EmoICL: Enhancing Visual Emotion Recognition with Emotion-Aware In-Context Learning

Visual emotion recognition aims to infer human emotional states from visual cues. Adapting pretrained Vision-Language Models (VLMs) to this task remains challenging. In-Context Learning (ICL) enables a frozen model to condition its predictions on a few labeled demonstrations without task-specific fine-tuning. Here, demonstrations are retrieved from an existing labeled support pool; the small prompt size does not imply a small total annotation budget. Despite this potential, how to effectively leverage ICL for visual emotion recognition remains underexplored. In this paper, we propose EmoICL, a hybrid retrieval framework that leverages VLMs to enhance emotion recognition. To this end, we first introduce Emotion-Aware Dual Retrieval (EDR), which generates emotion-sensitive descriptions using guided prompts and refines retrieved samples based on visual similarity and cross-modal relevance. Then, we design Category-Aware Diversity Optimization (CDO), which combines category-aware filtering with Maximum Marginal Relevance to reduce label-level interference and improve visual diversity. Experiments on three benchmark datasets show that EmoICL achieves the best average performance across different VLM backbones, with a mean absolute gain of approximately 1.96 percentage points over the strongest baseline by average accuracy for each backbone. Code is available at: https://github.com/Atlantic4Li/EmoICL.