Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge
Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Wang, Jiahe, Huang, Jiale, Cai, Bingzhao, Cao, Yifan, Yun, Xin, Wang, Shangfei
Institution: University of Science and Technology of China
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4392970093