Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for objects in different scale granularity.In this paper, we propose adaptive linear span network (AdaLSN), driven by neural architecture search (NAS), to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN is formulated with the theory of linear span, which provides one of the earliest explanations for multi-scale deep feature fusion. AdaLSN is materialized by defining a mixed unit-pyramid search space, which goes beyond many existing search spaces using unit-level or pyramid-level features.Within the mixed space, we apply genetic architecture search to jointly optimize unit-level operations and pyramid-level connections for adaptive feature space expansion. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off compared with state-of-the-arts. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction. Code is available at \href{https://github.com/sunsmarterjie/SDL-Skeleton}{\color{magenta}github.com/sunsmarterjie/SDL-Skeleton}.
Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment
Tsinghua University（清华大学）；Beihang University（北京航空航天大学）；Children’s Hospital, Capital Institute of Pediatrics（首都儿科研究所附属儿童医院）；Zhongguancun Laboratory（中关村实验室）
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
Microsoft Research Asia（微软亚洲研究院）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Sun Yat-sen University（中山大学）；State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Peking University, Shenzhen Graduate School（北京大学深圳研究生院）；Sony R&D Center China（索尼中国研发中心）
Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition
Hefei University of Technology（合肥工业大学）；Jilin University（吉林大学）；Zhejiang Gongshang University（浙江工商大学）；University of Science and Technology of China（中国科学技术大学）
Relational Network for Skeleton-Based Action Recognition
Center for Research on Intelligent Perception and Computing, CASIA（中国科学院自动化研究所智能感知与计算研究中心）；National Laboratory of Pattern Recognition, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；University of Chinese Academy of Sciences（中国科学院大学）
Towards Efficient General Feature Prediction in Masked Skeleton Modeling
Hefei University of Technology（合肥工业大学）；Jilin University（吉林大学）；Zhejiang Gongshang University（浙江工商大学）；University of Science and Technology of China（中国科学技术大学）
Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information（视觉信息工程国家级工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；Robotics, Xi'an Jiaotong University（机器人学，西安交通大学）；School of Electronics and Control Engineering（电子控制工程学院）
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学协同媒体网络创新中心）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Shanghai Jiao Tong University（上海交通大学）
Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
State Key Laboratory of General Artificial Intelligence, Peking University（通用人工智能国家重点实验室，北京大学）；Center for Research in Computer Vision, University of Central Florida（中佛罗里达大学计算机视觉研究中心）
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition
University of California, Merced（加州大学默塞德分校）；Bishop’s University（毕索大学）；The Education University of Hong Kong（香港教育大学）；City University of Hong Kong（香港城市大学）；Hefei University of Technology（合肥工业大学）
Affinity Contrastive Learning for Skeleton-based Human Activity Understanding
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Electronic, Electrical and Communication Engineering, Univeristy of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Moonshot AI
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学协同媒体网络创新中心）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition
Central South University（中南大学）；Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous System, Southern University of Science and Technology（南方科技大学可信自主系统研究院）
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition
Beijing Engineering Research Center of Mixed Reality and Advanced Display（北京混合现实与先进显示工程研究中心）；School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院）
Conditional Directed Graph Convolution for 3D Human Pose Estimation
The Chinese University of Hong Kong（香港中文大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Nanyang Technological University（南洋理工大学）；The Hong Kong Polytechnic University（香港理工大学）
Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
University of South Carolina（南卡罗来纳大学）；South Carolina Institute of Archaeology and Anthropology, University of South Carolina（南卡罗来纳大学南卡罗来纳考古与人类学研究所）；Center for Digital Humanities, University of South Carolina（南卡罗来纳大学数字人文中心）；Tianjin University（天津大学）
