根据面部表情的表达力动态调整情感识别权重，提升社交情境下的情绪判断准确率。
Salience Adjustment for Context-Based Emotion Recognition
- 基于贝叶斯线索整合与视觉语言模型，动态调节面部与情境信息权重。
- 在囚徒困境场景中，融合显著性调整后情绪识别性能显著提升。
- 适用于社交互动、人机交互等需要理解复杂情绪的多模态场景。
动态社交情境中的情感识别需理解面部表情与情境线索之间的复杂互动。本文提出一种显著性调整框架，结合贝叶斯线索整合（BCI）与视觉语言模型（VLMs），根据面部线索的表达力动态加权面部与情境信息。我们通过人类标注和自动情绪识别系统，在设计用于激发情绪反应的囚徒困境场景中评估该方法。结果表明，引入显著性调整可显著提升情绪识别性能，为未来将该框架扩展至更广泛的社交情境和多模态应用提供了有前景的方向。
原文摘要 · Abstract (English)
Emotion recognition in dynamic social contexts requires an understanding of the complex interaction between facial expressions and situational cues. This paper presents a salience-adjusted framework for context-aware emotion recognition with Bayesian Cue Integration (BCI) and Visual-Language Models (VLMs) to dynamically weight facial and contextual information based on the expressivity of facial cues. We evaluate this approach using human annotations and automatic emotion recognition systems in prisoner's dilemma scenarios, which are designed to evoke emotional reactions. Our findings demonstrate that incorporating salience adjustment enhances emotion recognition performance, offering promising directions for future research to extend this framework to broader social contexts and multimodal applications.
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