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arXiv 2024年

作者： Zhou, Kanglei Cai, Ruizhi Wang, Liyuan Shum, Hubert P.H. Liang, Xiaohui State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China The Department of Computer Science and Technology Institute for AI BNRist Center THBI Lab Tsinghua-Bosch Joint Center for ML Tsinghua University Beijing100190 China The Department of Computer Science Durham University DurhamDH1 3LE United Kingdom The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Zhongguancun Laboratory Beijing China

Action Quality Assessment (AQA) quantitatively evaluates the quality of human actions, providing automated assessments that reduce biases in human judgment. Its applications span domains such as sports analysis, skill assessment, and medical care. Recent advances in AQA have introduced innovative methodologies, but similar methods often intertwine across different domains, highlighting the fragmented nature that hinders systematic reviews. In addition, the lack of a unified benchmark and limited computational comparisons hinder consistent evaluation and fair assessment of AQA approaches. In this work, we address these gaps by systematically analyzing over 150 AQA-related papers to develop a hierarchical taxonomy, construct a unified benchmark, and provide an in-depth analysis of current trends, challenges, and future directions. Our hierarchical taxonomy categorizes AQA methods based on input modalities (video, skeleton, multi-modal) and their specific characteristics, highlighting the evolution and interrelations across various approaches. To promote standardization, we present a unified benchmark, integrating diverse datasets to evaluate the assessment precision and computational efficiency. Finally, we review emerging task-specific applications and identify under-explored challenges in AQA, providing actionable insights into future research directions. This survey aims to deepen understanding of AQA progress, facilitate method comparison, and guide future innovations. The project web page can be found at https://***/AQA-Survey. Copyright © 2024, The Authors. All rights reserved.

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PointHGN: Point Heterogeneous Graph Neural Network for Point Cloud Learning

PointHGN: Point Heterogeneous Graph Neural Network for Point...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Zhiying Leng Shun-cheng Wu Frederick W. B. Li Federico Tombari Xiaohui Liang State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Chair for Computer Aided Medical Procedures & Augmented Reality Technical University of Munich Germany Department of Computer Science Durham University U.K.

Point cloud learning has gained significant attention in 3D multimedia. However, existing methods learn point-wise features in a homogeneous manner, disregarding the heterogeneity that inherently exists in point clouds. Embedding the heterogeneity into feature learning is favorable to learning more discriminative point-wise features with rich geometric structures. In this work, we are the first to analyze and utilize the heterogeneity of point clouds, namely geometric heterogeneity and category heterogeneity. We hence propose two general heterogeneous graph convolutions to exploit such heterogeneity, including Geometric Heterogeneous Graph Convolution (GHGC) and Category Heterogeneous Graph Convolution (CHGC). We implement a point heterogeneous graph neural network (PointHGN) based on classical MLP-based point cloud pipelines, without altering network configurations. Experimental results on point cloud semantic segmentation tasks manifest that our PointHGN achieves commendable performance on public datasets. Our proposed method provides an effective and alternative approach for point cloud learning that researchers can explore further.

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3D Face Reconstruction With Geometry Details From a Single Color Image Under Occluded Scenes

arXiv

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arXiv 2024年

作者： Zhao, Dapeng Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Qingdao Research Institute of Beihang University Qingdao China

3D face reconstruction technology aims to generate a face stereo model naturally and realistically. Previous deep face reconstruction approaches are typically designed to generate convincing textures and cannot generalize well to multiple occluded scenarios simultaneously. By introducing bump mapping, we successfully added mid-level details to coarse 3D faces. More innovatively, our method takes into account occlusion scenarios. Thus on top of common 3D face reconstruction approaches, we in this paper propose a unified framework to handle multiple types of obstruction simultaneously (e.g., hair, palms and glasses et al.).Extensive experiments and comparisons demonstrate that our method can generate high-quality reconstruction results with geometry details from captured facial images under occluded scenes. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D reconstruction

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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WildPose: HRNet-based Lightweight and Efficient Wildlife Pose Estimation

WildPose: HRNet-based Lightweight and Efficient Wildlife Pos...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Sibusiso Reuben Bakana Yongfei Zhang Bhekisipho Twala School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Faculty of Information and Communication Technology Tshwane University of Technology Tshwane South Africa

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Lightweight and efficient wildlife monitoring algorithms, including pose estimation, that can operate on edge devices with limited computational resources are urgently needed for wildlife studies and protections. To reduce computational costs while maintaining accuracy in animal pose estimation, this paper introduces WildPose, an HRNet-w32 based model, designed, which integrates Efficient Channel Attention (ECA) to enhance important feature representations without complex operations, Non-Local Blocks (NLB) to capture long-range contextual information and handle occlusions, and Depthwise Convolutions (DWConv) to reduce computational complexity and parameters. Additionally, the Online Hard keypoint Mining with Mean Squared Error (OHKMSE) loss function is employed to improve accuracy for occluded keypoints detection by focusing on harder keypoints. When evaluated on the largest wildlife dataset of Animal Kingdom, WildPose demonstrated a nice trade-off between accuracy and efficiency, 75% reduction in parameters and 65% reduction in GFLOPs, while achieving an increased Percentage of Correct keypoints (PCK) for occluded keypoints such as the hip, ankle, and tail. Therefore, WildPose can serve as a practical solution for real-time wildlife monitoring and artificial intelligence-based ecological studies, especially in resource-constrained environments.

