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Dual-Generator Face Reenactment | Gee-Sern Hsu, Chun-Hung Tsai, Hung-Yi Wu | We propose the Dual-Generator (DG) network for large-pose face reenactment. Given a source face and a reference face as inputs, the DG network can generate an output face that has the same pose and expression as of the reference face, and has the same identity as of the source face. As most approaches do not particularly consider large-pose reenactment, the proposed approach addresses this issue by incorporating a 3D landmark detector into the framework and considering a loss function to capture visible local shape variation across large pose. The DG network consists of two modules, the ID-preserving Shape Generator (IDSG) and the Reenacted Face Generator (RFG). The IDSG encodes the 3D landmarks of the reference face into a reference landmark code, and encodes the source face into a source face code. The reference landmark code and the source face code are concatenated and decoded to a set of target landmarks that exhibits the pose and expression of the reference face and preserves the identity of the source face. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hsu_Dual-Generator_Face_Reenactment_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hsu_Dual-Generator_Face_Reenactment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hsu_Dual-Generator_Face_Reenactment_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence | Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, Errui Ding | Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and downscaling degradation kernel. To improve real world applicability of such models, there are growing interests to develop models optimized for arbitrary upscaling factors. Our proposed method is the first to treat arbitrary rescaling, both upscaling and downscaling, as one unified process. Using joint optimization of both directions, the proposed model is able to learn upscaling and downscaling simultaneously and achieve bidirectional arbitrary image rescaling. It improves the performance of current arbitrary upscaling models by a large margin while at the same time learns to maintain visual perception quality in downscaled images. The proposed model is further shown to be robust in cycle idempotence test, free of severe degradations in reconstruction accuracy when the downscaling-to-upscaling cycle is applied repetitively. This robustness is beneficial for image rescaling in the wild when this cycle could be applied to one image for multiple times. It also performs well on tests with arbitrary large scales and asymmetric scales, even when the model is not trained with such tasks. Extensive experiments are conducted to demonstrate the superior performance of our model. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pan_Towards_Bidirectional_Arbitrary_Image_Rescaling_Joint_Optimization_and_Cycle_Idempotence_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.00911 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Towards_Bidirectional_Arbitrary_Image_Rescaling_Joint_Optimization_and_Cycle_Idempotence_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Towards_Bidirectional_Arbitrary_Image_Rescaling_Joint_Optimization_and_Cycle_Idempotence_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Neural Articulated Shape and Appearance Models | Fangyin Wei, Rohan Chabra, Lingni Ma, Christoph Lassner, Michael Zollhöfer, Szymon Rusinkiewicz, Chris Sweeney, Richard Newcombe, Mira Slavcheva | Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches has focused on static objects, dynamic objects, especially with controllable articulation, are less explored. We propose a novel approach for learning a representation of the geometry, appearance, and motion of a class of articulated objects given only a set of color images as input. In a self-supervised manner, our novel representation learns shape, appearance, and articulation codes that enable independent control of these semantic dimensions. Our model is trained end-to-end without requiring any articulation annotations. Experiments show that our approach performs well for different joint types, such as revolute and prismatic joints, as well as different combinations of these joints. Compared to state of the art that uses direct 3D supervision and does not output appearance, we recover more faithful geometry and appearance from 2D observations only. In addition, our representation enables a large variety of applications, such as few-shot reconstruction, the generation of novel articulations, and novel view-synthesis. Project page: https://weify627.github.io/nasam/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_Self-Supervised_Neural_Articulated_Shape_and_Appearance_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wei_Self-Supervised_Neural_Articulated_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.08525 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Self-Supervised_Neural_Articulated_Shape_and_Appearance_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Self-Supervised_Neural_Articulated_Shape_and_Appearance_Models_CVPR_2022_paper.html | CVPR 2022 | null |
A Hybrid Quantum-Classical Algorithm for Robust Fitting | Anh-Dzung Doan, Michele Sasdelli, David Suter, Tat-Jun Chin | Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds. It is therefore critical to develop novel approaches that can bridge the gap between exact solutions that are costly, and fast heuristics that offer no quality assurances. In this paper, we propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs and terminates with a global solution or an error bound. The combinatorial subproblems are amenable to a quantum annealer, which helps to tighten the bound efficiently. While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics. Moreover, our work represents a concrete application of quantum computing in computer vision. We present results obtained using an actual quantum computer (D-Wave Advantage) and via simulation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Doan_A_Hybrid_Quantum-Classical_Algorithm_for_Robust_Fitting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Doan_A_Hybrid_Quantum-Classical_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.10110 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Doan_A_Hybrid_Quantum-Classical_Algorithm_for_Robust_Fitting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Doan_A_Hybrid_Quantum-Classical_Algorithm_for_Robust_Fitting_CVPR_2022_paper.html | CVPR 2022 | null |
Topology Preserving Local Road Network Estimation From Single Onboard Camera Image | Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc Van Gool | Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furthermore, it needs to refer to the ground plane, where also the driving actions are taken. This paper aims at extracting the local road network topology, directly in the bird's-eye-view (BEV), all in a complex urban setting. The only input consists of a single onboard, forward looking camera image. We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points. To better capture topology, we introduce the concept of minimal cycles and their covers. A minimal cycle is the smallest cycle formed by the directed curve segments (between two intersections). The cover is a set of curves whose segments are involved in forming a minimal cycle. We first show that the covers suffice to uniquely represent the road topology. The covers are then used to supervise deep neural networks, along with the lane curve supervision. These learn to predict the road topology from a single input image. The results on the NuScenes and Argoverse benchmarks are significantly better than those obtained with baselines. Code: https://github.com/ybarancan/TopologicalLaneGraph. | https://openaccess.thecvf.com/content/CVPR2022/papers/Can_Topology_Preserving_Local_Road_Network_Estimation_From_Single_Onboard_Camera_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Can_Topology_Preserving_Local_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.10155 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Can_Topology_Preserving_Local_Road_Network_Estimation_From_Single_Onboard_Camera_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Can_Topology_Preserving_Local_Road_Network_Estimation_From_Single_Onboard_Camera_CVPR_2022_paper.html | CVPR 2022 | null |
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes | Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon, Chang-Su Kim | A novel algorithm to detect road lanes in the eigenlane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candidates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection network, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://github.com/dongkwonjin/Eigenlanes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Eigenlanes_Data-Driven_Lane_Descriptors_for_Structurally_Diverse_Lanes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Eigenlanes_Data-Driven_Lane_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Eigenlanes_Data-Driven_Lane_Descriptors_for_Structurally_Diverse_Lanes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Eigenlanes_Data-Driven_Lane_Descriptors_for_Structurally_Diverse_Lanes_CVPR_2022_paper.html | CVPR 2022 | null |
Human Instance Matting via Mutual Guidance and Multi-Instance Refinement | Yanan Sun, Chi-Keung Tang, Yu-Wing Tai | This paper introduces a new matting task called human instance matting (HIM), which requires the pertinent model to automatically predict a precise alpha matte for each human instance. Straightforward combination of closely related techniques, namely, instance segmentation, soft segmentation and human/conventional matting, will easily fail in complex cases requiring disentangling mingled colors belonging to multiple instances along hairy and thin boundary structures. To tackle these technical challenges, we propose a human instance matting framework, called InstMatt, where a novel mutual guidance strategy working in tandem with a multi-instance refinement module is used, for delineating multi-instance relationship among humans with complex and overlapping boundaries if present. A new instance matting metric called instance matting quality (IMQ) is proposed, which addresses the absence of a unified and fair means of evaluation emphasizing both instance recognition and mat-ting quality. Finally, we construct a HIM benchmark for evaluation, which comprises of both synthetic and natural benchmark images. In addition to thorough experimental results on HIM, preliminary results are presented on general instance matting beyond multiple and overlapping human instances. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Human_Instance_Matting_via_Mutual_Guidance_and_Multi-Instance_Refinement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Human_Instance_Matting_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.10767 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Human_Instance_Matting_via_Mutual_Guidance_and_Multi-Instance_Refinement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Human_Instance_Matting_via_Mutual_Guidance_and_Multi-Instance_Refinement_CVPR_2022_paper.html | CVPR 2022 | null |
TCTrack: Temporal Contexts for Aerial Tracking | Ziang Cao, Ziyuan Huang, Liang Pan, Shiwei Zhang, Ziwei Liu, Changhong Fu | Temporal contexts among consecutive frames are far from being fully utilized in existing visual trackers. In this work, we present TCTrack, a comprehensive framework to fully exploit temporal contexts for aerial tracking. The temporal contexts are incorporated at two levels: the extraction of features and the refinement of similarity maps. Specifically, for feature extraction, an online temporally adaptive convolution is proposed to enhance the spatial features using temporal information, which is achieved by dynamically calibrating the convolution weights according to the previous frames. For similarity map refinement, we propose an adaptive temporal transformer, which first effectively encodes temporal knowledge in a memory-efficient way, before the temporal knowledge is decoded for accurate adjustment of the similarity map. TCTrack is effective and efficient: evaluation on four aerial tracking benchmarks shows its impressive performance; real-world UAV tests show its high speed of over 27 FPS on NVIDIA Jetson AGX Xavier. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_TCTrack_Temporal_Contexts_for_Aerial_Tracking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_TCTrack_Temporal_Contexts_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_TCTrack_Temporal_Contexts_for_Aerial_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_TCTrack_Temporal_Contexts_for_Aerial_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing | Jing Shi, Ning Xu, Haitian Zheng, Alex Smith, Jiebo Luo, Chenliang Xu | Recently, large pretrained models (e.g., BERT, StyleGAN, CLIP) show great knowledge transfer and generalization capability on various downstream tasks within their domains. Inspired by these efforts, in this paper we propose a unified model for open-domain image editing focusing on color and tone adjustment of open-domain images while keeping their original content and structure. Our model learns a unified editing space that is more semantic, intuitive, and easy to manipulate than the operation space (e.g., contrast, brightness, color curve) used in many existing photo editing softwares. Our model belongs to the image-to-image translation framework which consists of an image encoder and decoder, and is trained on pairs of before-and-after edited images to produce multimodal outputs. We show that by inverting image pairs into latent codes of the learned editing space, our model can be leveraged for various downstream editing tasks such as language-guided image editing, personalized editing, editing-style clustering, retrieval, etc. We extensively study the unique properties of the editing space in experiments and demonstrate superior performance on the aforementioned tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_SpaceEdit_Learning_a_Unified_Editing_Space_for_Open-Domain_Image_Color_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_SpaceEdit_Learning_a_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_SpaceEdit_Learning_a_Unified_Editing_Space_for_Open-Domain_Image_Color_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_SpaceEdit_Learning_a_Unified_Editing_Space_for_Open-Domain_Image_Color_CVPR_2022_paper.html | CVPR 2022 | null |
GAN-Supervised Dense Visual Alignment | William Peebles, Jun-Yan Zhu, Richard Zhang, Antonio Torralba, Alexei A. Efros, Eli Shechtman | We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end. We apply our framework to the dense visual alignment problem. Inspired by the classic Congealing method, our GANgealing algorithm trains a Spatial Transformer to map random samples from a GAN trained on unaligned data to a common, jointly-learned target mode. We show results on eight datasets, all of which demonstrate our method successfully aligns complex data and discovers dense correspondences. GANgealing significantly outperforms past self-supervised correspondence algorithms and performs on-par with (and sometimes exceeds) state-of-the-art supervised correspondence algorithms on several datasets---without making use of any correspondence supervision or data augmentation and despite being trained exclusively on GAN-generated data. For precise correspondence, we improve upon state-of-the-art supervised methods by as much as 3x. We show applications of our method for augmented reality, image editing and automated pre-processing of image datasets for downstream GAN training. | https://openaccess.thecvf.com/content/CVPR2022/papers/Peebles_GAN-Supervised_Dense_Visual_Alignment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peebles_GAN-Supervised_Dense_Visual_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peebles_GAN-Supervised_Dense_Visual_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peebles_GAN-Supervised_Dense_Visual_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
SwinTextSpotter: Scene Text Spotting via Better Synergy Between Text Detection and Text Recognition | Mingxin Huang, Yuliang Liu, Zhenghao Peng, Chongyu Liu, Dahua Lin, Shenggao Zhu, Nicholas Yuan, Kai Ding, Lianwen Jin | End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. However, recent state-of-the-art methods usually incorporate detection and recognition simply by sharing the backbone, which does not directly take advantage of the feature interaction between the two tasks. In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer encoder with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism to explicitly guide text localization through recognition loss. The straightforward design results in a concise framework that requires neither additional rectification module nor character-level annotation for the arbitrarily-shaped text. Qualitative and quantitative experiments on multi-oriented datasets RoIC13 and ICDAR 2015, arbitrarily-shaped datasets Total-Text and CTW1500, and multi-lingual datasets ReCTS (Chinese) and VinText (Vietnamese) demonstrate SwinTextSpotter significantly outperforms existing methods. Code is available at https://github.com/mxin262/SwinTextSpotter. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_SwinTextSpotter_Scene_Text_Spotting_via_Better_Synergy_Between_Text_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_SwinTextSpotter_Scene_Text_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10209 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_SwinTextSpotter_Scene_Text_Spotting_via_Better_Synergy_Between_Text_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_SwinTextSpotter_Scene_Text_Spotting_via_Better_Synergy_Between_Text_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Level Feature Learning for Contrastive Multi-View Clustering | Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He | Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same feature space, where they ignore the conflict between learning consistent common semantics and reconstructing inconsistent view-private information. In this paper, we propose a new framework of multi-level feature learning for contrastive multi-view clustering to address the aforementioned issue. Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces. Specifically, the reconstruction objective is conducted on the low-level features. Two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. They make the high-level features effectively explore the common semantics and the semantic labels achieve the multi-view clustering. As a result, the proposed framework can reduce the adverse influence of view-private information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Multi-Level_Feature_Learning_for_Contrastive_Multi-View_Clustering_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2106.11193 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Multi-Level_Feature_Learning_for_Contrastive_Multi-View_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Multi-Level_Feature_Learning_for_Contrastive_Multi-View_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering | Ruoxi Shi, Xinyang Jiang, Caihua Shan, Yansen Wang, Dongsheng Li | Vector graphics (VG) have been ubiquitous in our daily life with vast applications in engineering, architecture, designs, etc. The VG recognition process of most existing methods is to first render the VG into raster graphics (RG) and then conduct recognition based on RG formats. However, this procedure discards the structure of geometries and loses the high resolution of VG. Recently, another category of algorithms is proposed to recognize directly from the original VG format. But it is affected by the topological errors that can be filtered out by RG rendering. Instead of looking at one format, it is a good solution to utilize the formats of VG and RG together to avoid these shortcomings. Besides, we argue that the VG-to-RG rendering process is essential to effectively combine VG and RG information. By specifying the rules on how to transfer VG primitives to RG pixels, the rendering process depicts the interaction and correlation between VG and RG. As a result, we propose RenderNet, a unified architecture for recognition on both 2D and 3D scenarios, which considers both VG/RG representations and exploits their interaction by incorporating the VG-to-RG rasterization process. Experiments show that RenderNet can achieve state-of-the-art performance on 2D and 3D object recognition tasks on various VG datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_RendNet_Unified_2D3D_Recognizer_With_Latent_Space_Rendering_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_RendNet_Unified_2D3D_Recognizer_With_Latent_Space_Rendering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_RendNet_Unified_2D3D_Recognizer_With_Latent_Space_Rendering_CVPR_2022_paper.html | CVPR 2022 | null |
iPLAN: Interactive and Procedural Layout Planning | Feixiang He, Yanlong Huang, He Wang | Layout design is ubiquitous in many applications, e.g. architecture/urban planning, etc, which involves a lengthy iterative design process. Recently, deep learning has been leveraged to automatically generate layouts via image generation, showing a huge potential to free designers from laborious routines. While automatic generation can greatly boost productivity, designer input is undoubtedly crucial. An ideal AI-aided design tool should automate repetitive routines, and meanwhile accept human guidance and provide smart/proactive suggestions. However, the capability of involving humans into the loop has been largely ignored in existing methods which are mostly end-to-end approaches. To this end, we propose a new human-in-the-loop generative model, iPLAN, which is capable of automatically generating layouts, but also interacting with designers throughout the whole procedure, enabling humans and AI to co-evolve a sketchy idea gradually into the final design. iPLAN is evaluated on diverse datasets and compared with existing methods. The results show that iPLAN has high fidelity in producing similar layouts to those from human designers, great flexibility in accepting designer inputs and providing design suggestions accordingly, and strong generalizability when facing unseen design tasks and limited training data. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_iPLAN_Interactive_and_Procedural_Layout_Planning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_iPLAN_Interactive_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14412 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_iPLAN_Interactive_and_Procedural_Layout_Planning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_iPLAN_Interactive_and_Procedural_Layout_Planning_CVPR_2022_paper.html | CVPR 2022 | null |
