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Disentangling Label Distribution for Long-tailed Visual recognition

Disentangling Label Distribution for Long-tailed Visual Reco...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hong, Youngkyu Han, Seungju Choi, Kwanghee Seo, Seokjun Kim, Beomsu Chang, Buru Hyperconnect Seoul South Korea

ISBN: (纸本)9781665445092

The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution. Such protocol has questionable practicality since the target may also be long-tailed. Therefore, we formulate long-tailed visual recognition as a label shift problem where the target and source label distributions are different. One of the significant hurdles in dealing with the label shift problem is the entanglement between the source label distribution and the model prediction. In this paper, we focus on disentangling the source label distribution from the model prediction. We first introduce a simple but overlooked baseline method that matches the target label distribution by post-processing the model prediction trained by the cross-entropy loss and the Softmax function. Although this method surpasses state-of-the-art methods on benchmark datasets, it can be further improved by directly disentangling the source label distribution from the model prediction in the training phase. Thus, we propose a novel method, LAbel distribution DisEntangling (LADE) loss based on the optimal bound of Donsker-Varadhan representation. LADE achieves state-of-the-art performance on benchmark datasets such as CIFAR-100-LL Places-LT ImageNet-LL and iNaturalist 2018. Moreover LADE outperforms existing methods on various shifted target label distributions, showing the general adaptability of our proposed method.

关键词： Training Visualization computer vision Protocols Target recognition Object detection Predictive models

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Self-supervised Motion Learning from Static Images

Self-supervised Motion Learning from Static Images

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Huang, Ziyuan Zhang, Shiwei Jiang, Jianwen Tang, Mingqian Jin, Rong Ang, Marcelo H., Jr. Natl Univ Singapore Singapore Singapore Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Motions are reflected in videos as the movement of pixels, and actions are essentially patterns of inconsistent motions between the foreground and the background. To well distinguish the actions, especially those with complicated spatio-temporal interactions, correctly locating the prominent motion areas is of crucial importance. However, most motion information in existing videos are difficult to label and training a model with good motion representations with supervision will thus require a large amount of human labour for annotation. In this paper, we address this problem by self-supervised learning. Specifically, we propose to learn Motion from Static Images (MoSI). The model learns to encode motion information by classifying pseudo motions generated by MoSI. We furthermore introduce a static mask in pseudo motions to create local motion patterns, which forces the model to additionally locate notable motion areas for the correct classification. We demonstrate that MoSI can discover regions with large motion even without fine-tuning on the downstream datasets. As a result, the learned motion representations boost the performance of tasks requiring understanding of complex scenes and motions, i.e., action recognition. Extensive experiments show the consistent and transferable improvements achieved by MoSI. Codes will be soon released.

关键词： Training computer vision Codes Annotations pattern recognition Task analysis Videos

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Weakly-Supervised Physically Unconstrained Gaze Estimation

Weakly-Supervised Physically Unconstrained Gaze Estimation

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kothari, Rakshit De Mello, Shalini Iqbal, Umar Byeon, Wonmin Park, Seonwook Kautz, Jan NVIDIA Santa Clara CA 95051 USA Rochester Inst Technol Rochester NY 14623 USA Lunit Inc Seoul South Korea

ISBN: (纸本)9781665445092

A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios. In contrast, videos of human interactions in unconstrained environments are abundantly available and can be much more easily annotated with frame-level activity labels. In this work, we tackle the previously unexplored problem of weakly-supervised gaze estimation from videos of human interactions. We leverage the insight that strong gaze-related geometric constraints exist when people perform the activity of "looking at each other" (LAEO). To acquire viable 3D gaze supervision from LAEO labels, we propose a training algorithm along with several novel loss functions especially designed for the task. With weak supervision from two large scale CMU-Panoptic and AVA-LAEO activity datasets, we show significant improvements in (a) the accuracy of semisupervised gaze estimation and (b) cross-domain generalization on the state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation benchmark.

