Modeling Action Spatiotemporal Relationships Using Graph-Based Class-Level Attention Network for Long-Term Action Detection-Reference-Cited by-同舟云学术

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Modeling Action Spatiotemporal Relationships Using Graph-Based Class-Level Attention Network for Long-Term Action Detection

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Wu Yuankai¹,Su Xin¹,Salihu Driton¹,Xing Hao²,Zakour Marsil¹,Patsch Constantin¹

Affiliation:

1. Institute of Robotics and Machine Intelligence, School of Computation, Information and Technology, Technical University of Munich,Chair of Media Technology and Munich,Germany

2. School of Computation, Information and Technology, Technical University of Munich,Maschine Vision and Perception group,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10341341/10341342/10341409.pdf?arnumber=10341409

Reference37 articles.

1. Introducing GARMI - A Service Robotics Platform to Support the Elderly at Home: Design Philosophy, System Overview and First Results

2. ARMAR-6: A Collaborative Humanoid Robot for Industrial Environments

3. PKU-MMD

4. The KIT Bimanual Manipulation Dataset

5. NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. TSCL: Timestamp Supervised Contrastive Learning for Action Segmentation;IEEE Robotics and Automation Letters;2024-09

2. Long-Term Action Anticipation Based on Contextual Alignment;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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