Semi-Supervised Adaptation of Vision-Language Models for Image Classification

• URL: http://arxiv.org/abs/2608.25485v1
• Date: Wed, 26 Aug 2026 07:54:50 GMT
• Title: Semi-Supervised Adaptation of Vision-Language Models for Image Classification
• Abstract summary: Self-Evolutionary CLIP (SE- CLIP) is a semi-supervised framework for label mining in scene classification.<n>Results on the UCM and NWPU benchmarks indicate that SE-CLIP significantly outperforms existing semi-supervised approaches.
• Score: 2.526705939946147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models like CLIP have shown sig- nificant potential in handling natural images, yet their perfor- mance is often limited by the distinct characteristics of satellite imagery. While parameter-efficient adaptation techniques exist, their efficacy is frequently limited by the scarcity of annotated samples. In this letter, we propose Self-Evolutionary CLIP (SE- CLIP), a semi-supervised framework designed for recursive label mining in scene classification. The approach follows a dual-phase pipeline, where an initial warm-up on a few annotated seeds is followed by a recursive discovery phase that iteratively identifies high-confidence samples from unlabeled pools. To maintain the integrity of the evolving support set, we employ a class-balanced selection strategy that prevents the model from being dominated by easily learned categories. Results on the UCM and NWPU benchmarks indicate that SE-CLIP significantly outperforms existing semi-supervised approaches. The framework provides a viable solution for adapting VLMs to the remote sensing domain with minimal human intervention.

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