Unsupervised Domain Specialization for Multimodal Embeddings

ACL ARR 2026 January Submission2878 Authors

Keywords: Multimodal Retrieval, Unsupervised Domain Adaptation

Abstract: Recent advances in multimodal foundation models have enabled powerful generic embeddings. However, real-world applications—such as e-commerce or healthcare—rely on domain-specific patterns that these generic models often fail to capture precisely. Adapting them without readily available labeled data remains a critical challenge. In this paper, we propose MM-UDA, a robust unsupervised domain adaptation framework designed to systematically convert a generic multimodal embedding model into a domain expert using only unlabeled target data. Our framework employs a two-stage strategy. First, we introduce a within-modality pseudo-labeling task refined by Gaussian Mixture Model filtering, which rapidly adapts the model to domain-specific features while suppressing label noise. Then, we align modalities through a score-guided learning scheme to refine cross-modal pairing. Extensive evaluations on three real-world datasets demonstrate that MM-UDA both significantly enhances various backbone models and consistently outperforms competitive baselines, confirming its effectiveness for cross-modal retrieval in specialized domains. The source code is publicly available at \url{https://anonymous.4open.science/r/UDSME}.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: multimodality embedding, text-image matching, content retrieval

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 2878