Video Frame Interpolation With Transformer | Liying Lu, Ruizheng Wu, Huaijia Lin, Jiangbo Lu, Jiaya Jia | Video frame interpolation (VFI), which aims to synthesize intermediate frames of a video, has made remarkable progress with development of deep convolutional networks over past years. Existing methods built upon convolutional networks generally face challenges of handling large motion due to the locality of convolution operations. To overcome this limitation, we introduce a novel framework, which takes advantage of Transformer to model long-range pixel correlation among video frames. Further, our network is equipped with a novel cross-scale window-based attention mechanism, where cross-scale windows interact with each other. This design effectively enlarges the receptive field and aggregates multi-scale information. Extensive quantitative and qualitative experiments demonstrate that our method achieves new state-of-the-art results on various benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Video_Frame_Interpolation_With_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Video_Frame_Interpolation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.07230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Video_Frame_Interpolation_With_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Video_Frame_Interpolation_With_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
GIFS: Neural Implicit Function for General Shape Representation | Jianglong Ye, Yuntao Chen, Naiyan Wang, Xiaolong Wang | Recent development of neural implicit function has shown tremendous success on high-quality 3D shape reconstruction. However, most works divide the space into inside and outside of the shape, which limits their representing power to single-layer and watertight shapes. This limitation leads to tedious data processing (converting non-watertight raw data to watertight) as well as the incapability of representing general object shapes in the real world. In this work, we propose a novel method to represent general shapes including non-watertight shapes and shapes with multi-layer surfaces. We introduce General Implicit Function for 3D Shape (GIFS), which models the relationships between every two points instead of the relationships between points and surfaces. Instead of dividing 3D space into predefined inside-outside regions, GIFS encodes whether two points are separated by any surface. Experiments on ShapeNet show that GIFS outperforms previous state-of-the-art methods in terms of reconstruction quality, rendering efficiency, and visual fidelity. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_GIFS_Neural_Implicit_Function_for_General_Shape_Representation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_GIFS_Neural_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_GIFS_Neural_Implicit_Function_for_General_Shape_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_GIFS_Neural_Implicit_Function_for_General_Shape_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
Deblur-NeRF: Neural Radiance Fields From Blurry Images | Li Ma, Xiaoyu Li, Jing Liao, Qi Zhang, Xuan Wang, Jue Wang, Pedro V. Sander | Neural Radiance Field (NeRF) has gained considerable attention recently for 3D scene reconstruction and novel view synthesis due to its remarkable synthesis quality. However, image blurriness caused by defocus or motion, which often occurs when capturing scenes in the wild, significantly degrades its reconstruction quality. To address this problem, We propose Deblur-NeRF, the first method that can recover a sharp NeRF from blurry input. We adopt an analysis-by-synthesis approach that reconstructs blurry views by simulating the blurring process, thus making NeRF robust to blurry inputs. The core of this simulation is a novel Deformable Sparse Kernel (DSK) module that models spatially-varying blur kernels by deforming a canonical sparse kernel at each spatial location. The ray origin of each kernel point is jointly optimized, inspired by the physical blurring process. This module is parameterized as an MLP that has the ability to be generalized to various blur types. Jointly optimizing the NeRF and the DSK module allows us to restore a sharp NeRF. We demonstrate that our method can be used on both camera motion blur and defocus blur: the two most common types of blur in real scenes. Evaluation results on both synthetic and real-world data show that our method outperforms several baselines. The synthetic and real datasets along with the source code can be find in https://limacv.github.io/deblurnerf/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Deblur-NeRF_Neural_Radiance_Fields_From_Blurry_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Deblur-NeRF_Neural_Radiance_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Deblur-NeRF_Neural_Radiance_Fields_From_Blurry_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Deblur-NeRF_Neural_Radiance_Fields_From_Blurry_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Egocentric Prediction of Action Target in 3D | Yiming Li, Ziang Cao, Andrew Liang, Benjamin Liang, Luoyao Chen, Hang Zhao, Chen Feng | We are interested in anticipating as early as possible the target location of a person's object manipulation action in a 3D workspace from egocentric vision. It is important in fields like human-robot collaboration, but has not yet received enough attention from vision and learning communities. To stimulate more research on this challenging egocentric vision task, we propose a large multimodality dataset of more than 1 million frames of RGB-D and IMU streams, and provide evaluation metrics based on our high-quality 2D and 3D labels from semi-automatic annotation. Meanwhile, we design baseline methods using recurrent neural networks and conduct various ablation studies to validate their effectiveness. Our results demonstrate that this new task is worthy of further study by researchers in robotics, vision, and learning communities. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Egocentric_Prediction_of_Action_Target_in_3D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Egocentric_Prediction_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Egocentric_Prediction_of_Action_Target_in_3D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Egocentric_Prediction_of_Action_Target_in_3D_CVPR_2022_paper.html | CVPR 2022 | null |
TemporalUV: Capturing Loose Clothing With Temporally Coherent UV Coordinates | You Xie, Huiqi Mao, Angela Yao, Nils Thuerey | We propose a novel approach to generate temporally coherent UV coordinates for loose clothing. Our method is not constrained by human body outlines and can capture loose garments and hair. We implemented a differentiable pipeline to learn UV mapping between a sequence of RGB inputs and textures via UV coordinates. Instead of treating the UV coordinates of each frame separately, our data generation approach connects all UV coordinates via feature matching for temporal stability. Subsequently, a generative model is trained to balance the spatial quality and temporal stability. It is driven by supervised and unsupervised losses in both UV and image spaces. Our experiments show that the trained models output high-quality UV coordinates and generalize to new poses. Once a sequence of UV coordinates has been inferred by our model, it can be used to flexibly synthesize new looks and modified visual styles. Compared to existing methods, our approach reduces the computational workload to animate new outfits by several orders of magnitude. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_TemporalUV_Capturing_Loose_Clothing_With_Temporally_Coherent_UV_Coordinates_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_TemporalUV_Capturing_Loose_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03671 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_TemporalUV_Capturing_Loose_Clothing_With_Temporally_Coherent_UV_Coordinates_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_TemporalUV_Capturing_Loose_Clothing_With_Temporally_Coherent_UV_Coordinates_CVPR_2022_paper.html | CVPR 2022 | null |
Whose Track Is It Anyway? Improving Robustness to Tracking Errors With Affinity-Based Trajectory Prediction | Xinshuo Weng, Boris Ivanovic, Kris Kitani, Marco Pavone | Multi-agent trajectory prediction is critical for planning and decision-making in human-interactive autonomous systems, such as self-driving cars. However, most prediction models are developed separately from their upstream perception (detection and tracking) modules, assuming ground truth past trajectories as inputs. As a result, their performance degrades significantly when using real-world noisy tracking results as inputs. This is typically caused by the propagation of errors from tracking to prediction, such as noisy tracks, fragments, and identity switches. To alleviate this propagation of errors, we propose a new prediction paradigm that uses detections and their affinity matrices across frames as inputs, removing the need for error-prone data association during tracking. Since affinity matrices contain "soft" information about the similarity and identity of detections across frames, making predictions directly from affinity matrices retains strictly more information than making predictions from the tracklets generated by data association. Experiments on large-scale, real-world autonomous driving datasets show that our affinity-based prediction scheme reduces overall prediction errors by up to 57.9%, in comparison to standard prediction pipelines that use tracklets as inputs, with even more significant error reduction (up to 88.6%) if restricting the evaluation to challenging scenarios with tracking errors. Our project website is at https://www.xinshuoweng.com/projects/Affinipred | https://openaccess.thecvf.com/content/CVPR2022/papers/Weng_Whose_Track_Is_It_Anyway_Improving_Robustness_to_Tracking_Errors_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Weng_Whose_Track_Is_It_Anyway_Improving_Robustness_to_Tracking_Errors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Weng_Whose_Track_Is_It_Anyway_Improving_Robustness_to_Tracking_Errors_CVPR_2022_paper.html | CVPR 2022 | null |
DoubleField: Bridging the Neural Surface and Radiance Fields for High-Fidelity Human Reconstruction and Rendering | Ruizhi Shao, Hongwen Zhang, He Zhang, Mingjia Chen, Yan-Pei Cao, Tao Yu, Yebin Liu | We introduce DoubleField, a novel framework combining the merits of both surface field and radiance field for high-fidelity human reconstruction and rendering. Within DoubleField, the surface field and radiance field are associated together by a shared feature embedding and a surface-guided sampling strategy. Moreover, a view-to-view transformer is introduced to fuse multi-view features and learn view-dependent features directly from high-resolution inputs. With the modeling power of DoubleField and the view-to-view transformer, our method significantly improves the reconstruction quality of both geometry and appearance, while supporting direct inference, scene-specific high-resolution finetuning, and fast rendering. The efficacy of DoubleField is validated by the quantitative evaluations on several datasets and the qualitative results in a real-world sparse multi-view system, showing its superior capability for high-quality human model reconstruction and photo-realistic free-viewpoint human rendering. Data and source code will be made public for the research purpose. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shao_DoubleField_Bridging_the_Neural_Surface_and_Radiance_Fields_for_High-Fidelity_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shao_DoubleField_Bridging_the_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.03798 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shao_DoubleField_Bridging_the_Neural_Surface_and_Radiance_Fields_for_High-Fidelity_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shao_DoubleField_Bridging_the_Neural_Surface_and_Radiance_Fields_for_High-Fidelity_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Real-World Navigation With Deep Differentiable Planners | Shu Ishida, João F. Henriques | We train embodied neural networks to plan and navigate unseen complex 3D environments, emphasising real-world deployment. Rather than requiring prior knowledge of the agent or environment, the planner learns to model the state transitions and rewards. To avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations. Although they work well in small simulations, we address two major limitations that hinder their deployment. First, we observed that current differentiable planners struggle to plan long-term in environments with a high branching complexity. While they should ideally learn to assign low rewards to obstacles to avoid collisions, these penalties are not strong enough to guarantee collision-free operation. We thus impose a structural constraint on the value iteration, which explicitly learns to model impossible actions and noisy motion. Secondly, we extend the model to plan exploration with a limited perspective camera under translation and fine rotations, which is crucial for real robot deployment. Our proposals significantly improve semantic navigation and exploration on several 2D and 3D environments, succeeding in settings that are otherwise challenging for differentiable planners. As far as we know, we are the first to successfully apply them to the difficult Active Vision Dataset, consisting of real images captured from a robot. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ishida_Towards_Real-World_Navigation_With_Deep_Differentiable_Planners_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ishida_Towards_Real-World_Navigation_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2108.05713 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ishida_Towards_Real-World_Navigation_With_Deep_Differentiable_Planners_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ishida_Towards_Real-World_Navigation_With_Deep_Differentiable_Planners_CVPR_2022_paper.html | CVPR 2022 | null |
An Iterative Quantum Approach for Transformation Estimation From Point Sets | Natacha Kuete Meli, Florian Mannel, Jan Lellmann | We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio. | https://openaccess.thecvf.com/content/CVPR2022/papers/Meli_An_Iterative_Quantum_Approach_for_Transformation_Estimation_From_Point_Sets_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Meli_An_Iterative_Quantum_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Meli_An_Iterative_Quantum_Approach_for_Transformation_Estimation_From_Point_Sets_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Meli_An_Iterative_Quantum_Approach_for_Transformation_Estimation_From_Point_Sets_CVPR_2022_paper.html | CVPR 2022 | null |
Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation | Xiangtai Li, Wenwei Zhang, Jiangmiao Pang, Kai Chen, Guangliang Cheng, Yunhai Tong, Chen Change Loy | This paper presents Video K-Net, a simple, strong, and unified framework for fully end-to-end video panoptic segmentation. The method is built upon K-Net, a method that unifies image segmentation via a group of learnable kernels. We observe that these learnable kernels from K-Net, which encode object appearances and contexts, can naturally associate identical instances across video frames. Motivated by this observation, Video K-Net learns to simultaneously segment and track "things" and "stuff" in a video with simple kernel-based appearance modeling and cross-temporal kernel interaction. Despite the simplicity, it achieves state-of-the-art video panoptic segmentation results on Citscapes-VPS and KITTI-STEP without bells and whistles. In particular on KITTI-STEP, the simple method can boost almost 12% relative improvements over previous methods. We also validate its generalization on video semantic segmentation, where we boost various baselines by 2% on the VSPW dataset. Moreover, we extend K-Net into clip-level video framework for video instance segmentation where we obtain 40.5% for ResNet50 backbone and 51.5% mAP for Swin-base on YouTube-2019 validation set. We hope this simple yet effective method can serve as a new flexible baseline in video segmentation. Both code and models are released at \href https://github.com/lxtGH/Video-K-Net. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Video_K-Net_A_Simple_Strong_and_Unified_Baseline_for_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Video_K-Net_A_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Video_K-Net_A_Simple_Strong_and_Unified_Baseline_for_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Video_K-Net_A_Simple_Strong_and_Unified_Baseline_for_Video_CVPR_2022_paper.html | CVPR 2022 | null |
UnweaveNet: Unweaving Activity Stories | Will Price, Carl Vondrick, Dima Damen | Our lives can be seen as a complex weaving of activities; we switch from one activity to another, to maximise our achievements or in reaction to demands placed upon us. Observing a video of unscripted daily activities, we parse the video into its constituent activity threads through a process we call unweaving. To accomplish this, we introduce a video representation explicitly capturing activity threads called a thread bank, along with a neural controller capable of detecting goal changes and continuations of past activities, together forming UnweaveNet. We train and evaluate UnweaveNet on sequences from the unscripted egocentric dataset EPIC-KITCHENS. We propose and showcase the efficacy of pretraining UnweaveNet in a self-supervised manner. | https://openaccess.thecvf.com/content/CVPR2022/papers/Price_UnweaveNet_Unweaving_Activity_Stories_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Price_UnweaveNet_Unweaving_Activity_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.10194 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Price_UnweaveNet_Unweaving_Activity_Stories_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Price_UnweaveNet_Unweaving_Activity_Stories_CVPR_2022_paper.html | CVPR 2022 | null |
Balanced MSE for Imbalanced Visual Regression | Jiawei Ren, Mingyuan Zhang, Cunjun Yu, Ziwei Liu | Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous labels, which can be boundless and high-dimensional and hence more challenging. In this work, we identify that the widely used Mean Square Error (MSE) loss function can be ineffective in imbalanced regression. We revisit MSE from a statistical view and propose a novel loss function, Balanced MSE, to accommodate the imbalanced training label distribution. We further design multiple implementations of Balanced MSE to tackle different real-world scenarios, particularly including the one that requires no prior knowledge about the training label distribution. Moreover, to the best of our knowledge, Balanced MSE is the first general solution to high-dimensional imbalanced regression. Extensive experiments on both synthetic and three real-world benchmarks demonstrate the effectiveness of Balanced MSE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Balanced_MSE_for_Imbalanced_Visual_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ren_Balanced_MSE_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Balanced_MSE_for_Imbalanced_Visual_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Balanced_MSE_for_Imbalanced_Visual_Regression_CVPR_2022_paper.html | CVPR 2022 | null |
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning | Matias Mendieta, Taojiannan Yang, Pu Wang, Minwoo Lee, Zhengming Ding, Chen Chen | Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distribution among clients is often non-IID in nature, making efficient optimization difficult. To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model. Instead, we consider rethinking solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. To this end, we first present a systematic study informed by second-order indicators to better understand algorithm effectiveness in FL. Interestingly, we find that standard regularization methods are surprisingly strong performers in mitigating data heterogeneity effects. Based on our findings, we further propose a simple and effective method, FedAlign, to overcome data heterogeneity and the pitfalls of previous methods. FedAlign achieves competitive accuracy with state-of-the-art FL methods across a variety of settings while minimizing computation and memory overhead. Code is available at https://github.com/mmendiet/FedAlign. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mendieta_Local_Learning_Matters_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14213 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
