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Hauptverfasser: Wang, Jiahe, Huang, Jiale, Cai, Bingzhao, Cao, Yifan, Yun, Xin, Wang, Shangfei
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2403.11450
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author Wang, Jiahe
Huang, Jiale
Cai, Bingzhao
Cao, Yifan
Yun, Xin
Wang, Shangfei
author_facet Wang, Jiahe
Huang, Jiale
Cai, Bingzhao
Cao, Yifan
Yun, Xin
Wang, Shangfei
contents Conventional approaches to facial expression recognition primarily focus on the classification of six basic facial expressions. Nevertheless, real-world situations present a wider range of complex compound expressions that consist of combinations of these basics ones due to limited availability of comprehensive training datasets. The 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) offered unlabeled datasets containing compound expressions. In this study, we propose a zero-shot approach for recognizing compound expressions by leveraging a pretrained visual language model integrated with some traditional CNN networks.
format Preprint
id arxiv_https___arxiv_org_abs_2403_11450
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge
Wang, Jiahe
Huang, Jiale
Cai, Bingzhao
Cao, Yifan
Yun, Xin
Wang, Shangfei
Computer Vision and Pattern Recognition
Conventional approaches to facial expression recognition primarily focus on the classification of six basic facial expressions. Nevertheless, real-world situations present a wider range of complex compound expressions that consist of combinations of these basics ones due to limited availability of comprehensive training datasets. The 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) offered unlabeled datasets containing compound expressions. In this study, we propose a zero-shot approach for recognizing compound expressions by leveraging a pretrained visual language model integrated with some traditional CNN networks.
title Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2403.11450