# Salience Adjustment for Context-Based Emotion Recognition

arXiv: https://arxiv.org/abs/2507.15878 · HTML: https://mlsanity.com/paper/2507.15878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Improve Vision Language Model Chain-of-thought Reasoning](https://mlsanity.com/paper/2410.16198/) (2024)
- [Context-Aware Emotion Recognition Networks](https://mlsanity.com/paper/1908.05913/) (2019)
- [Context Based Emotion Recognition using EMOTIC Dataset](https://mlsanity.com/paper/2003.13401/) (2020)
- [Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition](https://mlsanity.com/paper/2207.10299/) (2022)
- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition](https://mlsanity.com/paper/2312.04293/) (2023)
- [Chain of Thought Prompt Tuning in Vision Language Models](https://mlsanity.com/paper/2304.07919/) (2023)
- [Knowledge-based Emotion Recognition using Large Language Models](https://mlsanity.com/paper/2408.04123/) (2024)

## Abstract (arXiv)

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.