ZooClaw-FashionSigLIP2: Distilled Fine-tuning for Robust Fashion Retrieval
Adapting a foundation vision-language encoder to a specialized retrieval task creates a fundamental tradeoff: gains on the target distribution come at the cost of the foundation model's broad generalization, and fashion retrieval is a stringent instance of this problem. We present ZooClaw-FashionSigLIP2, a fashion-specialized SigLIP2-base model that resolves this tradeoff with a simple recipe -- full fine-tuning with knowledge distillation on curated in-domain data, followed by \wiseft~\citep{wortsman2022wiseft} weight interpolation with the base model -- and outperforms LoRA, larger backbones (up to 1B parameters), and external training data. Under fair evaluation, ZooClaw-FashionSigLIP2...
| Authors | Siqiao Xue, Chunxue Xu |
|---|---|
| Primary category | cs.CV |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
Models and datasets referencing this paper
- srpone/zooclaw-fashionsiglip2Model
- srpone/hm-evalDataset
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