@inproceedings{sun-etal-2025-adversarial,
    title = "Adversarial Alignment with Anchor Dragging Drift ($A^3D^2$): Multimodal Domain Adaptation with Partially Shifted Modalities",
    author = "Sun, Jun  and
      Zhang, Xinxin  and
      Hong, Simin  and
      Zhu, Jian  and
      Zeng, Lingfang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.967/",
    pages = "19680--19690",
    ISBN = "979-8-89176-251-0",
    abstract = "Multimodal learning has celebrated remarkable success across diverse areas, yet faces the challenge of prohibitively expensive data collection and annotation when adapting models to new environments. In this context, domain adaptation has gained growing popularity as a technique for knowledge transfer, which, however, remains underexplored in multimodal settings compared with unimodal ones. This paper investigates multimodal domain adaptation, focusing on a practical partially shifting scenario where some modalities (referred to as anchors) remain domain-stable, while others (referred to as drifts) undergo a domain shift. We propose a bi-alignment scheme to simultaneously perform drift-drift and anchor-drift matching. The former is achieved through adversarial learning, aligning the representations of the drifts across source and target domains; the latter corresponds to an ``anchor dragging drift'' strategy, which matches the distributions of the drifts and anchors within the target domain using the optimal transport (OT) method. The overall design principle features \textbf{A}dversarial \textbf{A}lignment with \textbf{A}nchor \textbf{D}ragging \textbf{D}rift, abbreviated as \textbf{ $A^3D^2$}, for multimodal domain adaptation with partially shifted modalities. Comprehensive empirical results verify the effectiveness of the proposed approach, and demonstrate that $A^3D^2$ achieves superior performance compared with state-of-the-art approaches. The code is available at: \url{https://github.com/sunjunaimer/A3D2.git}."
}