# An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images Source: [An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:37:17.197Z Authors: Modesto Castrill\\'on-Santana, Oliverio J Santana, David Freire-Obreg\\'on, Daniel Hern\\'andez-Sosa, Javier Lorenzo-Navarro Published: 2025-04-30T04:38:05.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()