Open Set Domain Adaptation with Vision-language models via Gradient-aware Separation

• URL: http://arxiv.org/abs/2505.13507v1
• Date: Fri, 16 May 2025 12:31:17 GMT
• Title: Open Set Domain Adaptation with Vision-language models via Gradient-aware Separation
• Authors: Haoyang Chen,
• Abstract summary: Open-Set Domain Adaptation (OSDA) confronts the dual challenge of aligning known-class distributions across domains.<n>We propose to harness Contrastive Language-Image Pretraining (CLIP) to address these limitations.
• Score: 0.6118897979046375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-Set Domain Adaptation (OSDA) confronts the dual challenge of aligning known-class distributions across domains while identifying target-domain-specific unknown categories. Current approaches often fail to leverage semantic relationships between modalities and struggle with error accumulation in unknown sample detection. We propose to harness Contrastive Language-Image Pretraining (CLIP) to address these limitations through two key innovations: 1) Prompt-driven cross-domain alignment: Learnable textual prompts conditioned on domain discrepancy metrics dynamically adapt CLIP's text encoder, enabling semantic consistency between source and target domains without explicit unknown-class supervision. 2) Gradient-aware open-set separation: A gradient analysis module quantifies domain shift by comparing the L2-norm of gradients from the learned prompts, where known/unknown samples exhibit statistically distinct gradient behaviors. Evaluations on Office-Home show that our method consistently outperforms CLIP baseline and standard baseline. Ablation studies confirm the gradient norm's critical role.

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