ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Conference Paper A simple baseline for domain generalization of action recognition and a realistic out-of-domain scenario
Cited 0 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Hyungmin Kim, Hobeum Jeon, Dohyung Kim, Jaehong Kim
Issue Date
2023-06
Citation
International Conference on Ubiquitous Robots (UR) 2023, pp.515-520
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/UR57808.2023.10202303
Abstract
In this study, we tackle the Domain Generalization (DG) challenge in the field of video action recognition. Previous works on DG for action recognition mostly focus on the Single Domain Generalization (SDG) problem, which is difficult to achieve due to domain biases present in datasets. Thus, we aim to address this challenge by re-defining DG for video action recognition. Another limitation of existing research is the lack of realistic out-of-domain scenarios. To overcome this, we introduce a new benchmark for DG in video action recognition named ENT dataset that contains realistic domain shifts. Additionally, we propose a straightforward baseline for DG in the video domain that helps improve the performance of action recognition models in unseen domains. Our proposed approach adapts the image-based methods to the video domain, leading to improved DG performance.
KSP Keywords
Recognition model, Video action recognition, image-based method, single domain