| title | ZooClaw-FashionSigLIP2: Distilled Fine-tuning for Robust Fashion Retrieval |
|---|---|
| authors | Siqiao Xue, Chunxue Xu |
| date | 2026-06-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.27708 |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | 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~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 outperforms all baselines on every benchmark in our suite. In addition, we release ZooClaw-Fashion, a new high-quality fashion retrieval benchmark, and a systematic quality analysis of widely-used benchmarks that exposes and mitigates structural biases in their public ground truth. We open-source the model weights and all evaluation artifacts to facilitate future research. |
cite
@misc{xue2026zooclaw,
title={ZooClaw-FashionSigLIP2: Distilled Fine-tuning for Robust Fashion Retrieval},
author={Siqiao Xue and Chunxue Xu},
year={2026},
eprint={2606.27708},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.27708}
}