arXiv Computer Vision November 9, 2026 By Song Tang, Yunxiang Bai, Wenxin Su, Mao Ye, Jianwei Zhang, Xiatian Zhu

Source-Free Domain Adaptation with Vision-Language Prior

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Source-Free Domain Adaptation with Vision-Language Prior proposes DIFO++, a two-step adaptation framework that customizes a vision‑language model (e.g., CLIP) to a target domain via prompt learning and then distills its knowledge into a target model. The method focuses on a gap region where features are entangled and class‑ambiguous, generating reliable pseudo‑labels by fusing predictions from both models and employing memory, category attention, predictive consistency, and entropy minimization to align semantics. Experiments demonstrate that DIFO++ outperforms existing state‑of‑the‑art approaches.

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