Hierarchical Context Interaction and Reasoning with Transformer for Emotion Recognition
Wenxuan Wang, Chenglei Wang, Xiaomei Wang, Xuli Shen, Qing Xu, Xuelin Qian
Abstract
Emotion recognition is an important task in computer vision. However, current approaches using hard associations (e.g., element-wise addition or concatenation) suffer from information pollution. To overcome these challenges and utilize information at different scales, we present a novel Transformer-based emotion recognition (TransEmo) framework utilizing only the RGB modality as input. TransEmo is a hierarchical feature fusion framework with soft-association to effectively realize contextual interaction and emotion reasoning. It comprises a top-down path and a bottom-up path with cross-attention layers. The top-down path enriches the features of the current hierarchy gradually by using information from a larger receptive field and interacts effectively with contexts to suppress redundant information and noise. The bottom-up path introduces a set of learnable prototypes to adaptively capture clues for various emotions by reasoning with hierarchical features. Our proposed approach achieves state-of-the-art performance on two benchmarks and provides an analysis to discuss the advantages of our modules.
BibTeX
@inproceedings{icassp2025_hierarchicalcont,
title = {Hierarchical Context Interaction and Reasoning with Transformer for Emotion Recognition},
author = {Wenxuan Wang and Chenglei Wang and Xiaomei Wang and Xuli Shen and Qing Xu and Xuelin Qian},
booktitle = {ICASSP 2025},
year = {2025}
}