关键词： Accuracy Biological system modeling Computational modeling Wildlife Pose estimation Tail Real-time systems Computational efficiency Monitoring Hip

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PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction

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IEEE Transactions on Image Processing 2025年 34卷 3718-3732页

作者： Zhou, Kanglei Shum, Hubert P. H. Li, Frederick W. B. Zhang, Xinxing Liang, Xiaohui Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing 100191 China Durham University Department of Computer Science Durham DH1 3LE United Kingdom BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University Department of Computer Science and Technology China Zhongguancun Laboratory Beijing 100190 China

Long-term Action Quality Assessment (AQA) aims to evaluate the quantitative performance of actions in long videos. However, existing methods face challenges due to domain shifts between the pre-trained large-scale action recognition backbones and the specific AQA task, thereby hindering their performance. This arises since fine-tuning resource-intensive backbones on small AQA datasets is impractical. We address this by identifying two levels of domain shift: task-level, regarding differences in task objectives, and feature-level, regarding differences in important features. For feature-level shifts, which are more detrimental, we propose Progressive Hierarchical Instruction (PHI) with two strategies. First, Gap Minimization Flow (GMF) leverages flow matching to progressively learn a fast flow path that reduces the domain gap between initial and desired features across shallow to deep layers. Additionally, a temporally-enhanced attention module captures long-range dependencies essential for AQA. Second, List-wise Contrastive Regularization (LCR) facilitates coarse-to-fine alignment by comprehensively comparing batch pairs to learn fine-grained cues while mitigating domain shift. Integrating these modules, PHI offers an effective solution. Experiments demonstrate that PHI achieves state-of-the-art performance on three representative long-term AQA datasets, proving its superiority in addressing the domain shift for long-term AQA. © 1992-2012 IEEE.

关键词： Action Quality Assessment Domain Shift Flow Matching Long-Term Action Quality Assessment

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Hao Sheng Yebin Liu Jingyi Yu Gaochang Wu Wei Xiong Ruixuan Cong Rongshan Chen Longzhao Guo Yanlin Xie Shuo Zhang Song Chang Youfang Lin Wentao Chao Xuechun Wang Guanghui Wang Fuqing Duan Tun Wang Da Yang Zhenglong Cui Sizhe Wang Mingyuan Zhao Qiong Wang Qianyu Chen Zhengyu Liang Yingqian Wang Jungang Yang Xueting Yang Junli Deng LFNAT 2023 Challenge State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University and Beihang Hangzhou Innovation Institute Yuhang

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research.

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Parametric Primitive Analysis of CAD Sketches with vision Transformer

arXiv

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arXiv 2024年

作者： Wang, Xiaogang Wang, Liang Wu, Hongyu Xiao, Guoqiang Xu, Kai The College of Computer and Information Science Southwest University Chongqing400715 China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The National University of Defense Technology Changsha410073 China

The design and analysis of computer-Aided Design (CAD) sketches play a crucial role in industrial product design, primarily involving CAD primitives and their inter-primitive constraints. To address challenges related to error accumulation in autoregressive models and the complexities associated with self-supervised model design for this task, we propose a two-stage network framework. This framework consists of a primitive network and a constraint network, transforming the sketch analysis task into a set prediction problem to enhance the effective handling of primitives and constraints. By decoupling target types from parameters, the model gains increased flexibility and optimization while reducing complexity. Additionally, the constraint network incorporates a pointer module to explicitly indicate the relationship between constraint parameters and primitive indices, enhancing interpretability and performance. Qualitative and quantitative analyses on two publicly available datasets demonstrate the superiority of this method. © 2024, CC BY.

关键词： computer aided design

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Learning Semantic Abstraction of Shape via 3D Region of Interest

arXiv

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arXiv 2022年

作者： Fang, Haiyue Wang, Xiaogang Cai, Zheyuan Shi, Yahao Sun, Xun Wu, Shilin Zhou, Bin State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China

In this paper, we focus on the two tasks of 3D shape abstraction and semantic analysis. This is in contrast to current methods, which focus solely on either 3D shape abstraction or semantic analysis. In addition, previous methods have had difficulty producing instance-level semantic results, which has limited their application. We present a novel method for the joint estimation of a 3D shape abstraction and semantic analysis. Our approach first generates a number of 3D semantic candidate regions for a 3D shape;we then employ these candidates to directly predict the semantic categories and refine the parameters of the candidate regions simultaneously using a deep convolutional neural network. Finally, we design an algorithm to fuse the predicted results and obtain the final semantic abstraction, which is shown to be an improvement over a standard non maximum suppression. Experimental results demonstrate that our approach can produce state-of-the-art results. Moreover, we also find that our results can be easily applied to instance-level semantic part segmentation and shape matching. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning

arXiv

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arXiv 2024年

作者： Zhu, Guogang Liu, Xuefeng Wu, Xinghao Tang, Shaojie Tang, Chao Niu, Jianwei Su, Hao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Jindal School of Management The University of Texas at Dallas RichardsonTX United States

Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model. In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model’s prediction to skew towards some certain classes. Existing FSSL methods primarily tackle this issue by enhancing consistency in model parameters or outputs. However, as the models themselves are biased, merely constraining their consistency is not sufficient to alleviate prediction bias. In this paper, we explore this bias from a Bayesian perspective and demonstrate that it principally originates from label prior bias within the training data. Building upon this insight, we propose a debiasing method for FSSL named FedDB. FedDB utilizes the Average Prediction Probability of Unlabeled Data (APP-U) to approximate the biased prior. During local training, FedDB employs APP-U to refine pseudo-labeling through Bayes’ theorem, thereby significantly reducing the label prior bias. Concurrently, during the model aggregation, FedDB uses APP-U from participating clients to formulate unbiased aggregate weights, thereby effectively diminishing bias in the global model. Experimental results show that FedDB can surpass existing FSSL methods. The code is available at https://***/GuogangZhu/FedDB. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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