PhysFormer: Facial Video-Based Physiological Measurement With Temporal Difference Transformer | Zitong Yu, Yuming Shen, Jingang Shi, Hengshuang Zhao, Philip H.S. Torr, Guoying Zhao | Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications. Recent deep learning approaches focus on mining subtle rPPG clues using convolutional neural networks with limited spatio-temporal receptive fields, which neglect the long-range spatio-temporal perception and interaction for rPPG modeling. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement. As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference. Furthermore, we also propose the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain, which provide elaborate supervisions for PhysFormer and alleviate overfitting. Comprehensive experiments are performed on four benchmark datasets to show our superior performance on both intra- and cross-dataset testings. One highlight is that, unlike most transformer networks needed pretraining from large-scale datasets, the proposed PhysFormer can be easily trained from scratch on rPPG datasets, which makes it promising as a novel transformer baseline for the rPPG community. The codes will be released soon. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_PhysFormer_Facial_Video-Based_Physiological_Measurement_With_Temporal_Difference_Transformer_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.12082 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_PhysFormer_Facial_Video-Based_Physiological_Measurement_With_Temporal_Difference_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_PhysFormer_Facial_Video-Based_Physiological_Measurement_With_Temporal_Difference_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Dimension Embeddings for Monocular 3D Object Detection | Yunpeng Zhang, Wenzhao Zheng, Zheng Zhu, Guan Huang, Dalong Du, Jie Zhou, Jiwen Lu | Most existing deep learning-based approaches for monocular 3D object detection directly regress the dimensions of objects and overlook their importance in solving the ill-posed problem. In this paper, we propose a general method to learn appropriate embeddings for dimension estimation in monocular 3D object detection. Specifically, we consider two intuitive clues in learning the dimension-aware embeddings with deep neural networks. First, we constrain the pair-wise distance on the embedding space to reflect the similarity of corresponding dimensions so that the model can take advantage of inter-object information to learn more discriminative embeddings for dimension estimation. Second, we propose to learn representative shape templates on the dimension-aware embedding space. Through the attention mechanism, each object can interact with the learnable templates and obtain the attentive dimensions as the initial estimation, which is further refined by the combined features from both the object and the attentive templates. Experimental results on the well-established KITTI dataset demonstrate the proposed method of dimension embeddings can bring consistent improvements with negligible computation cost overhead. We achieve new state-of-the-art performance on the KITTI 3D object detection benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Dimension_Embeddings_for_Monocular_3D_Object_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Dimension_Embeddings_for_Monocular_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Dimension_Embeddings_for_Monocular_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Look Closer To Supervise Better: One-Shot Font Generation via Component-Based Discriminator | Yuxin Kong, Canjie Luo, Weihong Ma, Qiyuan Zhu, Shenggao Zhu, Nicholas Yuan, Lianwen Jin | Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures. Typically, only a few samples can serve as the style/content reference (termed few-shot learning), which further increases the difficulty to preserve local style patterns or detailed glyph structures. We investigate the drawbacks of previous studies and find that a coarse-grained discriminator is insufficient for supervising a font generator. To this end, we propose a novel Component-Aware Module (CAM), which supervises the generator to decouple content and style at a more fine-grained level, i.e., the component level. Different from previous studies struggling to increase the complexity of generators, we aim to perform more effective supervision for a relatively simple generator to achieve its full potential, which is a brand new perspective for font generation. The whole framework achieves remarkable results by coupling component-level supervision with adversarial learning, hence we call it Component-Guided GAN, shortly CG-GAN. Extensive experiments show that our approach outperforms state-of-the-art one-shot font generation methods. Furthermore, it can be applied to handwritten word synthesis and scene text image editing, suggesting the generalization of our approach. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_Look_Closer_To_Supervise_Better_One-Shot_Font_Generation_via_Component-Based_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kong_Look_Closer_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.00146 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Look_Closer_To_Supervise_Better_One-Shot_Font_Generation_via_Component-Based_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Look_Closer_To_Supervise_Better_One-Shot_Font_Generation_via_Component-Based_CVPR_2022_paper.html | CVPR 2022 | null |
NeRFReN: Neural Radiance Fields With Reflections | Yuan-Chen Guo, Di Kang, Linchao Bao, Yu He, Song-Hai Zhang | Neural Radiance Fields (NeRF) has achieved unprecedented view synthesis quality using coordinate-based neural scene representations. However, NeRF's view dependency can only handle simple reflections like highlights but cannot deal with complex reflections such as those from glass and mirrors. In these scenarios, NeRF models the virtual image as real geometries which leads to inaccurate depth estimation, and produces blurry renderings when the multi-view consistency is violated as the reflected objects may only be seen under some of the viewpoints. To overcome these issues, we introduce NeRFReN, which is built upon NeRF to model scenes with reflections. Specifically, we propose to split a scene into transmitted and reflected components, and model the two components with separate neural radiance fields. Considering that this decomposition is highly under-constrained, we exploit geometric priors and apply carefully-designed training strategies to achieve reasonable decomposition results. Experiments on various self-captured scenes show that our method achieves high-quality novel view synthesis and physically sound depth estimation results while enabling scene editing applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_NeRFReN_Neural_Radiance_Fields_With_Reflections_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_NeRFReN_Neural_Radiance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15234 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_NeRFReN_Neural_Radiance_Fields_With_Reflections_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_NeRFReN_Neural_Radiance_Fields_With_Reflections_CVPR_2022_paper.html | CVPR 2022 | null |
Blind Image Super-Resolution With Elaborate Degradation Modeling on Noise and Kernel | Zongsheng Yue, Qian Zhao, Jianwen Xie, Lei Zhang, Deyu Meng, Kwan-Yee K. Wong | While researches on model-based blind single image super-resolution (SISR) have achieved tremendous successes recently, most of them do not consider the image degradation sufficiently. Firstly, they always assume image noise obeys an independent and identically distributed (i.i.d.) Gaussian or Laplacian distribution, which largely underestimates the complexity of real noise. Secondly, previous commonly-used kernel priors (e.g., normalization, sparsity) are not effective enough to guarantee a rational kernel solution, and thus degenerates the performance of subsequent SISR task. To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel. Specifically, instead of the traditional i.i.d. noise assumption, a patch-based non-i.i.d. noise model is proposed to tackle the complicated real noise, expecting to increase the degrees of freedom of the model for noise representation. As for the blur kernel, we novelly con- struct a concise yet effective kernel generator, and plug it into the proposed blind SISR method as an explicit kernel prior (EKP). To solve the proposed model, a theoretically grounded Monte Carlo EM algorithm is specifically designed. Comprehensive experiments demonstrate the superiority of our method over current state-of-the-arts on synthetic and real datasets. The source code is available at https://github.com/zsyOAOA/BSRDM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yue_Blind_Image_Super-Resolution_With_Elaborate_Degradation_Modeling_on_Noise_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yue_Blind_Image_Super-Resolution_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.00986 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yue_Blind_Image_Super-Resolution_With_Elaborate_Degradation_Modeling_on_Noise_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yue_Blind_Image_Super-Resolution_With_Elaborate_Degradation_Modeling_on_Noise_and_CVPR_2022_paper.html | CVPR 2022 | null |
Finding Good Configurations of Planar Primitives in Unorganized Point Clouds | Mulin Yu, Florent Lafarge | We present an algorithm for detecting planar primitives from unorganized 3D point clouds. Departing from an initial configuration, the algorithm refines both the continuous plane parameters and the discrete assignment of input points to them by seeking high fidelity, high simplicity and high completeness. Our key contribution relies upon the design of an exploration mechanism guided by a multi-objective energy function. The transitions within the large solution space are handled by five geometric operators that create, remove and modify primitives. We demonstrate the potential of our method on a variety of scenes, from organic shapes to man-made objects, and sensors, from multiview stereo to laser. We show its efficacy with respect to existing primitive fitting approaches and illustrate its applicative interest in compact mesh reconstruction, when combined with a plane assembly method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Finding_Good_Configurations_of_Planar_Primitives_in_Unorganized_Point_Clouds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Finding_Good_Configurations_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Finding_Good_Configurations_of_Planar_Primitives_in_Unorganized_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Finding_Good_Configurations_of_Planar_Primitives_in_Unorganized_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images | Zhen Li, Lingli Wang, Xiang Huang, Cihui Pan, Jiaqi Yang | Inverse rendering of complex material such as glossy, metal and mirror material is a long-standing ill-posed problem in this area, which has not been well solved. Previous approaches cannot tackle them well due to simplified BRDF and unsuitable illumination representations. In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance. Our framework estimates geometry, material and Spatially-Coherent (SC) illumination from a single indoor panorama. Due to the lack of panoramic datasets with completed SVBRDF and full-spherical light probes, we introduce an artist-designed dataset named FutureHouse with high-quality geometry, SVBRDF and per-pixel Spatially-Varying (SV) lighting. To ensure the coherence of SV lighting, a novel SC loss is proposed. Extensive experiments on both synthetic and real-world data show that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively, and is able to produce photorealistic results for a number of applications such as dynamic virtual object insertion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_PhyIR_Physics-Based_Inverse_Rendering_for_Panoramic_Indoor_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_PhyIR_Physics-Based_Inverse_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_PhyIR_Physics-Based_Inverse_Rendering_for_Panoramic_Indoor_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_PhyIR_Physics-Based_Inverse_Rendering_for_Panoramic_Indoor_Images_CVPR_2022_paper.html | CVPR 2022 | null |
SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization | Yucheng Hang, Bin Xia, Wenming Yang, Qingmin Liao | Image harmonization aims to achieve visual consistency in composite images by adapting a foreground to make it compatible with a background. However, existing methods always only use the real image as the positive sample to guide the training, and at most introduce the corresponding composite image as a single negative sample for an auxiliary constraint, which leads to limited distortion knowledge, and further causes a too large solution space, making the generated harmonized image distorted. Besides, none of them jointly constrain from the foreground self-style and foreground-background style consistency, which exacerbates this problem. Moreover, recent region-aware adaptive instance normalization achieves great success but only considers the global background feature distribution, making the aligned foreground feature distribution biased. To address these issues, we propose a self-consistent style contrastive learning scheme (SCS-Co). By dynamically generating multiple negative samples, our SCS-Co can learn more distortion knowledge and well regularize the generated harmonized image in the style representation space from two aspects of the foreground self-style and foreground-background style consistency, leading to a more photorealistic visual result. In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity. Experiments demonstrate the superiority of our method over other state-of-the-art methods in both quantitative comparison and visual analysis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hang_SCS-Co_Self-Consistent_Style_Contrastive_Learning_for_Image_Harmonization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hang_SCS-Co_Self-Consistent_Style_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hang_SCS-Co_Self-Consistent_Style_Contrastive_Learning_for_Image_Harmonization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hang_SCS-Co_Self-Consistent_Style_Contrastive_Learning_for_Image_Harmonization_CVPR_2022_paper.html | CVPR 2022 | null |
Beyond Fixation: Dynamic Window Visual Transformer | Pengzhen Ren, Changlin Li, Guangrun Wang, Yun Xiao, Qing Du, Xiaodan Liang, Xiaojun Chang | Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a fixed single-scale window for modeling by default, ignoring the impact of window size on model performance. However, this may limit the modeling potential of these window-based models for multi-scale information. In this paper, we propose a novel method, named Dynamic Window Vision Transformer (DW-ViT). To the best of our knowledge, we are the first to use dynamic multi-scale windows to explore the upper limit of the effect of window settings on model performance. In DW-ViT, multi-scale information is obtained by assigning windows of different sizes to different head groups of window multi-head self-attention. Then, the information is dynamically fused by assigning different weights to the multi-scale window branches. We conducted a detailed performance evaluation on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with related state-of-the-art (SoTA) methods, DW-ViT obtains the best performance. Specifically, compared with the current SoTA Swin Transformers [??], DW-ViT has achieved consistent and substantial improvements on all three datasets with similar parameters and computational costs. In addition, DW-ViT exhibits good scalability and can be easily inserted into any window-based visual transformers. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Beyond_Fixation_Dynamic_Window_Visual_Transformer_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.12856 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Beyond_Fixation_Dynamic_Window_Visual_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Beyond_Fixation_Dynamic_Window_Visual_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Progressive End-to-End Object Detection in Crowded Scenes | Anlin Zheng, Yuang Zhang, Xiangyu Zhang, Xiaojuan Qi, Jian Sun | In this paper, we propose a new query-based detection framework for crowd detection. Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be inferred for a single object, typically in crowded scenes; second, the performance saturates as the depth of the decoding stage increases. Benefiting from the nature of the one-to-one label assignment rule, we propose a progressive predicting method to address the above issues. Specifically, we first select accepted queries prone to generate true positive predictions, then refine the rest noisy queries according to the previously accepted predictions. Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes. Equipped with our approach, Sparse RCNN achieves 92.0% \text AP , 41.4% \text MR ^ -2 and 83.2% \text JI on the challenging CrowdHuman [??] dataset, outperforming the box-based method MIP [??] that specifies in handling crowded scenarios. Moreover, the proposed method, robust to crowdedness, can still obtain consistent improvements on moderately and slightly crowded datasets like CityPersons [??] and COCO [??]. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Progressive_End-to-End_Object_Detection_in_Crowded_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Progressive_End-to-End_Object_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07669 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Progressive_End-to-End_Object_Detection_in_Crowded_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Progressive_End-to-End_Object_Detection_in_Crowded_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification | Qiang Zhang, Changzhou Lai, Jianan Liu, Nianchang Huang, Jungong Han | For Visible-Infrared person Re-IDentification (VI-ReID), existing modality-specific information compensation based models try to generate the images of missing modality from existing ones for reducing cross-modality discrepancy. However, because of the large modality discrepancy between visible and infrared images, the generated images usually have low qualities and introduce much more interfering information (e.g., color inconsistency). This greatly degrades the subsequent VI-ReID performance. Alternatively, we present a novel Feature-level Modality Compensation Network (FMCNet) for VIReID in this paper, which aims to compensate the missing modality-specific information in the feature level rather than in the image level, i.e., directly generating those missing modality-specific features of one modality from existing modality-shared features of the other modality. This will enable our model to mainly generate some discriminative person related modality-specific features and discard those non-discriminative ones for benefiting VI-ReID. For that, a single-modality feature decomposition module is first designed to decompose single-modality features into modality-specific ones and modality-shared ones. Then, a feature-level modality compensation module is present to generate those missing modality-specific features from existing modality-shared ones. Finally, a shared-specific feature fusion module is proposed to combine the existing and generated features for VI-ReID. The effectiveness of our proposed model is verified on two benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_FMCNet_Feature-Level_Modality_Compensation_for_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_FMCNet_Feature-Level_Modality_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_FMCNet_Feature-Level_Modality_Compensation_for_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_FMCNet_Feature-Level_Modality_Compensation_for_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Improving GAN Equilibrium by Raising Spatial Awareness | Jianyuan Wang, Ceyuan Yang, Yinghao Xu, Yujun Shen, Hongdong Li, Bolei Zhou | The success of Generative Adversarial Networks (GANs) is largely built upon the adversarial training between a generator (G) and a discriminator (D). They are expected to reach a certain equilibrium where D cannot distinguish the generated images from the real ones. However, such an equilibrium is rarely achieved in practical GAN training, instead, D almost always surpasses G. We attribute one of its sources to the information asymmetry between D and G. We observe that D learns its own visual attention when determining whether an image is real or fake, but G has no explicit clue on which regions to focus on for a particular synthesis. To alleviate the issue of D dominating the competition in GANs, we aim to raise the spatial awareness of G. Randomly sampled multi-level heatmaps are encoded into the intermediate layers of G as an inductive bias. Thus G can purposefully improve the synthesis of certain image regions. We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance. As a byproduct, the introduced spatial awareness facilitates interactive editing over the output synthesis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Improving_GAN_Equilibrium_by_Raising_Spatial_Awareness_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Improving_GAN_Equilibrium_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00718 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Improving_GAN_Equilibrium_by_Raising_Spatial_Awareness_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Improving_GAN_Equilibrium_by_Raising_Spatial_Awareness_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Convolutional Surfaces | Luca Morreale, Noam Aigerman, Paul Guerrero, Vladimir G. Kim, Niloy J. Mitra | This work is concerned with representation of shapes while disentangling fine, local and possibly repeating geometry, from global, coarse structures. Achieving such disentanglement leads to two unrelated advantages: i) a significant compression in the number of parameters required to represent a given geometry; ii) the ability to manipulate either global geometry, or local details, without harming the other. At the core of our approach lies a novel pipeline and neural architecture, which are optimized to represent one specific atlas, representing one 3D surface. Our pipeline and architecture are designed so that disentanglement of global geometry from local details is accomplished through optimization, in a completely unsupervised manner. We show that this approach achieves better neural shape compression than the state of the art, as well as enabling manipulation and transfer of shape details. | https://openaccess.thecvf.com/content/CVPR2022/papers/Morreale_Neural_Convolutional_Surfaces_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Morreale_Neural_Convolutional_Surfaces_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02289 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Morreale_Neural_Convolutional_Surfaces_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Morreale_Neural_Convolutional_Surfaces_CVPR_2022_paper.html | CVPR 2022 | null |
HyperSegNAS: Bridging One-Shot Neural Architecture Search With 3D Medical Image Segmentation Using HyperNet | Cheng Peng, Andriy Myronenko, Ali Hatamizadeh, Vishwesh Nath, Md Mahfuzur Rahman Siddiquee, Yufan He, Daguang Xu, Rama Chellappa, Dong Yang | Semantic segmentation of 3D medical images is a challenging task due to the high variability of the shape and pattern of objects (such as organs or tumors). Given the recent success of deep learning in medical image segmentation, Neural Architecture Search (NAS) has been introduced to find high-performance 3D segmentation network architectures. However, because of the massive computational requirements of 3D data and the discrete optimization nature of architecture search, previous NAS methods require a long search time or necessary continuous relaxation, and commonly lead to sub-optimal network architectures. While one-shot NAS can potentially address these disadvantages, its application in the segmentation domain has not been well studied in the expansive multi-scale multi-path search space. To enable one-shot NAS for medical image segmentation, our method, named HyperSegNAS, introduces a HyperNet to assist super-net training by incorporating architecture topology information. Such a HyperNet can be removed once the super-net is trained and introduces no overhead during architecture search. We show that HyperSegNAS yields better performing and more intuitive architectures compared to the previous state-of-the-art (SOTA) segmentation networks; furthermore, it can quickly and accurately find good architecture candidates under different computing constraints. Our method is evaluated on public datasets from the Medical Segmentation Decathlon (MSD) challenge, and achieves SOTA performances. | https://openaccess.thecvf.com/content/CVPR2022/papers/Peng_HyperSegNAS_Bridging_One-Shot_Neural_Architecture_Search_With_3D_Medical_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peng_HyperSegNAS_Bridging_One-Shot_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.10652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_HyperSegNAS_Bridging_One-Shot_Neural_Architecture_Search_With_3D_Medical_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_HyperSegNAS_Bridging_One-Shot_Neural_Architecture_Search_With_3D_Medical_Image_CVPR_2022_paper.html | CVPR 2022 | null |
