First-Person Video Domain Adaptation with Multi-Scene Cross-Site Datasets and Attention-Based Methods

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person video action recognition is an under-explored problem, with a lack of benchmark datasets and limited consideration of first-person video...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on circuits and systems for video technology Jg. 33; H. 12; S. 1
Hauptverfasser:	Liu, Xianyuan, Zhou, Shuo, Lei, Tao, Jiang, Ping, Chen, Zhixiang, Lu, Haiping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Action recognition Activity recognition Adaptation Benchmark testing Benchmarks channel-temporal attention Data mining Datasets first-person vision Image reconstruction Kitchens Knowledge management Modules Networks Representation learning Scalability Target recognition Training unsupervised domain adaptation Video
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!