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...

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Vydané v:	IEEE transactions on circuits and systems for video technology Ročník 33; číslo 12; s. 1
Hlavní autori:	Liu, Xianyuan, Zhou, Shuo, Lei, Tao, Jiang, Ping, Chen, Zhixiang, Lu, Haiping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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