In this paper, we propose a novel framework for recognizing both discrete and dimensional emotions. In our framework, deep features extracted from foundation models are used as robust acoustic and visual representations of raw video. Three different structures based on attention-guided feature gathering (AFG) are designed for deep feature fusion. Then, we introduce a joint decoding structure for emotion classification and valence regression in the decoding stage. A multi-task loss based on uncertainty is also designed to optimize the whole process. Finally, by combining three different structures on the posterior probability level, we obtain the final predictions of discrete and dimensional emotions. When tested on the dataset of multimodal emotion recognition challenge (MER 2023), the proposed framework yields consistent improvements in both emotion classification and valence regression. Our final system achieves state-of-the-art performance and ranks third on the leaderboard on MER-MULTI sub-challenge.
@article{arxiv.2309.07925,
title = {Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023},
author = {Haotian Wang and Yuxuan Xi and Hang Chen and Jun Du and Yan Song and Qing Wang and Hengshun Zhou and Chenxi Wang and Jiefeng Ma and Pengfei Hu and Ya Jiang and Shi Cheng and Jie Zhang and Yuzhe Weng},
journal= {arXiv preprint arXiv:2309.07925},
year = {2023}
}