer-Based_Skeleton_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.06819 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rao_TranSG_Transformer-Based_Skeleton_Graph_Prototype_Contrastive_Learning_With_Structure-Trajectory_Prompted_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rao_TranSG_Transformer-Based_Skeleton_Graph_Prototype_Contrastive_Learning_With_Structure-Trajectory_Prompted_CVPR_2023_paper.html | CVPR 2023 | null |
All Are Worth Words: A ViT Backbone for Diffusion Models | Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu | Vision transformers (ViT) have shown promise in various vision tasks while the U-Net based on a convolutional neural network (CNN) remains dominant in diffusion models. We design a simple and general ViT-based architecture (named U-ViT) for image generation with diffusion models. U-ViT is characterized by treating all inputs including the time, condition and noisy image patches as tokens and employing long skip connections between shallow and deep layers. We evaluate U-ViT in unconditional and class-conditional image generation, as well as text-to-image generation tasks, where U-ViT is comparable if not superior to a CNN-based U-Net of a similar size. In particular, latent diffusion models with U-ViT achieve record-breaking FID scores of 2.29 in class-conditional image generation on ImageNet 256x256, and 5.48 in text-to-image generation on MS-COCO, among methods without accessing large external datasets during the training of generative models. Our results suggest that, for diffusion-based image modeling, the long skip connection is crucial while the down-sampling and up-sampling operators in CNN-based U-Net are not always necessary. We believe that U-ViT can provide insights for future research on backbones in diffusion models and benefit generative modeling on large scale cross-modality datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bao_All_Are_Worth_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.12152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ZBS: Zero-Shot Background Subtraction via Instance-Level Background Modeling and Foreground Selection | Yongqi An, Xu Zhao, Tao Yu, Haiyun Guo, Chaoyang Zhao, Ming Tang, Jinqiao Wang | Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories. In this work, we propose an unsupervised BGS algorithm based on zero-shot object detection called Zero-shot Background Subtraction ZBS. The proposed method fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. Based on it, the foreground can be effectively extracted by comparing the detection results of new frames with the background model. ZBS performs well for sophisticated scenarios, and it has rich and extensible categories. Furthermore, our method can easily generalize to other tasks, such as abandoned object detection in unseen environments. We experimentally show that ZBS surpasses state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is released at https://github.com/CASIA-IVA-Lab/ZBS. | https://openaccess.thecvf.com/content/CVPR2023/papers/An_ZBS_Zero-Shot_Background_Subtraction_via_Instance-Level_Background_Modeling_and_Foreground_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/An_ZBS_Zero-Shot_Background_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14679 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/An_ZBS_Zero-Shot_Background_Subtraction_via_Instance-Level_Background_Modeling_and_Foreground_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/An_ZBS_Zero-Shot_Background_Subtraction_via_Instance-Level_Background_Modeling_and_Foreground_CVPR_2023_paper.html | CVPR 2023 | null |
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