@inproceedings{
li2024two,
title={Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer},
author={Xinpeng Li and Teng Wang and Shuyi Mao and Jinbao Wang and Jian Zhao and Xiaojiang Peng and Feng Zheng and Xuelong Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=1rvrYgWeEG}
}
Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues. Current approaches typically follow a two-stage pipeline: first localize subjects by off-the-shelf detectors, then perform emotion classification through the late fusion of subject and context features. However, the complicated paradigm suffers from disjoint training stages and limited interaction between fine-grained subject-context elements. To address the challenge, we present a single-stage emotion recognition approach, employing a Decoupled Subject-Context Transformer (DSCT), for simultaneous subject localization and emotion classification. Rather than compartmentalizing training stages, we jointly leverage box and emotion signals as supervision to enrich subject-centric feature learning. Furthermore, we introduce DSCT to facilitate interactions between fine-grained subject-context cues in a ``decouple-then-fuse'' manner. The decoupled query tokens—subject queries and context queries—gradually intertwine across layers within DSCT, during which spatial and semantic relations are exploited and aggregated. We evaluate our single-stage framework on two widely used context-aware emotion recognition datasets, CAER-S and EMOTIC. Our approach surpasses two-stage alternatives with fewer parameter numbers, achieving a 3.39% accuracy improvement and a 6.46% average precision gain on CAER-S and EMOTIC datasets, respectively.
Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer
Xinpeng Li Teng Wang Shuyi Mao Jinbao Wang Jian Zhao Xiaojiang Peng Feng Zheng Xuelong Li