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes | Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi | While datasets with single-label supervision have propelled rapid advances in image classification, additional annotations are necessary in order to quantitatively assess how models make predictions. To this end, for a subset of ImageNet samples, we collect segmentation masks for the entire object and 18 informative attributes. We call this dataset RIVAL10 (RIch Visual Attributes with Localization), consisting of roughly 26k instances over 10 classes. Using RIVAL10, we evaluate the sensitivity of a broad set of models to noise corruptions in foregrounds, backgrounds and attributes. In our analysis, we consider diverse state-of-the-art architectures (ResNets, Transformers) and training procedures (CLIP, SimCLR, DeiT, Adversarial Training). We find that, somewhat surprisingly, in ResNets, adversarial training makes models more sensitive to the background compared to foreground than standard training. Similarly, contrastively-trained models also have lower relative foreground sensitivity in both transformers and ResNets. Lastly, we observe intriguing adaptive abilities of transformers to increase relative foreground sensitivity as corruption level increases. Using saliency methods, we automatically discover spurious features that drive the background sensitivity of models and assess alignment of saliency maps with foregrounds. Finally, we quantitatively study the attribution problem for neural features by comparing feature saliency with ground-truth localization of semantic attributes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Moayeri_A_Comprehensive_Study_of_Image_Classification_Model_Sensitivity_to_Foregrounds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Moayeri_A_Comprehensive_Study_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.10766 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Moayeri_A_Comprehensive_Study_of_Image_Classification_Model_Sensitivity_to_Foregrounds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Moayeri_A_Comprehensive_Study_of_Image_Classification_Model_Sensitivity_to_Foregrounds_CVPR_2022_paper.html | CVPR 2022 | null |
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes | Rahul Sajnani, Adrien Poulenard, Jivitesh Jain, Radhika Dua, Leonidas J. Guibas, Srinath Sridhar | Progress in 3D object understanding has relied on manually "canonicalized" shape datasets that contain instances with consistent position and orientation (3D pose). This has made it hard to generalize these methods to in-the-wild shapes, e.g., from internet model collections or depth sensors. ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds. We build on top of Tensor Field Networks (TFNs), a class of permutation- and rotation-equivariant, and translation-invariant 3D networks. During inference, our method takes an unseen full or partial 3D point cloud at an arbitrary pose and outputs an equivariant canonical pose. During training, this network uses self-supervision losses to learn the canonical pose from an un-canonicalized collection of full and partial 3D point clouds. ConDor can also learn to consistently co-segment object parts without any supervision. Extensive quantitative results on four new metrics show that our approach outperforms existing methods while enabling new applications such as operation on depth images and annotation transfer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sajnani_ConDor_Self-Supervised_Canonicalization_of_3D_Pose_for_Partial_Shapes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sajnani_ConDor_Self-Supervised_Canonicalization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.07788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sajnani_ConDor_Self-Supervised_Canonicalization_of_3D_Pose_for_Partial_Shapes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sajnani_ConDor_Self-Supervised_Canonicalization_of_3D_Pose_for_Partial_Shapes_CVPR_2022_paper.html | CVPR 2022 | null |
Source-Free Domain Adaptation via Distribution Estimation | Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, Dacheng Tao | Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. However, the training data in source domain required by most of the existing methods is usually unavailable in real-world applications due to privacy preserving policies. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation. Firstly, we produce robust pseudo-labels for target data with spherical k-means clustering, whose initial class centers are the weight vectors (anchors) learned by the classifier of pretrained model. Furthermore, we propose to estimate the class-conditioned feature distribution of source domain by exploiting target data and corresponding anchors. Finally, we sample surrogate features from the estimated distribution, which are then utilized to align two domains by minimizing a contrastive adaptation loss function. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple DA benchmarks, and even outperforms traditional DA methods which require plenty of source data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Source-Free_Domain_Adaptation_via_Distribution_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Source-Free_Domain_Adaptation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.11257 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Source-Free_Domain_Adaptation_via_Distribution_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Source-Free_Domain_Adaptation_via_Distribution_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Combination of Distributed Gradients Under Adversarial Perturbations | Kwang In Kim | We consider distributed (gradient descent-based) learning scenarios where the server combines the gradients of learning objectives gathered from local clients. As individual data collection and learning environments can vary, some clients could transfer erroneous gradients e.g., due to adversarial data or gradient perturbations. Further, for data privacy and security, the identities of such affected clients are often unknown to the server. In such cases, naively aggregating the resulting gradients can mislead the learning process. We propose a new server-side learning algorithm that robustly combines gradients. Our algorithm embeds the local gradients into the manifold of normalized gradients and refines their combinations via simulating a diffusion process therein. The resulting algorithm is instantiated as a computationally simple and efficient weighted gradient averaging algorithm. In the experiments with five classification and three regression benchmark datasets, our algorithm demonstrated significant performance improvements over existing robust gradient combination algorithms as well as the baseline uniform gradient averaging algorithm. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Robust_Combination_of_Distributed_Gradients_Under_Adversarial_Perturbations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Robust_Combination_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Robust_Combination_of_Distributed_Gradients_Under_Adversarial_Perturbations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Robust_Combination_of_Distributed_Gradients_Under_Adversarial_Perturbations_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Endogenous Shift for Cross-Domain Detection: A Large-Scale Benchmark and Perturbation Suppression Network | Renshuai Tao, Hainan Li, Tianbo Wang, Yanlu Wei, Yifu Ding, Bowei Jin, Hongping Zhi, Xianglong Liu, Aishan Liu | Existing cross-domain detection methods mostly study the domain shifts where differences between domains are often caused by external environment and perceivable for humans. However, in real-world scenarios (e.g., MRI medical diagnosis, X-ray security inspection), there still exists another type of shift, named endogenous shift, where the differences between domains are mainly caused by the intrinsic factors (e.g., imaging mechanisms, hardware components, etc.), and usually inconspicuous. This shift can also severely harm the cross-domain detection performance but has been rarely studied. To support this study, we contribute the first Endogenous Domain Shift (EDS) benchmark, X-ray security inspection, where the endogenous shifts among the domains are mainly caused by different X-ray machine types with different hardware parameters, wear degrees, etc. EDS consists of 14,219 images including 31,654 common instances from three domains (X-ray machines), with bounding-box annotations from 10 categories. To handle the endogenous shift, we further introduce the Perturbation Suppression Network (PSN), motivated by the fact that this shift is mainly caused by two types of perturbations: category-dependent and category-independent ones. PSN respectively exploits local prototype alignment and global adversarial learning mechanism to suppress these two types of perturbations. The comprehensive evaluation results show that PSN outperforms SOTA methods, serving a new perspective to the cross-domain research community. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tao_Exploring_Endogenous_Shift_for_Cross-Domain_Detection_A_Large-Scale_Benchmark_and_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Exploring_Endogenous_Shift_for_Cross-Domain_Detection_A_Large-Scale_Benchmark_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Exploring_Endogenous_Shift_for_Cross-Domain_Detection_A_Large-Scale_Benchmark_and_CVPR_2022_paper.html | CVPR 2022 | null |
VisCUIT: Visual Auditor for Bias in CNN Image Classifier | Seongmin Lee, Zijie J. Wang, Judy Hoffman, Duen Horng (Polo) Chau | CNN image classifiers are widely used, thanks to their efficiency and accuracy. However, they can suffer from biases that impede their practical applications. Most existing bias investigation techniques are either inapplicable to general image classification tasks or require significant user efforts in perusing all data subgroups to manually specify which data attributes to inspect. We present VisCUIT, an interactive visualization system that reveals how and why a CNN classifier is biased. VisCUIT visually summarizes the subgroups on which the classifier underperforms and helps users discover and characterize the cause of the underperformances by revealing image concepts responsible for activating neurons that contribute to misclassifications. VisCUIT runs in modern browsers and is open-source, allowing people to easily access and extend the tool to other model architectures and datasets. VisCUIT is available at the following public demo link: https://poloclub.github.io/VisCUIT. A video demo is available at https://youtu.be/eNDbSyM4R_4. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_VisCUIT_Visual_Auditor_for_Bias_in_CNN_Image_Classifier_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.05899 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_VisCUIT_Visual_Auditor_for_Bias_in_CNN_Image_Classifier_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_VisCUIT_Visual_Auditor_for_Bias_in_CNN_Image_Classifier_CVPR_2022_paper.html | CVPR 2022 | null |
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis | Albert Tseng, Jennifer J. Sun, Yisong Yue | Obtaining annotations for large training sets is expensive, especially in settings where domain knowledge is required, such as behavior analysis. Weak supervision has been studied to reduce annotation costs by using weak labels from task-specific labeling functions (LFs) to augment ground truth labels. However, domain experts still need to hand-craft different LFs for different tasks, limiting scalability. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level LFs. The key to our approach is to efficiently represent expert knowledge in a reusable domain-specific language and more general domain-level LFs, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate task-level LFs. Additionally, we propose a novel structural diversity cost that allows for efficient synthesis of diverse sets of LFs, further improving AutoSWAP's performance. We evaluate AutoSWAP in three behavior analysis domains and demonstrate that AutoSWAP outperforms existing approaches using only a fraction of the data. Our results suggest that AutoSWAP is an effective way to automatically generate LFs that can significantly reduce expert effort for behavior analysis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tseng_Automatic_Synthesis_of_Diverse_Weak_Supervision_Sources_for_Behavior_Analysis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tseng_Automatic_Synthesis_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15186 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tseng_Automatic_Synthesis_of_Diverse_Weak_Supervision_Sources_for_Behavior_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tseng_Automatic_Synthesis_of_Diverse_Weak_Supervision_Sources_for_Behavior_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Transferability Estimation Using Bhattacharyya Class Separability | Michal Pándy, Andrea Agostinelli, Jasper Uijlings, Vittorio Ferrari, Thomas Mensink | Transfer learning has become a popular method for leveraging pre-trained models in computer vision. However, without performing computationally expensive fine-tuning, it is difficult to quantify which pre-trained source models are suitable for a specific target task, or, conversely, to which tasks a pre-trained source model can be easily adapted to. In this work, we propose Gaussian Bhattacharyya Coefficient (GBC), a novel method for quantifying transferability between a source model and a target dataset. In a first step we embed all target images in the feature space defined by the source model, and represent them with per-class Gaussians. Then, we estimate their pairwise class separability using the Bhattacharyya coefficient, yielding a simple and effective measure of how well the source model transfers to the target task. We evaluate GBC on image classification tasks in the context of dataset and architecture selection. Further, we also perform experiments on the more complex semantic segmentation transferability estimation task. We demonstrate that GBC outperforms state-of-the-art transferability metrics on most evaluation criteria in the semantic segmentation settings, matches the performance of top methods for dataset transferability in image classification, and performs best on architecture selection problems for image classification. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pandy_Transferability_Estimation_Using_Bhattacharyya_Class_Separability_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pandy_Transferability_Estimation_Using_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pandy_Transferability_Estimation_Using_Bhattacharyya_Class_Separability_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pandy_Transferability_Estimation_Using_Bhattacharyya_Class_Separability_CVPR_2022_paper.html | CVPR 2022 | null |
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition | Thanh-Dat Truong, Quoc-Huy Bui, Chi Nhan Duong, Han-Seok Seo, Son Lam Phung, Xin Li, Khoa Luu | Human action recognition has recently become one ofthe popular research topics in the computer vision community. Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in thetask of video action recognition with competitive results.However, these methods have suffered some fundamentallimitations such as lack of robustness and generalization,e.g., how does the temporal ordering of video frames af-fect the recognition results? This work presents a novelend-to-end Transformer-based Directed Attention (Direc-Former) framework1for robust action recognition. The method takes a simple but novel perspective of Transformer-based approach to understand the right order of sequence actions. Therefore, the contributions of this work are three-fold. Firstly, we introduce the problem of ordered temporal learning issues to the action recognition problem. Secondly, a new Directed Attention mechanism is introduced to understand and provide attentions to human actions in the right order. Thirdly, we introduce the conditional dependency in action sequence modeling that includes orders and classes. The proposed approach consistently achieves the state-of-the-art (SOTA) results compared with the recent action recognition methods [4, 15, 62, 64], on three standard large-scale benchmarks, i.e. Jester, Kinetics-400 and Something-Something-V2. | https://openaccess.thecvf.com/content/CVPR2022/papers/Truong_DirecFormer_A_Directed_Attention_in_Transformer_Approach_to_Robust_Action_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.10233 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Truong_DirecFormer_A_Directed_Attention_in_Transformer_Approach_to_Robust_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Truong_DirecFormer_A_Directed_Attention_in_Transformer_Approach_to_Robust_Action_CVPR_2022_paper.html | CVPR 2022 | null |
Hierarchical Self-Supervised Representation Learning for Movie Understanding | Fanyi Xiao, Kaustav Kundu, Joseph Tighe, Davide Modolo | Most self-supervised video representation learning approaches focus on action recognition. In contrast, in this paper we focus on self-supervised video learning for movie understanding and propose a novel hierarchical self-supervised pretraining strategy that separately pretrains each level of our hierarchical movie understanding model. Specifically, we propose to pretrain the low-level video backbone using a contrastive learning objective, while pretrain the higher-level video contextualizer using an event mask prediction task, which enables the usage of different data sources for pretraining different levels of the hierarchy. We first show that our self-supervised pretraining strategies are effective and lead to improved performance on all tasks and metrics on VidSitu benchmark (e.g., improving on semantic role prediction from 47% to 61% CIDEr scores). We further demonstrate the effectiveness of our contextualized event features on LVU tasks, both when used alone and when combined with instance features, showing their complementarity. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiao_Hierarchical_Self-Supervised_Representation_Learning_for_Movie_Understanding_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.03101 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Hierarchical_Self-Supervised_Representation_Learning_for_Movie_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Hierarchical_Self-Supervised_Representation_Learning_for_Movie_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Egocentric Photo-Realistic Facial Expression Transfer for Virtual Reality | Amin Jourabloo, Fernando De la Torre, Jason Saragih, Shih-En Wei, Stephen Lombardi, Te-Li Wang, Danielle Belko, Autumn Trimble, Hernan Badino | Social presence, the feeling of being there with a "real" person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR). The best 3D video-realistic VR avatars that minimize the uncanny effect rely on person-specific (PS) models. However, these PS models are time-consuming to build and are typically trained with limited data variability, which results in poor generalization and robustness. Major sources of variability that affects the accuracy of facial expression transfer algorithms include using different VR headsets (e.g., camera configuration, slop of the headset), facial appearance changes over time (e.g., beard, make-up), and environmental factors (e.g., lighting, backgrounds). This is a major drawback for the scalability of these models in VR. This paper makes progress in overcoming these limitations by proposing an end-to-end multi-identity architecture (MIA) trained with specialized augmentation strategies. MIA drives the shape component of the avatar from three cameras in the VR headset (two eyes, one mouth), in untrained subjects, using minimal personalized information (i.e., neutral 3D mesh shape). Similarly, if the PS texture decoder is available, MIA is able to drive the full avatar (shape+texture) robustly outperforming PS models in challenging scenarios. Our key contribution to improve robustness and generalization, is that our method implicitly decouples, in an unsupervised manner, the facial expression from nuisance factors (e.g., headset, environment, facial appearance). We demonstrate the superior performance and robustness of the proposed method versus state-of-the-art PS approaches in a variety of experiments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jourabloo_Robust_Egocentric_Photo-Realistic_Facial_Expression_Transfer_for_Virtual_Reality_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.04794 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jourabloo_Robust_Egocentric_Photo-Realistic_Facial_Expression_Transfer_for_Virtual_Reality_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jourabloo_Robust_Egocentric_Photo-Realistic_Facial_Expression_Transfer_for_Virtual_Reality_CVPR_2022_paper.html | CVPR 2022 | null |
