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

The paper introduces Self‑Evolutionary CLIP (SE‑CLIP), a semi‑supervised framework that adapts vision‑language models like CLIP to satellite imagery. SE‑CLIP uses a two‑phase pipeline: an initial warm‑up on a small set of annotated seeds followed by a recursive discovery phase that iteratively selects high‑confidence samples from unlabeled data. A class‑balanced selection strategy is applied to keep the evolving support set balanced, and experiments on the UCM and NWPU benchmarks show that SE‑CLIP outperforms existing semi‑supervised methods.

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• llms
• computer-vision
• multimodal
• benchmarks