关键词： Training Geometry Three-dimensional displays Annotations Estimation Training data pattern recognition

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Representing Videos as Discriminative Sub-graphs for Action recognition

Representing Videos as Discriminative Sub-graphs for Action ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Dong Qiu, Zhaofan Pan, Yingwei Yao, Ting Li, Houqiang Mei, Tao Univ Sci & Technol China Hefei Peoples R China JD AI Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.

关键词： computer vision Three-dimensional displays Prototypes pattern recognition Complexity theory Proposals Kernel

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Self-supervised Video Representation Learning by Context and Motion Decoupling

Self-supervised Video Representation Learning by Context and...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Huang, Lianghua Liu, Yu Wang, Bin Pan, Pan Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665445092

A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias. While most existing works implicitly achieve this with video-specific pretext tasks (e.g., predicting clip orders, time arrows, and paces), we develop a method that explicitly decouples motion supervision from context bias through a carefully designed pretext task. Specifically, we take the key frames and motion vectors in compressed videos (e.g., in H.264 format) as the supervision sources for context and motion, respectively, which can be efficiently extracted at over 500 fps on CPU. Then we design two pretext tasks that are jointly optimized: a context matching task where a pairwise contrastive loss is cast between video clip and key frame features;and a motion prediction task where clip features, passed through an encoderdecoder network, are used to estimate motion features in a near future. These two tasks use a shared video backbone and separate MLP heads. Experiments show that our approach improves the quality of the learned video representation over previous works, where we obtain absolute gains of 16.0% and 11.1% in video retrieval recall on UCF101 and HMDB51, respectively. Moreover, we find the motion prediction to be a strong regularization for video networks, where using it as an auxiliary task improves the accuracy of action recognition with a margin of 7.4% similar to 13.8%.

关键词： computer vision Feature extraction pattern recognition Task analysis

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Spherical Confidence Learning for Face recognition

Spherical Confidence Learning for Face Recognition

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Shen Xu, Jianqing Xu, Xiaqing Shen, Pengcheng Li, Shaoxin Hooi, Bryan Natl Univ Singapore Inst Data Sci Singapore Singapore Tencent Youtu Lab Shenzhen Peoples R China Aibee Beijing Peoples R China

ISBN: (纸本)9781665445092

An emerging line of research has found that spherical spaces better match the underlying geometry of facial images, as evidenced by the state-of-the-art facial recognition methods which benefit empirically from spherical representations. Yet, these approaches rely on deterministic embeddings and hence suffer from the feature ambiguity dilemma, whereby ambiguous or noisy images are mapped into poorly learned regions of representation space, leading to inaccuracies. Probabilistic Face Embeddings (PFE) [17] is the first attempt to address this dilemma. However, we theoretically and empirically identify two main failures of PFE when it is applied to spherical deterministic embeddings aforementioned. To address these issues, in this paper, we propose a novel framework for face confidence learning in spherical space. Mathematically, we extend the von Mises Fisher density to its r-radius counterpart and derive a new optimization objective in closed form. Theoretically, the proposed probabilistic framework provably allows for better interpretability, leading to principled feature comparison and pooling. Extensive experimental results on multiple challenging benchmarks confirm our hypothesis and theory, and showcase the advantages of our framework over prior probabilistic methods and spherical deterministic embeddings in various face recognition tasks.

关键词： Geometry computer vision Face recognition Benchmark testing Probabilistic logic Noise measurement Task analysis

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WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos

WOAD: Weakly Supervised Online Action Detection in Untrimmed...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gao, Mingfei Zhou, Yingbo Xu, Ran Socher, Richard Xiong, Caiming Salesforce Res Palo Alto CA 94301 USA

ISBN: (纸本)9781665445092

Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious annotations of temporal action boundaries for training, which hinders the scalability of online action detection systems. We propose WOAD, a weakly supervised framework that can be trained using only video-class labels. WOAD contains two jointly-trained modules, i.e., temporal proposal generator (TPG) and online action recognizer (OAR). Supervised by video-class labels, TPG works offline and targets at accurately mining pseudo frame-level labels for OAR. With the supervisory signals from TPG, OAR learns to conduct action detection in an online fashion. Experimental results on THUMOS'14, ActivityNet1.2 and ActivityNet1.3 show that our weakly-supervised method largely outperforms weakly-supervised baselines and achieves comparable performance to the previous strongly-supervised methods. Beyond that, WOAD is flexible to leverage strong supervision when it is available. When strongly supervised, our method obtains the state-of-the-art results in the tasks of both online per-frame action recognition and online detection of action start.