Does Robustness on ImageNet Transfer to Downstream Tasks? | Yutaro Yamada, Mayu Otani | As clean ImageNet accuracy nears its ceiling, the research community is increasingly more concerned about robust accuracy under distributional shifts. While a variety of methods have been proposed to robustify neural networks, these techniques often target models trained on ImageNet classification. At the same time, it is a common practice to use ImageNet pretrained backbones for downstream tasks such as object detection, semantic segmentation, and image classification from different domains. This raises a question: Can these robust image classifiers transfer robustness to downstream tasks? For object detection and semantic segmentation, we find that a vanilla Swin Transformer, a variant of Vision Transformer tailored for dense prediction tasks, transfers robustness better than Convolutional Neural Networks that are trained to be robust to the corrupted version of ImageNet. For CIFAR10 classification, we find that models that are robustified for ImageNet do not retain robustness when fully fine-tuned. These findings suggest that current robustification techniques tend to emphasize ImageNet evaluations. Moreover, network architecture is a strong source of robustness when we consider transfer learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yamada_Does_Robustness_on_ImageNet_Transfer_to_Downstream_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yamada_Does_Robustness_on_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03934 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yamada_Does_Robustness_on_ImageNet_Transfer_to_Downstream_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yamada_Does_Robustness_on_ImageNet_Transfer_to_Downstream_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples | Noo-ri Kim, Jee-Hyong Lee | Semi-supervised learning (SSL) is a method to make better models using a large number of easily accessible unlabeled data along with a small number of labeled data obtained at a high cost. Most of existing SSL studies focus on the cases where sufficient amount of labeled samples are available, tens to hundreds labeled samples for each class, which still requires a lot of labeling cost. In this paper, we focus on SSL environment with extremely scarce labeled samples, only 1 or 2 labeled samples per class, where most of existing methods fail to learn. We propose a propagation regularizer which can achieve efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias. In addition, for the realistic model selection in the absence of the validation dataset, we also propose a model selection method based on our propagation regularizer. The proposed methods show 70.9%, 30.3%, and 78.9% accuracy on CIFAR-10, CIFAR-100, SVHN dataset with just one labeled sample per class, which are improved by 8.9% to 120.2% compared to the existing approaches. And our proposed methods also show good performance on a higher resolution dataset, STL-10. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Propagation_Regularizer_for_Semi-Supervised_Learning_With_Extremely_Scarce_Labeled_Samples_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Propagation_Regularizer_for_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Propagation_Regularizer_for_Semi-Supervised_Learning_With_Extremely_Scarce_Labeled_Samples_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Propagation_Regularizer_for_Semi-Supervised_Learning_With_Extremely_Scarce_Labeled_Samples_CVPR_2022_paper.html | CVPR 2022 | null |
Bailando: 3D Dance Generation by Actor-Critic GPT With Choreographic Memory | Li Siyao, Weijiang Yu, Tianpei Gu, Chunze Lin, Quan Wang, Chen Qian, Chen Change Loy, Ziwei Liu | Driving 3D characters to dance following a piece of music is highly challenging due to the spatial constraints applied to poses by choreography norms. In addition, the generated dance sequence also needs to maintain temporal coherency with different music genres. To tackle these challenges, we propose a novel music-to-dance framework, Bailando, with two powerful components: 1) a choreographic memory that learns to summarize meaningful dancing units from 3D pose sequence to a quantized codebook, 2) an actor-critic Generative Pre-trained Transformer (GPT) that composes these units to a fluent dance coherent to the music. With the learned choreographic memory, dance generation is realized on the quantized units that meet high choreography standards, such that the generated dancing sequences are confined within the spatial constraints. To achieve synchronized alignment between diverse motion tempos and music beats, we introduce an actor-critic-based reinforcement learning scheme to the GPT with a newly-designed beat-align reward function. Extensive experiments on the standard benchmark demonstrate that our proposed framework achieves state-of-the-art performance both qualitatively and quantitatively. Notably, the learned choreographic memory is shown to discover human-interpretable dancing-style poses in an unsupervised manner. | https://openaccess.thecvf.com/content/CVPR2022/papers/Siyao_Bailando_3D_Dance_Generation_by_Actor-Critic_GPT_With_Choreographic_Memory_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Siyao_Bailando_3D_Dance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13055 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Siyao_Bailando_3D_Dance_Generation_by_Actor-Critic_GPT_With_Choreographic_Memory_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Siyao_Bailando_3D_Dance_Generation_by_Actor-Critic_GPT_With_Choreographic_Memory_CVPR_2022_paper.html | CVPR 2022 | null |
Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible Representations | Zhixuan Zhong, Liangyu Chai, Yang Zhou, Bailin Deng, Jia Pan, Shengfeng He | This paper presents a Generative prior ReciprocAted Invertible rescaling Network (GRAIN) for generating faithful high-resolution (HR) images from low-resolution (LR) invertible images with an extreme upscaling factor (64x). Previous researches have leveraged the prior knowledge of a pretrained GAN model to generate high-quality upscaling results. However, they fail to produce pixel-accurate results due to the highly ambiguous extreme mapping process. We remedy this problem by introducing a reciprocated invertible image rescaling process, in which high-resolution information can be delicately embedded into an invertible low-resolution image and generative prior for a faithful HR reconstruction. In particular, the invertible LR features not only carry significant HR semantics, but also are trained to predict scale-specific latent codes, yielding a preferable utilization of generative features. On the other hand, the enhanced generative prior is re-injected to the rescaling process, compensating the lost details of the invertible rescaling. Our reciprocal mechanism perfectly integrates the advantages of invertible encoding and generative prior, leading to the first feasible extreme rescaling solution. Extensive experiments demonstrate superior performance against state-of-the-art upscaling methods. Code is available at https://github.com/cszzx/GRAIN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_Faithful_Extreme_Rescaling_via_Generative_Prior_Reciprocated_Invertible_Representations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhong_Faithful_Extreme_Rescaling_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Faithful_Extreme_Rescaling_via_Generative_Prior_Reciprocated_Invertible_Representations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Faithful_Extreme_Rescaling_via_Generative_Prior_Reciprocated_Invertible_Representations_CVPR_2022_paper.html | CVPR 2022 | null |
Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection | Xian Qu, Changxing Ding, Xingao Li, Xubin Zhong, Dacheng Tao | Transformer-based methods have achieved great success in the field of human-object interaction (HOI) detection. However, these models tend to adopt semantically ambiguous queries, which lowers the transformer's representation learning power. Moreover, there are a very limited number of labeled human-object pairs for most images in existing datasets, which constrains the transformer's set prediction power. To handle the first problem, we propose an efficient knowledge distillation model, named Distillation using Oracle Queries (DOQ), which shares parameters between teacher and student networks. The teacher network adopts oracle queries that are semantically clear and generates high-quality decoder embeddings. By mimicking both the attention maps and decoder embeddings of the teacher network, the representation learning power of the student network is significantly promoted. To address the second problem, we introduce an efficient data augmentation method, named Context-Consistent Stitching (CCS), which generates complicated images online. Each new image is obtained by stitching labeled human-object pairs cropped from multiple training images. By selecting source images with similar context, the new synthesized image is made visually realistic. Our methods significantly promote both the accuracy and training efficiency of transformer-based HOI detection models. Experimental results show that our proposed approach consistently outperforms state-of-the-art methods on three benchmarks: HICO-DET, HOI-A, and V-COCO. Code will be released soon. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qu_Distillation_Using_Oracle_Queries_for_Transformer-Based_Human-Object_Interaction_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qu_Distillation_Using_Oracle_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qu_Distillation_Using_Oracle_Queries_for_Transformer-Based_Human-Object_Interaction_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qu_Distillation_Using_Oracle_Queries_for_Transformer-Based_Human-Object_Interaction_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Proto2Proto: Can You Recognize the Car, the Way I Do? | Monish Keswani, Sriranjani Ramakrishnan, Nishant Reddy, Vineeth N Balasubramanian | Prototypical methods have recently gained a lot of attention due to their intrinsic interpretable nature, which is obtained through the prototypes. With growing use cases of model reuse and distillation, there is a need to also study transfer of interpretability from one model to another. We present Proto2Proto, a novel method to transfer interpretability of one prototypical part network to another via knowledge distillation. Our approach aims to add interpretability to the "dark" knowledge transferred from the teacher to the shallower student model. We propose two novel losses: "Global Explanation" loss and "Patch-Prototype Correspondence" loss to facilitate such a transfer. Global Explanation loss forces the student prototypes to be close to teacher prototypes, and Patch-Prototype Correspondence loss enforces the local representations of the student to be similar to that of the teacher. Further, we propose three novel metrics to evaluate the student's proximity to the teacher as measures of interpretability transfer in our settings. We qualitatively and quantitatively demonstrate the effectiveness of our method on CUB-200-2011 and Stanford Cars datasets. Our experiments show that the proposed method indeed achieves interpretability transfer from teacher to student while simultaneously exhibiting competitive performance. The code is available at https://github.com/archmaester/proto2proto | https://openaccess.thecvf.com/content/CVPR2022/papers/Keswani_Proto2Proto_Can_You_Recognize_the_Car_the_Way_I_Do_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Keswani_Proto2Proto_Can_You_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.11830 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Keswani_Proto2Proto_Can_You_Recognize_the_Car_the_Way_I_Do_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Keswani_Proto2Proto_Can_You_Recognize_the_Car_the_Way_I_Do_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation | Nan Xue, Tianfu Wu, Gui-Song Xia, Liangpei Zhang | This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose. Specifically, our approach learns the keypoint attraction maps (KAMs) from the local keypoints expansion maps (KEMs) in small local windows in the first step, which are subsequently treated as dynamic convolutional kernels on the keypoints-focused global heatmaps for contextual adaptation, achieving accurate multi-person pose estimation. Our method is end-to-end trainable with near real-time inference speed in a single forward pass, obtaining state-of-the-art performance on the COCO keypoint benchmark for bottom-up human pose estimation. With the COCO trained model, our method also outperforms prior arts by a large margin on the challenging OCHuman dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_Learning_Local-Global_Contextual_Adaptation_for_Multi-Person_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xue_Learning_Local-Global_Contextual_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.03622 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Learning_Local-Global_Contextual_Adaptation_for_Multi-Person_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Learning_Local-Global_Contextual_Adaptation_for_Multi-Person_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Video Representations of Human Motion From Synthetic Data | Xi Guo, Wei Wu, Dongliang Wang, Jing Su, Haisheng Su, Weihao Gan, Jian Huang, Qin Yang | In this paper, we take an early step towards video representation learning of human actions with the help of largescale synthetic videos, particularly for human motion representation enhancement. Specifically, we first introduce an automatic action-related video synthesis pipeline based on a photorealistic video game. A large-scale human action dataset named GATA (GTA Animation Transformed Actions) is then built by the proposed pipeline, which includes 8.1 million action clips spanning over 28K action classes. Based on the presented dataset, we design a contrastive learning framework for human motion representation learning, which shows significant performance improvements on several typical video datasets for action recognition, e.g., Charades, HAA 500 and NTU-RGB. Besides, we further explore a domain adaptation method based on cross-domain positive pairs mining to alleviate the domain gap between synthetic and realistic data. Extensive properties analyses of learned representation are conducted to demonstrate the effectiveness of the proposed dataset for enhancing human motion representation learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Learning_Video_Representations_of_Human_Motion_From_Synthetic_Data_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Learning_Video_Representations_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Learning_Video_Representations_of_Human_Motion_From_Synthetic_Data_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Learning_Video_Representations_of_Human_Motion_From_Synthetic_Data_CVPR_2022_paper.html | CVPR 2022 | null |
TVConv: Efficient Translation Variant Convolution for Layout-Aware Visual Processing | Jierun Chen, Tianlang He, Weipeng Zhuo, Li Ma, Sangtae Ha, S.-H. Gary Chan | As convolution has empowered many smart applications, dynamic convolution further equips it with the ability to adapt to diverse inputs. However, the static and dynamic convolutions are either layout-agnostic or computation-heavy, making it inappropriate for layout-specific applications, e.g., face recognition and medical image segmentation. We observe that these applications naturally exhibit the characteristics of large intra-image (spatial) variance and small cross-image variance. This observation motivates our efficient translation variant convolution (TVConv) for layout-aware visual processing. Technically, TVConv is composed of affinity maps and a weight-generating block. While affinity maps depict pixel-paired relationships gracefully, the weight-generating block can be explicitly overparameterized for better training while maintaining efficient inference. Although conceptually simple, TVConv significantly improves the efficiency of the convolution and can be readily plugged into various network architectures. Extensive experiments on face recognition show that TVConv reduces the computational cost by up to 3.1x and improves the corresponding throughput by 2.3x while maintaining a high accuracy compared to the depthwise convolution. Moreover, for the same computation cost, we boost the mean accuracy by up to 4.21%. We also conduct experiments on the optic disc/cup segmentation task and obtain better generalization performance, which helps mitigate the critical data scarcity issue. Code is available at https://github.com/JierunChen/TVConv. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_TVConv_Efficient_Translation_Variant_Convolution_for_Layout-Aware_Visual_Processing_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.10489 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_TVConv_Efficient_Translation_Variant_Convolution_for_Layout-Aware_Visual_Processing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_TVConv_Efficient_Translation_Variant_Convolution_for_Layout-Aware_Visual_Processing_CVPR_2022_paper.html | CVPR 2022 | null |
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution | Xiaoqian Xu, Pengxu Wei, Weikai Chen, Yang Liu, Mingzhi Mao, Liang Lin, Guanbin Li | Due to the sophisticated imaging process, an identical scene captured by different cameras could exhibit distinct imaging patterns, introducing distinct proficiency among the super-resolution (SR) models trained on images from different devices. In this paper, we investigate a novel and practical task coded cross-device SR, which strives to adapt a real-world SR model trained on the paired images captured by one camera to low-resolution (LR) images captured by arbitrary target devices. The proposed task is highly challenging due to the absence of paired data from various imaging devices. To address this issue, we propose an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera. DADA employs the Domain-Invariant Attention (DIA) module to establish the basis of target model training even without HR supervision. Furthermore, the dual framework of DADA facilitates an Inter-domain Adversarial Adaptation (InterAA) in one branch for two LR input images from two domains, and an Intra-domain Adversarial Adaptation (IntraAA) in two branches for an LR input image. InterAA and IntraAA together improve the model transferability from the source domain to the target. We empirically conduct experiments under six Real to Real adaptation settings among three different cameras, and achieve superior performance compared with existing state-of-the-art approaches. We also evaluate the proposed DADA to address the adaptation to the video camera, which presents a promising research topic to promote the wide applications of real-world super-resolution. Our source code is publicly available at https://github.com/lonelyhope/DADA.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Dual_Adversarial_Adaptation_for_Cross-Device_Real-World_Image_Super-Resolution_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.03524 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Dual_Adversarial_Adaptation_for_Cross-Device_Real-World_Image_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Dual_Adversarial_Adaptation_for_Cross-Device_Real-World_Image_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
FS6D: Few-Shot 6D Pose Estimation of Novel Objects | Yisheng He, Yao Wang, Haoqiang Fan, Jian Sun, Qifeng Chen | 6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models. In this work, we study a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unknown object by a few support views without extra training. To tackle the problem, we point out the importance of fully exploring the appearance and geometric relationship between the given support views and query scene patches and propose a dense prototypes matching framework by extracting and matching dense RGBD prototypes with transformers. Moreover, we show that the priors from diverse appearances and shapes are crucial to the generalization capability under the problem setting and thus propose a large-scale RGBD photorealistic dataset (ShapeNet6D) for network pre-training. A simple and effective online texture blending approach is also introduced to eliminate the domain gap from the synthesis dataset, which enriches appearance diversity at a low cost. Finally, we discuss possible solutions to this problem and establish benchmarks on popular datasets to facilitate future research. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_FS6D_Few-Shot_6D_Pose_Estimation_of_Novel_Objects_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_FS6D_Few-Shot_6D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14628 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_FS6D_Few-Shot_6D_Pose_Estimation_of_Novel_Objects_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_FS6D_Few-Shot_6D_Pose_Estimation_of_Novel_Objects_CVPR_2022_paper.html | CVPR 2022 | null |
Habitat-Web: Learning Embodied Object-Search Strategies From Human Demonstrations at Scale | Ram Ramrakhya, Eric Undersander, Dhruv Batra, Abhishek Das | We present a large-scale study of imitating human demonstrations on tasks that require a virtual robot to search for objects in new environments - (1) ObjectGoal Navigation (e.g. 'find & go to a chair') and (2) Pick&Place (e.g. 'find mug, pick mug, find counter, place mug on counter'). First, we develop a virtual teleoperation data-collection infrastructure - connecting Habitat simulator running in a web browser to Amazon Mechanical Turk, allowing remote users to teleoperate virtual robots, safely and at scale. We collect 80k demonstrations for ObjectNav and 12k demonstrations for Pick&Place, which is an order of magnitude larger than existing human demonstration datasets in simulation or on real robots. Our virtual teleoperation data contains 29.3M actions, and is equivalent to 22.6k hours of real-world teleoperation time, and illustrates rich, diverse strategies for solving the tasks. Second, we use this data to answer the question - how does large-scale imitation learning (IL) (which has not been hitherto possible) compare to reinforcement learning (RL) (which is the status quo)? On ObjectNav, we find that IL (with no bells or whistles) using 70k human demonstrations outperforms RL using 240k agent-gathered trajectories. This effectively establishes an 'exchange rate' - a single human demonstration appears to be worth 4 agent-gathered ones. More importantly, we find the IL-trained agent learns efficient object-search behavior from humans - it peeks into rooms, checks corners for small objects, turns in place to get a panoramic view - none of these are exhibited as prominently by the RL agent, and to induce these behaviors via contemporary RL techniques would require tedious reward engineering. Finally, accuracy vs. training data size plots show promising scaling behavior, suggesting that simply collecting more demonstrations is likely to advance the state of art further. On Pick&Place, the comparison is starker - IL agents achieve 18% success on episodes with new object-receptacle locations when trained with 9.5k human demonstrations, while RL agents fail to get beyond 0%. Overall, our work provides compelling evidence for investing in large-scale imitation learning. Project page: https://ram81.github.io/projects/habitat-web. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ramrakhya_Habitat-Web_Learning_Embodied_Object-Search_Strategies_From_Human_Demonstrations_at_Scale_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ramrakhya_Habitat-Web_Learning_Embodied_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ramrakhya_Habitat-Web_Learning_Embodied_Object-Search_Strategies_From_Human_Demonstrations_at_Scale_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ramrakhya_Habitat-Web_Learning_Embodied_Object-Search_Strategies_From_Human_Demonstrations_at_Scale_CVPR_2022_paper.html | CVPR 2022 | null |
