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。

一步到位：基于分离的主题 - 上下文 Transformer 的单阶段情感识别

Two in One Go: Single-stage Emotion Recognition with Decoupled  Subject-context Transformer

Context-aware emotion recognition (CAER) has recently boosted the practical
applications of affective computing techniques in unconstrained environments.
Mainstream CAER methods invariably extract ensemble representations from
diverse contexts and subject-centred characteristics to perceive the target
person's emotional state. Despite advancements, the biggest challenge remains
due to context bias interference. The harmful bias forces the models to rely on
spurious correlations between background contexts and emotion labels in
likelihood estimation, causing severe performance bottlenecks and confounding
valuable context priors. In this paper, we propose a counterfactual emotion
inference (CLEF) framework to address the above issue. Specifically, we first
formulate a generalized causal graph to decouple the causal relationships among
the variables in CAER. Following the causal graph, CLEF introduces a
non-invasive context branch to capture the adverse direct effect caused by the
context bias. During the inference, we eliminate the direct context effect from
the total causal effect by comparing factual and counterfactual outcomes,
resulting in bias mitigation and robust prediction. As a model-agnostic
framework, CLEF can be readily integrated into existing methods, bringing
consistent performance gains.

提出了一个反事实情感推理框架 (CLEF)，通过消除上下文偏差和增强预测性能，改善情感识别中的整体因果效应。

上下文去偏的稳健情感识别

Robust Emotion Recognition in Context Debiasing

Context-Aware Emotion Recognition (CAER) is a crucial and challenging task
that aims to perceive the emotional states of the target person with contextual
information. Recent approaches invariably focus on designing sophisticated
architectures or mechanisms to extract seemingly meaningful representations
from subjects and contexts. However, a long-overlooked issue is that a context
bias in existing datasets leads to a significantly unbalanced distribution of
emotional states among different context scenarios. Concretely, the harmful
bias is a confounder that misleads existing models to learn spurious
correlations based on conventional likelihood estimation, significantly
limiting the models' performance. To tackle the issue, this paper provides a
causality-based perspective to disentangle the models from the impact of such
bias, and formulate the causalities among variables in the CAER task via a
tailored causal graph. Then, we propose a Contextual Causal Intervention Module
(CCIM) based on the backdoor adjustment to de-confound the confounder and
exploit the true causal effect for model training. CCIM is plug-in and
model-agnostic, which improves diverse state-of-the-art approaches by
considerable margins. Extensive experiments on three benchmark datasets
demonstrate the effectiveness of our CCIM and the significance of causal
insight.

本文提供了基于因果关系的视角来解决 “上下文感知情感识别（CAER）” 中的数据集中存在的偏见问题。通过构建因果图，设计了 Contextual Causal Intervention Module (CCIM) 来提高模型表现并且在三个基准数据集上进行了充分的实验来证明其有效性和重要性。