DAM: Dual Active Learning with Multimodal Foundation Model for Source-Free Domain Adaptation

• URL: http://arxiv.org/abs/2509.24896v1
• Date: Mon, 29 Sep 2025 15:06:56 GMT
• Title: DAM: Dual Active Learning with Multimodal Foundation Model for Source-Free Domain Adaptation
• Authors: Xi Chen, Hongxun Yao, Zhaopan Xu, Kui Jiang,
• Abstract summary: Source-free active domain adaptation (SFADA) enhances knowledge transfer from a source model to an unlabeled target domain using limited manual labels selected via active learning.<n>We propose Dual Active learning with Multimodal (DAM) foundation model, a novel framework that integrates multimodal supervision from a ViL model to complement sparse human annotations.<n>Extensive experiments demonstrate that DAM consistently outperforms existing methods and sets a new state-of-the-art across multiple SFADA benchmarks and active learning strategies.
• Score: 53.323488295994395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Source-free active domain adaptation (SFADA) enhances knowledge transfer from a source model to an unlabeled target domain using limited manual labels selected via active learning. While recent domain adaptation studies have introduced Vision-and-Language (ViL) models to improve pseudo-label quality or feature alignment, they often treat ViL-based and data supervision as separate sources, lacking effective fusion. To overcome this limitation, we propose Dual Active learning with Multimodal (DAM) foundation model, a novel framework that integrates multimodal supervision from a ViL model to complement sparse human annotations, thereby forming a dual supervisory signal. DAM initializes stable ViL-guided targets and employs a bidirectional distillation mechanism to foster mutual knowledge exchange between the target model and the dual supervisions during iterative adaptation. Extensive experiments demonstrate that DAM consistently outperforms existing methods and sets a new state-of-the-art across multiple SFADA benchmarks and active learning strategies.

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