The Probabilistic Normal Epipolar Constraint for Frame-to-Frame Rotation Optimization Under Uncertain Feature Positions | Dominik Muhle, Lukas Koestler, Nikolaus Demmel, Florian Bernard, Daniel Cremers | The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar constraint (NEC). However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is highly dependent on accurate feature positions in the target frame. In this work, we introduce the probabilistic normal epipolar constraint (PNEC) that overcomes this limitation by accounting for anisotropic and inhomogeneous uncertainties in the feature positions. To this end, we propose a novel objective function, along with an efficient optimization scheme that effectively minimizes our objective while maintaining real-time performance. In experiments on synthetic data, we demonstrate that the novel PNEC yields more accurate rotation estimates than the original NEC and several popular relative rotation estimation algorithms. Furthermore, we integrate the proposed method into a state-of-the-art monocular rotation-only odometry system and achieve consistently improved results for the real-world KITTI dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Muhle_The_Probabilistic_Normal_Epipolar_Constraint_for_Frame-to-Frame_Rotation_Optimization_Under_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Muhle_The_Probabilistic_Normal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02256 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Muhle_The_Probabilistic_Normal_Epipolar_Constraint_for_Frame-to-Frame_Rotation_Optimization_Under_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Muhle_The_Probabilistic_Normal_Epipolar_Constraint_for_Frame-to-Frame_Rotation_Optimization_Under_CVPR_2022_paper.html | CVPR 2022 | null |
Vision-Language Pre-Training for Boosting Scene Text Detectors | Sibo Song, Jianqiang Wan, Zhibo Yang, Jun Tang, Wenqing Cheng, Xiang Bai, Cong Yao | Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision and language, since text is the written form of language. Concretely, we propose to learn contextualized, joint representations through vision-language pre-training, for the sake of enhancing the performance of scene text detectors. Towards this end, we devise a pre-training architecture with an image encoder, a text encoder and a cross-modal encoder, as well as three pretext tasks: image-text contrastive learning (ITC), masked language modeling (MLM) and word-in-image prediction (WIP). The pre-trained model is able to produce more informative representations with richer semantics, which could readily benefit existing scene text detectors (such as EAST and PSENet) in the down-stream text detection task. Extensive experiments on standard benchmarks demonstrate that the proposed paradigm can significantly improve the performance of various representative text detectors, outperforming previous pre-training approaches. The code and pre-trained models will be publicly released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Song_Vision-Language_Pre-Training_for_Boosting_Scene_Text_Detectors_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.13867 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Song_Vision-Language_Pre-Training_for_Boosting_Scene_Text_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Song_Vision-Language_Pre-Training_for_Boosting_Scene_Text_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
Reflection and Rotation Symmetry Detection via Equivariant Learning | Ahyun Seo, Byungjin Kim, Suha Kwak, Minsu Cho | The inherent challenge of detecting symmetries stems from arbitrary orientations of symmetry patterns; a reflection symmetry mirrors itself against an axis with a specific orientation while a rotation symmetry matches its rotated copy with a specific orientation. Discovering such symmetry patterns from an image thus benefits from an equivariant feature representation, which varies consistently with reflection and rotation of the image. In this work, we introduce a group-equivariant convolutional network for symmetry detection, dubbed EquiSym, which leverages equivariant feature maps with respect to a dihedral group of reflection and rotation. The proposed network is built end-to-end with dihedrally-equivariant layers and trained to output a spatial map for reflection axes or rotation centers. We also present a new dataset, DENse and DIverse symmetry (DENDI), which mitigates limitations of existing benchmarks for reflection and rotation symmetry detection. Experiments show that our method achieves the state of the arts in symmetry detection on LDRS and DENDI datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Seo_Reflection_and_Rotation_Symmetry_Detection_via_Equivariant_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Seo_Reflection_and_Rotation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16787 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Seo_Reflection_and_Rotation_Symmetry_Detection_via_Equivariant_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Seo_Reflection_and_Rotation_Symmetry_Detection_via_Equivariant_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
BoostMIS: Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation | Wenqiao Zhang, Lei Zhu, James Hallinan, Shengyu Zhang, Andrew Makmur, Qingpeng Cai, Beng Chin Ooi | In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models: (1) BoostMIS can adaptively leverage the cluster assumption and consistency regularization of the unlabeled data according to the current learning status. This strategy can adaptively generate one-hot "hard" labels converted from task model predictions for better task model training. (2) For the unselected unlabeled images with low confidence, we introduce an Active learning (AL) algorithm to find the informative samples as the annotation candidates by exploiting virtual adversarial perturbation and model's density-aware entropy. These informative candidates are subsequently fed into the next training cycle for better SSL label propagation. Notably, the adaptive pseudo-labeling and informative active annotation form a learning closed-loop that are mutually collaborative to boost medical image SSL. To verify the effectiveness of the proposed method, we collected a metastatic epidural spinal cord compression (MESCC) dataset that aims to optimize MESCC diagnosis and classification for improved specialist referral and treatment. We conducted an extensive experimental study of BoostMIS on MESCC and another public dataset COVIDx. The experimental results verify our framework's effectiveness and generalisability for different medical image datasets with a significant improvement over various state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_BoostMIS_Boosting_Medical_Image_Semi-Supervised_Learning_With_Adaptive_Pseudo_Labeling_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.02533 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_BoostMIS_Boosting_Medical_Image_Semi-Supervised_Learning_With_Adaptive_Pseudo_Labeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_BoostMIS_Boosting_Medical_Image_Semi-Supervised_Learning_With_Adaptive_Pseudo_Labeling_CVPR_2022_paper.html | CVPR 2022 | null |
Simple but Effective: CLIP Embeddings for Embodied AI | Apoorv Khandelwal, Luca Weihs, Roozbeh Mottaghi, Aniruddha Kembhavi | Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial for a range of visual tasks from classification and detection to captioning and image manipulation. We investigate the effectiveness of CLIP visual backbones for Embodied AI tasks. We build incredibly simple baselines, named EmbCLIP, with no task specific architectures, inductive biases (such as the use of semantic maps), auxiliary tasks during training, or depth maps--yet we find that our improved baselines perform very well across a range of tasks and simulators. EmbCLIP tops the RoboTHOR ObjectNav leaderboard by a huge margin of 20 pts (Success Rate). It tops the iTHOR 1-Phase Rearrangement leaderboard, beating the next best submission, which employs Active Neural Mapping, and more than doubling the % Fixed Strict metric (0.08 to 0.17). It also beats the winners of the 2021 Habitat ObjectNav Challenge, which employ auxiliary tasks, depth maps, and human demonstrations, and those of the 2019 Habitat PointNav Challenge. We evaluate the ability of CLIP's visual representations at capturing semantic information about input observations--primitives that are useful for navigation-heavy embodied tasks--and find that CLIP's representations encode these primitives more effectively than ImageNet-pretrained backbones. Finally, we extend one of our baselines, producing an agent capable of zero-shot object navigation that can navigate to objects that were not used as targets during training. Our code and models are available at https://github.com/allenai/embodied-clip. | https://openaccess.thecvf.com/content/CVPR2022/papers/Khandelwal_Simple_but_Effective_CLIP_Embeddings_for_Embodied_AI_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Khandelwal_Simple_but_Effective_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09888 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Khandelwal_Simple_but_Effective_CLIP_Embeddings_for_Embodied_AI_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Khandelwal_Simple_but_Effective_CLIP_Embeddings_for_Embodied_AI_CVPR_2022_paper.html | CVPR 2022 | null |
NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition | Hao Liu, Xinghua Jiang, Xin Li, Zhimin Bao, Deqiang Jiang, Bo Ren | Recently, Vision Transformers (ViT), with the self-attention (SA) as the de facto ingredients, have demonstrated great potential in the computer vision community. For the sake of trade-off between efficiency and performance, a group of works merely perform SA operation within local patches, whereas the global contextual information is abandoned, which would be indispensable for visual recognition tasks. To solve the issue, the subsequent global-local ViTs take a stab at marrying local SA with global one in parallel or alternative way in the model. Nevertheless, the exhaustively combined local and global context may exist redundancy for various visual data, and the receptive field within each layer is fixed. Alternatively, a more graceful way is that global and local context can adaptively contribute per se to accommodate different visual data. To achieve this goal, we in this paper propose a novel ViT architecture, termed NomMer, which can dynamically Nominate the synergistic global-local context in vision transforMer. By investigating the working pattern of our proposed NomMer, we further explore what context information is focused. Beneficial from this "dynamic nomination" mechanism, without bells and whistles, the NomMer can not only achieve 84.5% Top-1 classification accuracy on ImageNet with only 73M parameters, but also show promising performance on dense prediction tasks, i.e., object detection and semantic segmentation. The code and models are publicly available at https://github.com/TencentYoutuResearch/VisualRecognition-NomMer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_NomMer_Nominate_Synergistic_Context_in_Vision_Transformer_for_Visual_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_NomMer_Nominate_Synergistic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12994 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_NomMer_Nominate_Synergistic_Context_in_Vision_Transformer_for_Visual_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_NomMer_Nominate_Synergistic_Context_in_Vision_Transformer_for_Visual_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction | Yunze Liu, Yun Liu, Che Jiang, Kangbo Lyu, Weikang Wan, Hao Shen, Boqiang Liang, Zhoujie Fu, He Wang, Li Yi | We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by 9 participants interacting with 800 different object instances from 16 categories over 610 different indoor rooms. Frame-wise annotations for panoptic segmentation, motion segmentation, 3D hand pose, category-level object pose and hand action have also been provided, together with reconstructed object meshes and scene point clouds. With HOI4D, we establish three benchmarking tasks to promote category-level HOI from 4D visual signals including semantic segmentation of 4D dynamic point cloud sequences, category-level object pose tracking, and egocentric action segmentation with diverse interaction targets. In-depth analysis shows HOI4D poses great challenges to existing methods and produces huge research opportunities. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_HOI4D_A_4D_Egocentric_Dataset_for_Category-Level_Human-Object_Interaction_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.01577 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_HOI4D_A_4D_Egocentric_Dataset_for_Category-Level_Human-Object_Interaction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_HOI4D_A_4D_Egocentric_Dataset_for_Category-Level_Human-Object_Interaction_CVPR_2022_paper.html | CVPR 2022 | null |
Collaborative Transformers for Grounded Situation Recognition | Junhyeong Cho, Youngseok Yoon, Suha Kwak | Grounded situation recognition is the task of predicting the main activity, entities playing certain roles within the activity, and bounding-box groundings of the entities in the given image. To effectively deal with this challenging task, we introduce a novel approach where the two processes for activity classification and entity estimation are interactive and complementary. To implement this idea, we propose Collaborative Glance-Gaze TransFormer (CoFormer) that consists of two modules: Glance transformer for activity classification and Gaze transformer for entity estimation. Glance transformer predicts the main activity with the help of Gaze transformer that analyzes entities and their relations, while Gaze transformer estimates the grounded entities by focusing only on the entities relevant to the activity predicted by Glance transformer. Our CoFormer achieves the state of the art in all evaluation metrics on the SWiG dataset. Training code and model weights are available at https://github.com/jhcho99/CoFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cho_Collaborative_Transformers_for_Grounded_Situation_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cho_Collaborative_Transformers_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16518 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cho_Collaborative_Transformers_for_Grounded_Situation_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cho_Collaborative_Transformers_for_Grounded_Situation_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
DyRep: Bootstrapping Training With Dynamic Re-Parameterization | Tao Huang, Shan You, Bohan Zhang, Yuxuan Du, Fei Wang, Chen Qian, Chang Xu | Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize all operations into an augmented network, including those that rarely contribute to the model's performance. As such, the price to pay is an expensive computational overhead to manipulate these unnecessary behaviors. To eliminate the above caveats, we aim to bootstrap the training with minimal cost by devising a dynamic re-parameterization (DyRep) method, which encodes Rep technique into the training process that dynamically evolves the network structures. Concretely, our proposal adaptively finds the operations which contribute most to the loss in the network, and applies Rep to enhance their representational capacity. Besides, to suppress the noisy and redundant operations introduced by Rep, we devise a de-parameterization technique for a more compact re-parameterization. With this regard, DyRep is more efficient than Rep since it smoothly evolves the given network instead of constructing an over-parameterized network. Experimental results demonstrate our effectiveness, e.g., DyRep improves the accuracy of ResNet-18 by 2.04% on ImageNet and reduces 22% runtime over the baseline. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_DyRep_Bootstrapping_Training_With_Dynamic_Re-Parameterization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_DyRep_Bootstrapping_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12868 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_DyRep_Bootstrapping_Training_With_Dynamic_Re-Parameterization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_DyRep_Bootstrapping_Training_With_Dynamic_Re-Parameterization_CVPR_2022_paper.html | CVPR 2022 | null |
Not All Labels Are Equal: Rationalizing the Labeling Costs for Training Object Detection | Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixé, Jose M. Alvarez | Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learning strategies have been proposed, typically based on the confidence of the detector. However, these methods are biased towards high-performing classes and can lead to acquired datasets that are not good representatives of the testing set data. In this work, we propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs well in all classes. Furthermore, our method leverages auto-labeling to suppress a potential distribution drift while boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active learning methods, yielding up to a 7.7% improvement in mAP, or up to 82% reduction in labeling cost. Code will be released upon acceptance of the paper. | https://openaccess.thecvf.com/content/CVPR2022/papers/Elezi_Not_All_Labels_Are_Equal_Rationalizing_the_Labeling_Costs_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Elezi_Not_All_Labels_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.11921 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Elezi_Not_All_Labels_Are_Equal_Rationalizing_the_Labeling_Costs_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Elezi_Not_All_Labels_Are_Equal_Rationalizing_the_Labeling_Costs_for_CVPR_2022_paper.html | CVPR 2022 | null |
CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild | Yang You, Ruoxi Shi, Weiming Wang, Cewu Lu | In this paper, we tackle the problem of category-level 9D pose estimation in the wild, given a single RGB-D frame. Using supervised data of real-world 9D poses is tedious and erroneous, and also fails to generalize to unseen scenarios. Besides, category-level pose estimation requires a method to be able to generalize to unseen objects at test time, which is also challenging. Drawing inspirations from traditional point pair features (PPFs), in this paper, we design a novel Category-level PPF (CPPF) voting method to achieve accurate, robust and generalizable 9D pose estimation in the wild. To obtain robust pose estimation, we sample numerous point pairs on an object, and for each pair our model predicts necessary SE(3)-invariant voting statistics on object centers, orientations and scales. A novel coarse-to-fine voting algorithm is proposed to eliminate noisy point pair samples and generate final predictions from the population. To get rid of false positives in the orientation voting process, an auxiliary binary disambiguating classification task is introduced for each sampled point pair. In order to detect objects in the wild, we carefully design our sim-to-real pipeline by training on synthetic point clouds only, unless objects have ambiguous poses in geometry. Under this circumstance, color information is leveraged to disambiguate these poses. Results on standard benchmarks show that our method is on par with current state of the arts with real-world training data. Extensive experiments further show that our method is robust to noise and gives promising results under extremely challenging scenarios. Our code is available on https://github.com/qq456cvb/CPPF. | https://openaccess.thecvf.com/content/CVPR2022/papers/You_CPPF_Towards_Robust_Category-Level_9D_Pose_Estimation_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/You_CPPF_Towards_Robust_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03089 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/You_CPPF_Towards_Robust_Category-Level_9D_Pose_Estimation_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/You_CPPF_Towards_Robust_Category-Level_9D_Pose_Estimation_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
Interact Before Align: Leveraging Cross-Modal Knowledge for Domain Adaptive Action Recognition | Lijin Yang, Yifei Huang, Yusuke Sugano, Yoichi Sato | Unsupervised domain adaptive video action recognition aims to recognize actions of a target domain using a model trained with only out-of-domain (source) annotations. The inherent complexity of videos makes this task challenging but also provides ground for leveraging multi-modal inputs (e.g., RGB, Flow, Audio). Most previous works utilize the multi-modal information by either aligning each modality individually or learning representation via cross-modal self-supervision. Different from previous works, we find that the cross-domain alignment can be more effectively done by using cross-modal interaction first. Cross-modal knowledge interaction allows other modalities to supplement missing transferable information because of the cross-modal complementarity. Also, the most transferable aspects of data can be highlighted using cross-modal consensus. In this work, we present a novel model that jointly considers these two characteristics for domain adaptive action recognition. We achieve this by implementing two modules, where the first module exchanges complementary transferable information across modalities through the semantic space, and the second module finds the most transferable spatial region based on the consensus of all modalities. Extensive experiments validate that our proposed method can significantly outperform the state-of-the-art methods on multiple benchmark datasets, including the complex fine-grained dataset EPIC-Kitchens-100. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Interact_Before_Align_Leveraging_Cross-Modal_Knowledge_for_Domain_Adaptive_Action_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Interact_Before_Align_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Interact_Before_Align_Leveraging_Cross-Modal_Knowledge_for_Domain_Adaptive_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Interact_Before_Align_Leveraging_Cross-Modal_Knowledge_for_Domain_Adaptive_Action_CVPR_2022_paper.html | CVPR 2022 | null |