关键词： Training computer vision Annotations Scalability Real-time systems Generators pattern recognition

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Reconstructing 3D Human Pose by Watching Humans in the Mirror

Reconstructing 3D Human Pose by Watching Humans in the Mirro...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fang, Qi Shuai, Qing Dong, Junting Bao, Hujun Zhou, Xiaowei Zhejiang Univ State Key Lab CAD&CG Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror. Compared to general scenarios of 3D pose estimation from a single view, the mirror reflection provides an additional view for resolving the depth ambiguity. We develop an optimization-based approach that exploits mirror symmetry constraints for accurate 3D pose reconstruction. We also provide a method to estimate the surface normal of the mirror from vanishing points in the single image. To validate the proposed approach, we collect a large-scale dataset named Mirrored-Human, which covers a large variety of human subjects, poses and backgrounds. The experiments demonstrate that, when trained on Mirrored-Human with our reconstructed 3D poses as pseudo ground-truth, the accuracy and generalizability of existing single-view 3D pose estimators can be largely improved. The code and dataset are available at https://***/Mirrored-Human/.

关键词： Training Surface reconstruction Three-dimensional displays Shape Pose estimation Reflection pattern recognition

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NPBG++: Accelerating Neural Point-Based Graphics

NPBG++: Accelerating Neural Point-Based Graphics

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Rakhimov, Ruslan Ardelean, Andrei-Timotei Lempitsky, Victor Burnaev, Evgeny Skolkovo Institute of Science and Technology Russia Yandex Russia Artificial Intelligence Research Institute Russia

ISBN: (纸本)9781665469463

We present a new system $(NPBG++)$ for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently lever-ages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graph-ics [1] in several important ways. By predicting the descrip-tors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suit-able for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality. Project page: https://***/npbgpp/. © 2022 ieee.

关键词： Rendering (computer graphics)

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NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

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ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Florin-Alexandru Vasluianu Tim Seizinger Zhuyun Zhou Zongwei Wu Cailian Chen Radu Timofte Wei Dong Han Zhou Yuqiong Tian Jun Chen Xueyang Fu Xin Lu Yurui Zhu Xi Wang Dong Li Jie Xiao Yunpeng Zhang Zheng-Jun Zha Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Yanyan Wei Zhihao Zhao Long Sun Tingting Yang Jinshan Pan Jiangxin Dong Jinhui Tang Bilel Benjdira Mohammed Nassif Anis Koubaa Ahmed Elhayek Anas M. Ali Kyotaro Tokoro Kento Kawai Kaname Yokoyama Takuya Seno Yuki Kondo Norimichi Ukita Chenghua Li Bo Yang Zhiqi Wu Gao Chen Yihan Yu Sixiang Chen Kai Zhang Tian Ye Wenbin Zou Yunlong Lin Zhaohu Xing Jinbin Bai Wenhao Chai Lei Zhu Ritik Maheshwari Rakshank Verma Rahul Tekchandani Praful Hambarde Satya Narayan Tazi Santosh Kumar Vipparthi Subrahmanyam Murala Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Fan Gao Weidan Yan Wenze Shao Dengyin Zhang Bin Chen Siqi Zhang Yanxin Qian Yuanbin Chen Yuanbo Zhou Tong Tong Rongfeng Wei Ruiqi Sun Yue Liu Nikhil Akalwadi Amogh Joshi Sampada Malagi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Adinath Dukre Vivek Deshmukh Shruti S. Phutke Ashutosh Kulkarni Anil Gonde Arun karthik K Manasa N Shri Hari Priya Wei Hao Xingzhuo Yan Minghan Fu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： computer vision Reviews Image synthesis conferences Buildings Image restoration pattern recognition

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