@inproceedings{yao-etal-2026-towards,
    title = "Towards Robust Evaluation of Visual Activity Recognition: Resolving Verb Ambiguity with Sense Clustering",
    author = "Yao, Louie Hong  and
      Jarvis, Nicholas  and
      Jiang, Tianyu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.32/",
    pages = "656--672",
    ISBN = "979-8-89176-386-9",
    abstract = "Evaluating visual activity recognition systems is challenging due to inherent ambiguities in verb semantics and image interpretation. When describing actions in images, synonymous verbs can refer to the same event (e.g., *brushing* vs. *grooming*), while different perspectives can lead to equally valid but distinct verb choices (e.g., *piloting* vs. *operating*). Standard exact-match evaluation, which relies on a single gold answer, fails to capture these ambiguities, resulting in an incomplete assessment of model performance. To address this, we propose a vision-language clustering framework that constructs **verb sense clusters**, providing a more robust evaluation. Our analysis of the imSitu dataset shows that each image maps to around four sense clusters, with each cluster representing a distinct perspective of the image. We evaluate multiple activity recognition models and compare our cluster-based evaluation with standard evaluation methods. Additionally, our human alignment analysis suggests that the cluster-based evaluation better aligns with human judgments, offering a more nuanced assessment of model performance."
}