Human Pose and Action Recognition -- 专题文献

专题：Human Pose and Action Recognition

This cluster of papers focuses on the development and application of deep learning techniques for human action recognition and pose estimation. It covers topics such as spatiotemporal feature learning, convolutional networks, 3D human pose estimation, skeleton-based recognition, and video classification. The research aims to advance the understanding and accurate detection of human actions in various environments.

专题：Human Pose and Action Recognition

最新文献

A Structure-Aware and Motion-Adaptive Framework for 3D Human Pose Estimation with Mamba

Dual-stream spatio-temporal GCN-transformer network for 3D human pose estimation

VQualA 2025 Challenge on GenAI-Bench AIGC Video Quality Assessment: Methods and Results

Exploring multi-transformer with fine-grained prompt-driven coupled with diffusion model for 3D human pose estimation

Information-theoretic graph fusion with vision-language-action model for policy reasoning and dual robotic control

VR-EmotionNet: Real-Time Emotion Recognition in Virtual Reality and Temporal-Difference Minimizing Neural Network

DVLTA-VQA: Decoupled Vision-Language Modeling With Text-Guided Adaptation for Blind Video Quality Assessment

Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation

REAL-SORT: RElation-aware for real-time multiple object tracking

CoachXNet: An Artificial Intelligence and Internet of Things Integrated Platform for Personalized Training and Feedback in Digital Sports

近5年高被引文献