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arXiv · 2510.21808

Semantic Relation-Enhanced CLIP Adapter for Domain Adaptive Zero-Shot Learning

Jiaao Yu·Mingjie Han·Jinkun Jiang·Junyu Dong·Tao Gong·Man Lan
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Abstract

The high cost of data annotation has spurred research on training deep learning models in data-limited scenarios. Existing paradigms, however, fail to balance cross-domain transfer and cross-category generalization, giving rise to the demand for Domain-Adaptive Zero-Shot Learning (DAZSL). Although vision-language models (e.g., CLIP) have inherent advantages in the DAZSL field, current studies do not fully exploit their potential. Applying CLIP to DAZSL faces two core challenges: inefficient cross-category knowledge transfer due to the lack of semantic relation guidance, and degraded cross-modal alignment during target domain fine-tuning. To address these issues, we propose a Semantic Relation-Enhanced CLIP (SRE-CLIP) Adapter framework, integrating a Semantic Relation Structure Loss and a Cross-Modal Alignment Retention Strategy. As the first CLIP-based DAZSL method, SRE-CLIP achieves state-of-the-art performance on the I2AwA and I2WebV benchmarks, significantly outperforming existing approaches.

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Jiaao Yu, Mingjie Han, Jinkun Jiang, Junyu Dong, Tao Gong, Man Lan. 2025-10-21. Semantic Relation-Enhanced CLIP Adapter for Domain Adaptive Zero-Shot Learning. https://arxiv.org/abs/2510.21808

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