DEDGraph: Delay Embedding of Dynamic Graph for Temporal Action Segmentation

Junbin Zhang*, Pei Hsuan Tsai, Meng Hsun Tsai

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

In real-world interactive applications, where videos are generated in real-time and require immediate feedback, online segmentation has practical advantages over offline inference. Many excellent previous models have been developed for offline scenarios, while real-time prediction for temporal action segmentation (TAS) is a difficult task. Some interactive applications can tolerate a certain amount of delay. In this paper, we propose a node delay embedding of a dynamic graph for real-time TAS. We transform the video stream into a dynamic graph stream that evolves over time. We define past, current, and future nodes to construct sub-graphs at each step. Specifically, future nodes are sampled using our proposed node delay method. A graph model is utilized to aggregate past, current, and future node information to update the representation of current nodes and predict their labels. To the best of our knowledge, it is the first real-time TAS graph model with delay embedding. Experiments show that delay embedding enhances node representation and improves performance. Overall, our proposed approach provides a promising solution for real-time TAS.

原文English
主出版物標題Proceedings - 2023 6th International Symposium on Computer, Consumer and Control, IS3C 2023
發行者Institute of Electrical and Electronics Engineers Inc.
頁面155-158
頁數4
ISBN(電子)9798350301953
DOIs
出版狀態Published - 2023
事件6th International Symposium on Computer, Consumer and Control, IS3C 2023 - Taichung City, Taiwan
持續時間: 30 6月 20233 7月 2023

出版系列

名字Proceedings - 2023 6th International Symposium on Computer, Consumer and Control, IS3C 2023

Conference

Conference6th International Symposium on Computer, Consumer and Control, IS3C 2023
國家/地區Taiwan
城市Taichung City
期間30/06/233/07/23

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