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.
Cite
@article{arxiv.2403.11450,
title = {Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge},
author = {Jiahe Wang and Jiale Huang and Bingzhao Cai and Yifan Cao and Xin Yun and Shangfei Wang},
journal= {arXiv preprint arXiv:2403.11450},
year = {2024}
}
Comments
USTC-AC's paper for Compound Expression (CE) Recognition Challenge in 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW)