@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://aclanthology.org/2025.acl-long.967/",
doi = "10.18653/v1/2025.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}."
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<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 Adversarial Alignment with Anchor Dragging Drift, abbreviated as A³D², for multimodal domain adaptation with partially shifted modalities. Comprehensive empirical results verify the effectiveness of the proposed approach, and demonstrate that A³D² achieves superior performance compared with state-of-the-art approaches. The code is available at: https://github.com/sunjunaimer/A3D2.git.</abstract>
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%0 Conference Proceedings
%T Adversarial Alignment with Anchor Dragging Drift (A³D²): Multimodal Domain Adaptation with Partially Shifted Modalities
%A Sun, Jun
%A Zhang, Xinxin
%A Hong, Simin
%A Zhu, Jian
%A Zeng, Lingfang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F sun-etal-2025-adversarial
%X 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 Adversarial Alignment with Anchor Dragging Drift, abbreviated as A³D², for multimodal domain adaptation with partially shifted modalities. Comprehensive empirical results verify the effectiveness of the proposed approach, and demonstrate that A³D² achieves superior performance compared with state-of-the-art approaches. The code is available at: https://github.com/sunjunaimer/A3D2.git.
%R 10.18653/v1/2025.acl-long.967
%U https://aclanthology.org/2025.acl-long.967/
%U https://doi.org/10.18653/v1/2025.acl-long.967
%P 19680-19690
Markdown (Informal)
[Adversarial Alignment with Anchor Dragging Drift (A3D2): Multimodal Domain Adaptation with Partially Shifted Modalities](https://aclanthology.org/2025.acl-long.967/) (Sun et al., ACL 2025)
ACL