Interactive Disentanglement: Learning Concepts by Interacting With Their Prototype Representations | Wolfgang Stammer, Marius Memmel, Patrick Schramowski, Kristian Kersting | Learning visual concepts from raw images without strong supervision is a challenging task. In this work, we show the advantages of prototype representations for understanding and revising the latent space of neural concept learners. For this purpose, we introduce interactive Concept Swapping Networks (iCSNs), a novel framework for learning concept-grounded representations via weak supervision and implicit prototype representations. iCSNs learn to bind conceptual information to specific prototype slots by swapping the latent representations of paired images. This semantically grounded and discrete latent space facilitates human understanding and human-machine interaction. We support this claim by conducting experiments on our novel data set "Elementary Concept Reasoning" (ECR), focusing on visual concepts shared by geometric objects. | https://openaccess.thecvf.com/content/CVPR2022/papers/Stammer_Interactive_Disentanglement_Learning_Concepts_by_Interacting_With_Their_Prototype_Representations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Stammer_Interactive_Disentanglement_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02290 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Stammer_Interactive_Disentanglement_Learning_Concepts_by_Interacting_With_Their_Prototype_Representations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Stammer_Interactive_Disentanglement_Learning_Concepts_by_Interacting_With_Their_Prototype_Representations_CVPR_2022_paper.html | CVPR 2022 | null |
CDGNet: Class Distribution Guided Network for Human Parsing | Kunliang Liu, Ouk Choi, Jianming Wang, Wonjun Hwang | The objective of human parsing is to partition a human in an image into constituent parts. This task involves labeling each pixel of the human image according to the classes. Since the human body comprises hierarchically structured parts, each body part of an image can have its sole position distribution characteristic. Probably, a human head is less likely to be under the feet, and arms are more likely to be near the torso. Inspired by this observation, we make instance class distributions by accumulating the original human parsing label in the horizontal and vertical directions, which can be utilized as supervision signals. Using these horizontal and vertical class distribution labels, the network is guided to exploit the intrinsic position distribution of each class. We combine two guided features to form a spatial guidance map, which is then superimposed onto the baseline network by multiplication and concatenation to distinguish the human parts precisely. We conducted extensive experiments to demonstrate the effectiveness and superiority of our method on three well-known benchmarks: LIP, ATR, and CIHP databases. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_CDGNet_Class_Distribution_Guided_Network_for_Human_Parsing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_CDGNet_Class_Distribution_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14173 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_CDGNet_Class_Distribution_Guided_Network_for_Human_Parsing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_CDGNet_Class_Distribution_Guided_Network_for_Human_Parsing_CVPR_2022_paper.html | CVPR 2022 | null |
Recall@k Surrogate Loss With Large Batches and Similarity Mixup | Yash Patel, Giorgos Tolias, Jiří Matas | This work focuses on learning deep visual representation models for retrieval by exploring the interplay between a new loss function, the batch size, and a new regularization approach. Direct optimization, by gradient descent, of an evaluation metric, is not possible when it is non-differentiable, which is the case for recall in retrieval. A differentiable surrogate loss for the recall is proposed in this work. Using an implementation that sidesteps the hardware constraints of the GPU memory, the method trains with a very large batch size, which is essential for metrics computed on the entire retrieval database. It is assisted by an efficient mixup regularization approach that operates on pairwise scalar similarities and virtually increases the batch size further. The suggested method achieves state-of-the-art performance in several image retrieval benchmarks when used for deep metric learning. For instance-level recognition, the method outperforms similar approaches that train using an approximation of average precision. | https://openaccess.thecvf.com/content/CVPR2022/papers/Patel_Recallk_Surrogate_Loss_With_Large_Batches_and_Similarity_Mixup_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Patel_Recallk_Surrogate_Loss_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Patel_Recallk_Surrogate_Loss_With_Large_Batches_and_Similarity_Mixup_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Patel_Recallk_Surrogate_Loss_With_Large_Batches_and_Similarity_Mixup_CVPR_2022_paper.html | CVPR 2022 | null |
Direct Voxel Grid Optimization: Super-Fast Convergence for Radiance Fields Reconstruction | Cheng Sun, Min Sun, Hwann-Tzong Chen | We present a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses. This task, which is often applied to novel view synthesis, is recently revolutionized by Neural Radiance Field (NeRF) for its state-of-the-art quality and flexibility. However, NeRF and its variants require a lengthy training time ranging from hours to days for a single scene. In contrast, our approach achieves NeRF-comparable quality and converges rapidly from scratch in less than 15 minutes with a single GPU. We adopt a representation consisting of a density voxel grid for scene geometry and a feature voxel grid with a shallow network for complex view-dependent appearance. Modeling with explicit and discretized volume representations is not new, but we propose two simple yet non-trivial techniques that contribute to fast convergence speed and high-quality output. First, we introduce the post-activation interpolation on voxel density, which is capable of producing sharp surfaces in lower grid resolution. Second, direct voxel density optimization is prone to suboptimal geometry solutions, so we robustify the optimization process by imposing several priors. Finally, evaluation on five inward-facing benchmarks shows that our method matches, if not surpasses, NeRF's quality, yet it only takes about 15 minutes to train from scratch for a new scene. We will make our code publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Direct_Voxel_Grid_Optimization_Super-Fast_Convergence_for_Radiance_Fields_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Direct_Voxel_Grid_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.11215 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Direct_Voxel_Grid_Optimization_Super-Fast_Convergence_for_Radiance_Fields_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Direct_Voxel_Grid_Optimization_Super-Fast_Convergence_for_Radiance_Fields_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
Continual Test-Time Domain Adaptation | Qin Wang, Olga Fink, Luc Van Gool, Dengxin Dai | Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time. Existing methods, which are mostly based on self-training and entropy regularization, can suffer from these non-stationary environments. Due to the distribution shift over time in the target domain, pseudo-labels become unreliable. The noisy pseudo-labels can further lead to error accumulation and catastrophic forgetting. To tackle these issues, we propose a continual test-time adaptation approach (CoTTA) which comprises two parts. Firstly, we propose to reduce the error accumulation by using weight-averaged and augmentation-averaged predictions which are often more accurate. On the other hand, to avoid catastrophic forgetting, we propose to stochastically restore a small part of the neurons to the source pre-trained weights during each iteration to help preserve source knowledge in the long-term. The proposed method enables the long-term adaptation for all parameters in the network. CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models. We demonstrate the effectiveness of our approach on four classification tasks and a segmentation task for continual test-time adaptation, on which we outperform existing methods. Our code is available at https://qin.ee/cotta. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Continual_Test-Time_Domain_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Continual_Test-Time_Domain_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13591 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Continual_Test-Time_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Continual_Test-Time_Domain_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement | Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, Jianmin Jiang | Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement. However, the commonly used hand-crafted priors and optimization-driven solutions lead to the absence of adaptivity and efficiency. To address these issues, in this paper, we propose a Retinex-based deep unfolding network (URetinex-Net), which unfolds an optimization problem into a learnable network to decompose a low-light image into reflectance and illumination layers. By formulating the decomposition problem as an implicit priors regularized model, three learning-based modules are carefully designed, responsible for data-dependent initialization, high-efficient unfolding optimization, and user-specified illumination enhancement, respectively. Particularly, the proposed unfolding optimization module, introducing two networks to adaptively fit implicit priors in data-driven manner, can realize noise suppression and details preservation for the final decomposition results. Extensive experiments on real-world low-light images qualitatively and quantitatively demonstrate the effectiveness and superiority of the proposed method over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_URetinex-Net_Retinex-Based_Deep_Unfolding_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_URetinex-Net_Retinex-Based_Deep_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_URetinex-Net_Retinex-Based_Deep_Unfolding_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_URetinex-Net_Retinex-Based_Deep_Unfolding_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Multi-Domain Single Image Dehazing via Test-Time Training | Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, Keyan Wang | Recent years have witnessed significant progress in the area of single image dehazing, thanks to the employment of deep neural networks and diverse datasets. Most of the existing methods perform well when the training and testing are conducted on a single dataset. However, they are not able to handle different types of hazy images using a dehazing model trained on a particular dataset. One possible remedy is to perform training on multiple datasets jointly. However, we observe that this training strategy tends to compromise the model performance on individual datasets. Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest. Specifically, during the test time, the helper network evaluates the quality of the dehazing results, then directs the dehazing network to improve the quality by adjusting its parameters via self-supervision. Nevertheless, the inclusion of the helper network does not automatically ensure the desired performance improvement. For this reason, a meta-learning approach is employed to make the objectives of the dehazing and helper networks consistent with each other. We demonstrate the effectiveness of the proposed method by providing extensive supporting experiments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Multi-Domain_Single_Image_Dehazing_via_Test-Time_Training_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Multi-Domain_Single_Image_Dehazing_via_Test-Time_Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Multi-Domain_Single_Image_Dehazing_via_Test-Time_Training_CVPR_2022_paper.html | CVPR 2022 | null |
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces From 3D MRI Scans With Geometric Deep Neural Networks | Fabian Bongratz, Anne-Marie Rickmann, Sebastian Pölsterl, Christian Wachinger | The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. Although traditional and deep learning-based algorithmic pipelines exist for this purpose, they have two major drawbacks: lengthy runtimes of multiple hours (traditional) or intricate post-processing, such as mesh extraction and topology correction (deep learning-based). In this work, we address both of these issues and propose Vox2Cortex, a deep learning-based algorithm that directly yields topologically correct, three-dimensional meshes of the boundaries of the cortex. Vox2Cortex leverages convolutional and graph convolutional neural networks to deform an initial template to the densely folded geometry of the cortex represented by an input MRI scan. We show in extensive experiments on three brain MRI datasets that our meshes are as accurate as the ones reconstructed by state-of-the-art methods in the field, without the need for time- and resource-intensive post-processing. To accurately reconstruct the tightly folded cortex, we work with meshes containing about 168,000 vertices at test time, scaling deep explicit reconstruction methods to a new level. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bongratz_Vox2Cortex_Fast_Explicit_Reconstruction_of_Cortical_Surfaces_From_3D_MRI_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bongratz_Vox2Cortex_Fast_Explicit_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bongratz_Vox2Cortex_Fast_Explicit_Reconstruction_of_Cortical_Surfaces_From_3D_MRI_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bongratz_Vox2Cortex_Fast_Explicit_Reconstruction_of_Cortical_Surfaces_From_3D_MRI_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Safe Multi-View Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase | Huayi Tang, Yong Liu | Multi-view clustering has been shown to boost clustering performance by effectively mining the complementary information from multiple views. However, we observe that learning from data with more views is not guaranteed to achieve better clustering performance than from data with fewer views. To address this issue, we propose a general deep learning based framework that is guaranteed to reduce the risk of performance degradation caused by view increase. Concretely, the model is trained to simultaneously extract complementary information and discard the meaningless noise by automatically selecting features. These two learning procedures are incorporated into one unified framework by the proposed optimization objective. In theory, the empirical clustering risk of the model is no higher than learning from data before the view increase and data of the new increased single view. Also, the expected clustering risk of the model under divergence-based loss is no higher than that with high probability. Comprehensive experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed framework in achieving safe multi-view clustering. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Deep_Safe_Multi-View_Clustering_Reducing_the_Risk_of_Clustering_Performance_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Deep_Safe_Multi-View_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Deep_Safe_Multi-View_Clustering_Reducing_the_Risk_of_Clustering_Performance_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Deep_Safe_Multi-View_Clustering_Reducing_the_Risk_of_Clustering_Performance_CVPR_2022_paper.html | CVPR 2022 | null |
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information | Lingfeng Yang, Xiang Li, Renjie Song, Borui Zhao, Juntian Tao, Shihao Zhou, Jiajun Liang, Jian Yang | Fine-grained image classification is a challenging computer vision task where various species share similar visual appearances, resulting in misclassification if merely based on visual clues. Therefore, it is helpful to leverage additional information, e.g., the locations and dates for data shooting, which can be easily accessible but rarely exploited. In this paper, we first demonstrate that existing multimodal methods fuse multiple features only on a single dimension, which essentially has insufficient help in feature discrimination. To fully explore the potential of multimodal information, we propose a dynamic MLP on top of the image representation, which interacts with multimodal features at a higher and broader dimension. The dynamic MLP is an efficient structure parameterized by the learned embeddings of variable locations and dates. It can be regarded as an adaptive nonlinear projection for generating more discriminative image representations in visual tasks. To our best knowledge, it is the first attempt to explore the idea of dynamic networks to exploit multimodal information in fine-grained image classification tasks. Extensive experiments demonstrate the effectiveness of our method. The t-SNE algorithm visually indicates that our technique improves the recognizability of image representations that are visually similar but with different categories. Furthermore, among published works across multiple fine-grained datasets, dynamic MLP consistently achieves SOTA results and takes third place in the iNaturalist challenge at FGVC8. Code is available at https://github.com/megvii-research/DynamicMLPForFinegrained. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Dynamic_MLP_for_Fine-Grained_Image_Classification_by_Leveraging_Geographical_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Dynamic_MLP_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03253 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Dynamic_MLP_for_Fine-Grained_Image_Classification_by_Leveraging_Geographical_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Dynamic_MLP_for_Fine-Grained_Image_Classification_by_Leveraging_Geographical_and_CVPR_2022_paper.html | CVPR 2022 | null |
HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network | Chang Yu, Xiangyu Zhu, Xiaomei Zhang, Zidu Wang, Zhaoxiang Zhang, Zhen Lei | Capsule networks are designed to present the objects by a set of parts and their relationships, which provide an insight into the procedure of visual perception. Although recent works have shown the success of capsule networks on simple objects like digits, the human faces with homologous structures, which are suitable for capsules to describe, have not been explored. In this paper, we propose a Hierarchical Parsing Capsule Network (HP-Capsule) for unsupervised face subpart-part discovery. When browsing large-scale face images without labels, the network first encodes the frequently observed patterns with a set of explainable subpart capsules. Then, the subpart capsules are assembled into part-level capsules through a Transformer-based Parsing Module (TPM) to learn the compositional relations between them. During training, as the face hierarchy is progressively built and refined, the part capsules adaptively encode the face parts with semantic consistency. HP-Capsule extends the application of capsule networks from digits to human faces and takes a step forward to show how the neural networks understand homologous objects without human intervention. Besides, HP-Capsule gives unsupervised face segmentation results by the covered regions of part capsules, enabling qualitative and quantitative evaluation. Experiments on BP4D and Multi-PIE datasets show the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_HP-Capsule_Unsupervised_Face_Part_Discovery_by_Hierarchical_Parsing_Capsule_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_HP-Capsule_Unsupervised_Face_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_HP-Capsule_Unsupervised_Face_Part_Discovery_by_Hierarchical_Parsing_Capsule_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_HP-Capsule_Unsupervised_Face_Part_Discovery_by_Hierarchical_Parsing_Capsule_Network_CVPR_2022_paper.html | CVPR 2022 | null |
ScanQA: 3D Question Answering for Spatial Scene Understanding | Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, Motoaki Kawanabe | We propose a new 3D spatial understanding task of 3D Question Answering (3D-QA). In the 3D-QA task, models receive visual information from the entire 3D scene of the rich RGB-D indoor scan and answer the given textual questions about the 3D scene. Unlike the 2D-question answering of VQA, the conventional 2D-QA models suffer from problems with spatial understanding of object alignment and directions and fail the object identification from the textual questions in 3D-QA. We propose a baseline model for 3D-QA, named ScanQA model, where the model learns a fused descriptor from 3D object proposals and encoded sentence embeddings. This learned descriptor correlates the language expressions with the underlying geometric features of the 3D scan and facilitates the regression of 3D bounding boxes to determine described objects in textual questions. We collected human-edited question-answer pairs with free-form answers that are grounded to 3D objects in each 3D scene. Our new ScanQA dataset contains over 40K question-answer pairs from the 800 indoor scenes drawn from the ScanNet dataset. To the best of our knowledge, ScanQA is the first large-scale effort to perform object-grounded question-answering in 3D environments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Azuma_ScanQA_3D_Question_Answering_for_Spatial_Scene_Understanding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Azuma_ScanQA_3D_Question_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.10482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Azuma_ScanQA_3D_Question_Answering_for_Spatial_Scene_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Azuma_ScanQA_3D_Question_Answering_for_Spatial_Scene_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-Based Visual Question Answering | Yang Ding, Jing Yu, Bang Liu, Yue Hu, Mingxin Cui, Qi Wu | Knowledge-based visual question answering requires the ability of associating external knowledge for open-ended cross-modal scene understanding. One limitation of existing solutions is that they capture relevant knowledge from text-only knowledge bases, which merely contain facts expressed by first-order predicates or language descriptions while lacking complex but indispensable multimodal knowledge for visual understanding. How to construct vision-relevant and explainable multimodal knowledge for the VQA scenario has been less studied. In this paper, we propose MuKEA to represent multimodal knowledge by an explicit triplet to correlate visual objects and fact answers with implicit relations. To bridge the heterogeneous gap, we propose three objective losses to learn the triplet representations from complementary views: embedding structure, topological relation and semantic space. By adopting a pre-training and fine-tuning learning strategy, both basic and domain-specific multimodal knowledge are progressively accumulated for answer prediction. We outperform the state-of-the-art by 3.35% and 6.08% respectively on two challenging knowledge-required datasets: OK-VQA and KRVQA. Experimental results prove the complementary benefits of the multimodal knowledge with existing knowledge bases and the advantages of our end-to-end framework over the existing pipeline methods. The code is available at https://github.com/AndersonStra/MuKEA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_MuKEA_Multimodal_Knowledge_Extraction_and_Accumulation_for_Knowledge-Based_Visual_Question_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_MuKEA_Multimodal_Knowledge_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09138 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_MuKEA_Multimodal_Knowledge_Extraction_and_Accumulation_for_Knowledge-Based_Visual_Question_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_MuKEA_Multimodal_Knowledge_Extraction_and_Accumulation_for_Knowledge-Based_Visual_Question_CVPR_2022_paper.html | CVPR 2022 | null |
