@inproceedings{saim-etal-2025-anatomy,
    title = "Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models",
    author = "Saim, Mohammad  and
      Duong, Phan Anh  and
      Luong, Cat  and
      Bhanderi, Aniket  and
      Jiang, Tianyu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.1276/",
    doi = "10.18653/v1/2025.findings-emnlp.1276",
    pages = "23480--23495",
    ISBN = "979-8-89176-335-7",
    abstract = "The embodiment of emotional reactions from body parts contains rich information about our affective experiences. We propose a framework that utilizes state-of-the-art large vision language models (LVLMs) to generate Embodied LVLM Emotion Narratives (ELENA). These are well-defined, multi-layered text outputs, primarily comprising descriptions that focus on the salient body parts involved in emotional reactions. We also employ attention maps and observe that contemporary models exhibit a persistent bias towards the facial region. Despite this limitation, we observe that our employed framework can effectively recognize embodied emotions in face-masked images, outperforming baselines without any fine-tuning. ELENA opens a new trajectory for embodied emotion analysis across the modality of vision and enriches modeling in an affect-aware setting."
}