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arXiv · 2504.21309

An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images

Modesto Castrillón-Santana·Oliverio J Santana·David Freire-Obregón·Daniel Hernández-Sosa·Javier Lorenzo-Navarro
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Abstract

Facial expression recognition (FER) is a key research area in computer vision and human-computer interaction. Despite recent advances in deep learning, challenges persist, especially in generalizing to new scenarios. In fact, zero-shot FER significantly reduces the performance of state-of-the-art FER models. To address this problem, the community has recently started to explore the integration of knowledge from Large Language Models for visual tasks. In this work, we evaluate a broad collection of locally executed Visual Language Models (VLMs), avoiding the lack of task-specific knowledge by adopting a Visual Question Answering strategy. We compare the proposed pipeline with state-of-the-art FER models, both integrating and excluding VLMs, evaluating well-known FER benchmarks: AffectNet, FERPlus, and RAF-DB. The results show excellent performance for some VLMs in zero-shot FER scenarios, indicating the need for further exploration to improve FER generalization.

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Modesto Castrillón-Santana, Oliverio J Santana, David Freire-Obregón, Daniel Hernández-Sosa, Javier Lorenzo-Navarro. 2025-04-30. An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images. https://arxiv.org/abs/2504.21309

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