@inproceedings{li-etal-2026-towards,
    title = "Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization",
    author = "Li, Jiaqi  and
      Wang, Guangming  and
      Zheng, Shuntian  and
      Ni, Minzhe  and
      Lu, Xiaoman  and
      Ye, Guanghui  and
      Guan, Yu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.508/",
    pages = "11087--11104",
    ISBN = "979-8-89176-390-6",
    abstract = "Temporal Action Localization (TAL) requires identifying both the boundaries and categories of actions in untrimmed videos. While vision-language models (VLMs) offer rich semantics to complement visual evidence, existing approaches tend to overemphasize linguistic priors at the expense of visual performance, leading to a pronounced modality bias. We propose ActionVLM, a vision-language aggregation framework that systematically mitigates modality bias in TAL. Our key insight is to preserve vision as the dominant signal while adaptively exploiting language only when beneficial. To this end, we introduce (i) a debiasing reweighting module that estimates the language advantage{---}the incremental benefit of language over vision-only predictions{---}and dynamically reweights language modality accordingly, and (ii) a residual aggregation strategy that treats language as a complementary refinement rather than the primary driver. This combination alleviates modality bias, reduces overconfidence from linguistic priors, and strengthens temporal reasoning. Experiments on THUMOS14 show that our model outperforms state-of-the-art by up to 3.2{\%} mAP. Our code is available at https://github.com/JiaqiLi404/ActionVLM"
}