Class-Incremental Learning by Knowledge Distillation With Adaptive Feature Consolidation | Minsoo Kang, Jaeyoo Park, Bohyung Han | We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing examples in the previous tasks. Our algorithm is based on knowledge distillation and provides a principled way to maintain the representations of old models while adjusting to new tasks effectively. The proposed method estimates the relationship between the representation changes and the resulting loss increases incurred by model updates. It minimizes the upper bound of the loss increases using the representations, which exploits the estimated importance of each feature map within a backbone model. Based on the importance, the model restricts updates of important features for robustness while allowing changes in less critical features for flexibility. This optimization strategy effectively alleviates the notorious catastrophic forgetting problem despite the limited accessibility of data in the previous tasks. The experimental results show significant accuracy improvement of the proposed algorithm over the existing methods on the standard datasets. Code is available | https://openaccess.thecvf.com/content/CVPR2022/papers/Kang_Class-Incremental_Learning_by_Knowledge_Distillation_With_Adaptive_Feature_Consolidation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kang_Class-Incremental_Learning_by_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00895 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_Class-Incremental_Learning_by_Knowledge_Distillation_With_Adaptive_Feature_Consolidation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_Class-Incremental_Learning_by_Knowledge_Distillation_With_Adaptive_Feature_Consolidation_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Program Representations for Food Images and Cooking Recipes | Dim P. Papadopoulos, Enrique Mora, Nadiia Chepurko, Kuan Wei Huang, Ferda Ofli, Antonio Torralba | In this paper, we are interested in modeling a how-to instructional procedure, such as a cooking recipe, with a meaningful and rich high-level representation. Specifically, we propose to represent cooking recipes and food images as cooking programs. Programs provide a structured representation of the task, capturing cooking semantics and sequential relationships of actions in the form of a graph. This allows them to be easily manipulated by users and executed by agents. To this end, we build a model that is trained to learn a joint embedding between recipes and food images via self-supervision and jointly generate a program from this embedding as a sequence. To validate our idea, we crowdsource programs for cooking recipes and show that: (a) projecting the image-recipe embeddings into programs leads to better cross-modal retrieval results; (b) generating programs from images leads to better recognition results compared to predicting raw cooking instructions; and (c) we can generate food images by manipulating programs via optimizing the latent code of a GAN. Code, data, and models are available online. | https://openaccess.thecvf.com/content/CVPR2022/papers/Papadopoulos_Learning_Program_Representations_for_Food_Images_and_Cooking_Recipes_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Papadopoulos_Learning_Program_Representations_for_Food_Images_and_Cooking_Recipes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Papadopoulos_Learning_Program_Representations_for_Food_Images_and_Cooking_Recipes_CVPR_2022_paper.html | CVPR 2022 | null |
Bending Graphs: Hierarchical Shape Matching Using Gated Optimal Transport | Mahdi Saleh, Shun-Cheng Wu, Luca Cosmo, Nassir Navab, Benjamin Busam, Federico Tombari | Shape matching has been a long-studied problem for the computer graphics and vision community. The objective is to predict a dense correspondence between meshes that have a certain degree of deformation. Existing methods either consider the local description of sampled points or discover correspondences based on global shape information. In this work, we investigate a hierarchical learning design, to which we incorporate local patch-level information and global shape-level structures. This flexible representation enables correspondence prediction and provides rich features for the matching stage. Finally, we propose a novel optimal transport solver by recurrently updating features on non-confident nodes to learn globally consistent correspondences between the shapes. Our results on publicly available datasets suggest robust performance in presence of severe deformations without the need of extensive training or refinement. | https://openaccess.thecvf.com/content/CVPR2022/papers/Saleh_Bending_Graphs_Hierarchical_Shape_Matching_Using_Gated_Optimal_Transport_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Saleh_Bending_Graphs_Hierarchical_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.01537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Saleh_Bending_Graphs_Hierarchical_Shape_Matching_Using_Gated_Optimal_Transport_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Saleh_Bending_Graphs_Hierarchical_Shape_Matching_Using_Gated_Optimal_Transport_CVPR_2022_paper.html | CVPR 2022 | null |
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering | Feng Gao, Qing Ping, Govind Thattai, Aishwarya Reganti, Ying Nian Wu, Prem Natarajan | Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question. Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast amount of external knowledge. In this paper, we call for an alternative paradigm for the OK-VQA task, which transforms the image into plain text, so that we can enable knowledge passage retrieval, and generative question-answering in the natural language space. This paradigm takes advantage of the sheer volume of gigantic knowledge bases and the richness of pre-trained language models. A Transform-Retrieve-Generate framework (TRiG) framework is proposed, which can be plug-and-played with alternative image-to-text models and textual knowledge bases. Experimental results show that our TRiG framework outperforms all state-of-the-art supervised methods by at least 11.1% absolute margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_Transform-Retrieve-Generate_Natural_Language-Centric_Outside-Knowledge_Visual_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gao_Transform-Retrieve-Generate_Natural_Language-Centric_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Transform-Retrieve-Generate_Natural_Language-Centric_Outside-Knowledge_Visual_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Transform-Retrieve-Generate_Natural_Language-Centric_Outside-Knowledge_Visual_Question_Answering_CVPR_2022_paper.html | CVPR 2022 | null |
Federated Learning With Position-Aware Neurons | Xin-Chun Li, Yi-Chu Xu, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, De-Chuan Zhan | Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data across clients make the locally updated parameters imprecisely aligned, disabling the coordinate-based parameter averaging. Traditional neurons do not explicitly consider position information. Hence, we propose Position-Aware Neurons (PANs) as an alternative, fusing position-related values (i.e., position encodings) into neuron outputs. PANs couple themselves to their positions and minimize the possibility of dislocation, even updating on heterogeneous data. We turn on/off PANs to disable/enable the permutation invariance property of neural networks. PANs are tightly coupled with positions when applied to FL, making parameters across clients pre-aligned and facilitating coordinate-based parameter averaging. PANs are algorithm-agnostic and could universally improve existing FL algorithms. Furthermore, "FL with PANs" is simple to implement and computationally friendly. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Federated_Learning_With_Position-Aware_Neurons_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Federated_Learning_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Federated_Learning_With_Position-Aware_Neurons_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Federated_Learning_With_Position-Aware_Neurons_CVPR_2022_paper.html | CVPR 2022 | null |
Fair Contrastive Learning for Facial Attribute Classification | Sungho Park, Jewook Lee, Pilhyeon Lee, Sunhee Hwang, Dohyung Kim, Hyeran Byun | Learning visual representation of high quality is essential for image classification. Recently, a series of contrastive representation learning methods have achieved preeminent success. Particularly, SupCon outperformed the dominant methods based on cross-entropy loss in representation learning. However, we notice that there could be potential ethical risks in supervised contrastive learning. In this paper, we for the first time analyze unfairness caused by supervised contrastive learning and propose a new Fair Supervised Contrastive Loss (FSCL) for fair visual representation learning. Inheriting the philosophy of supervised contrastive learning, it encourages representation of the same class to be closer to each other than that of different classes, while ensuring fairness by penalizing the inclusion of sensitive attribute information in representation. In addition, we introduce a group-wise normalization to diminish the disparities of intra-group compactness and inter-class separability between demographic groups that arouse unfair classification. Through extensive experiments on CelebA and UTK Face, we validate that the proposed method significantly outperforms SupCon and existing state-of-the-art methods in terms of the trade-off between top-1 accuracy and fairness. Moreover, our method is robust to the intensity of data bias and effectively works in incomplete supervised settings. Our code is available at https://github.com/sungho-CoolG/FSCL | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Fair_Contrastive_Learning_for_Facial_Attribute_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Fair_Contrastive_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16209 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Fair_Contrastive_Learning_for_Facial_Attribute_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Fair_Contrastive_Learning_for_Facial_Attribute_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
MDAN: Multi-Level Dependent Attention Network for Visual Emotion Analysis | Liwen Xu, Zhengtao Wang, Bin Wu, Simon Lui | Visual Emotion Analysis (VEA) is attracting increasing attention. One of the biggest challenges of VEA is to bridge the affective gap between visual clues in a picture and the emotion expressed by the picture. As the granularity of emotions increases, the affective gap increases as well. Existing deep approaches try to bridge the gap by directly learning discrimination among emotions globally in one shot without considering the hierarchical relationship among emotions at different affective levels and the affective level of emotions to be classified. In this paper, we present the Multi-level Dependent Attention Network (MDAN) with two branches, to leverage the emotion hierarchy and the correlation between different affective levels and semantic levels. The bottom-up branch directly learns emotions at the highest affective level and strictly follows the emotion hierarchy while predicting emotions at lower affective levels. In contrast, the top-down branch attempt to disentangle the affective gap by one-to-one mapping between semantic levels and affective levels, namely, Affective Semantic Mapping. At each semantic level, a local classifier learns discrimination among emotions at the corresponding affective level. Finally, We integrate global learning and local learning into a unified deep framework and optimize the network simultaneously. Moreover, to properly extract and leverage channel dependencies and spatial attention while disentangling the affective gap, we carefully designed two attention modules: the Multi-head Cross Channel Attention module and the Level-dependent Class Activation Map module. Finally, the proposed deep framework obtains new state-of-the-art performance on six VEA benchmarks, where it outperforms existing state-of-the-art methods by a large margin, e.g., +3.85% on the WEBEmo dataset at 25 classes classification accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_MDAN_Multi-Level_Dependent_Attention_Network_for_Visual_Emotion_Analysis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13443 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_MDAN_Multi-Level_Dependent_Attention_Network_for_Visual_Emotion_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_MDAN_Multi-Level_Dependent_Attention_Network_for_Visual_Emotion_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design | Xiran Fan, Chun-Hao Yang, Baba C. Vemuri | Hyperbolic neural networks have been popular in the recent past due to their ability to represent hierarchical data sets effectively and efficiently. The challenge in developing these networks lies in the nonlinearity of the embedding space namely, the Hyperbolic space. Hyperbolic space is a homogeneous Riemannian manifold of the Lorentz group which is a semi-Riemannian manifold, i.e. a manifold equipped with an indefinite metric. Most existing methods (with some exceptions) use local linearization to define a variety of operations paralleling those used in traditional deep neural networks in Euclidean spaces. In this paper, we present a novel fully hyperbolic neural network which uses the concept of projections (embeddings) followed by an intrinsic aggregation and a nonlinearity all within the hyperbolic space. The novelty here lies in the projection which is designed to project data on to a lower-dimensional embedded hyperbolic space and hence leads to a nested hyperbolic space representation independently useful for dimensionality reduction. The main theoretical contribution is that the proposed embedding is proved to be isometric and equivariant under the Lorentz transformations, which are the natural isometric transformations in hyperbolic spaces. This projection is computationally efficient since it can be expressed by simple linear operations, and, due to the aforementioned equivariance property, it allows for weight sharing. The nested hyperbolic space representation is the core component of our network and therefore, we first compare this representation - independent of the network - with other dimensionality reduction methods such as tangent PCA, principal geodesic analysis (PGA) and HoroPCA. Based on this equivariant embedding, we develop a novel fully hyperbolic graph convolutional neural network architecture to learn the parameters of the projection. Finally, we present experiments demonstrating comparative performance of our network on several publicly available data sets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Nested_Hyperbolic_Spaces_for_Dimensionality_Reduction_and_Hyperbolic_NN_Design_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_Nested_Hyperbolic_Spaces_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03402 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Nested_Hyperbolic_Spaces_for_Dimensionality_Reduction_and_Hyperbolic_NN_Design_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Nested_Hyperbolic_Spaces_for_Dimensionality_Reduction_and_Hyperbolic_NN_Design_CVPR_2022_paper.html | CVPR 2022 | null |
BNUDC: A Two-Branched Deep Neural Network for Restoring Images From Under-Display Cameras | Jaihyun Koh, Jangho Lee, Sungroh Yoon | The images captured by under-display cameras (UDCs) are degraded by the screen in front of them. We model this degradation in terms of a) diffraction by the pixel grid, which attenuates high-spatial-frequency components of the image; and b) diffuse intensity and color changes caused by the multiple thin-film layers in an OLED, which modulate the low-spatial-frequency components of the image. We introduce a deep neural network with two branches to reverse each type of degradation, which is more effective than performing both restorations in a single forward network. We also propose an affine transform connection to replace the skip connection used in most existing DNNs for restoring UDC images. Confining the solution space to the linear transform domain reduces the blurring caused by convolution; and any gross color shift in the training images is eliminated by inverse color filtering. Trained on three datasets of UDC images, our network outperformed existing methods in terms of measures of distortion and of perceived image quality. | https://openaccess.thecvf.com/content/CVPR2022/papers/Koh_BNUDC_A_Two-Branched_Deep_Neural_Network_for_Restoring_Images_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Koh_BNUDC_A_Two-Branched_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Koh_BNUDC_A_Two-Branched_Deep_Neural_Network_for_Restoring_Images_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Koh_BNUDC_A_Two-Branched_Deep_Neural_Network_for_Restoring_Images_From_CVPR_2022_paper.html | CVPR 2022 | null |
RGB-Depth Fusion GAN for Indoor Depth Completion | Haowen Wang, Mingyuan Wang, Zhengping Che, Zhiyuan Xu, Xiuquan Qiao, Mengshi Qi, Feifei Feng, Jian Tang | The raw depth image captured by the indoor depth sensor usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and limited distance range. The incomplete depth map burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed to alleviate this issue. While most existing methods can generate accurate dense depth maps from sparse and uniformly sampled depth maps, they are not suitable for complementing the large contiguous regions of missing depth values, which is common and critical. In this paper, we design a novel two-branch end-to-end fusion network, which takes a pair of RGB and incompleted depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure to regress the local dense depth values from the raw depth map, with the help of local guidance information extracted from the RGB image. In the other branch, we propose an RGB-depth fusion GAN to transfer the RGB image to the fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate the features across the two branches, and we append a confidence fusion head to fuse the two outputs of the branches for the final depth map. Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method clearly improves the depth completion performance, especially in a more realistic setting of indoor environments with the help of the pseudo depth map. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RGB-Depth_Fusion_GAN_for_Indoor_Depth_Completion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_RGB-Depth_Fusion_GAN_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10856 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_RGB-Depth_Fusion_GAN_for_Indoor_Depth_Completion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_RGB-Depth_Fusion_GAN_for_Indoor_Depth_Completion_CVPR_2022_paper.html | CVPR 2022 | null |
Training Object Detectors From Scratch: An Empirical Study in the Era of Vision Transformer | Weixiang Hong, Jiangwei Lao, Wang Ren, Jian Wang, Jingdong Chen, Wei Chu | Modeling in computer vision has long been dominated by convolutional neural networks (CNNs). Recently, in light of the excellent performances of self-attention mechanism in the language field, transformers tailored for visual data have drawn numerous attention and triumphed CNNs in various vision tasks. These vision transformers heavily rely on large-scale pre-training to achieve competitive accuracy, which not only hinders the freedom of architectural design in downstream tasks like object detection, but also causes learning bias and domain mismatch in the fine-tuning stages. To this end, we aim to get rid of the "pre-train & fine-tune" paradigm of vision transformer and train transformer based object detector from scratch. Some earlier work in the CNNs era have successfully trained CNNs based detectors without pre-training, unfortunately, their findings do not generalize well when the backbone is switched from CNNs to vision transformer. Instead of proposing a specific vision transformer based detector, in this work, our goal is to reveal the insights of training vision transformer based detectors from scratch. In particular, we expect those insights can help other researchers and practitioners, and inspire more interesting research in other fields, such as semantic segmentation, visual-linguistic pre-training, etc. One of the key findings is that both architectural changes and more epochs play critical roles in training vision transformer based detectors from scratch. Experiments on MS COCO datasets demonstrate that vision transformer based detectors trained from scratch can also achieve similar performances to their counterparts with ImageNet pre-training. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Training_Object_Detectors_From_Scratch_An_Empirical_Study_in_the_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Training_Object_Detectors_From_Scratch_An_Empirical_Study_in_the_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Training_Object_Detectors_From_Scratch_An_Empirical_Study_in_the_CVPR_2022_paper.html | CVPR 2022 | null |
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