ZooClaw-FashionSigLIP2：面向鲁棒时尚检索的蒸馏微调方法

摘要

将基础视觉语言编码器适配到专门的检索任务会产生一个根本性的权衡：目标分布上的性能提升以牺牲基础模型的广泛泛化能力为代价，而时尚检索正是这一问题的典型实例。我们提出的ZooClaw-FashionSigLIP2模型是一种专用于时尚领域的SigLIP2-base模型，通过一种简洁的方案解决了这一权衡——在精心设计的领域内数据上结合知识蒸馏进行全参数微调，随后与基础模型进行 WiseFT 权重插值——其性能超越了LoRA、更大规模的主干网络（参数高达1B）以及外部训练数据。在公平评估下，ZooClaw-FashionSigLIP2在所有基准测试中均优于所有基线方法。此外，我们发布了ZooClaw-Fashion——一个高质量的新型时尚检索基准，并对其广泛使用的基准进行了系统性质量分析，揭示并缓解了其公开真实数据中的结构性偏差。我们开源了模型权重及所有评估工具，以促进未来研究。

English

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.