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 token--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.

运用 Decoupled Subject-Context Transformer (DSCT) 实现了一种单阶段的情感识别方法，同时实现了主体定位和情感分类，在两个常用的上下文感知情感识别数据集上取得了比两阶段方法更好的性能。