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arXiv · 2601.14706

LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval

Gensmo. ai·Chao Gao·Siqiao Xue·Jiwen Fu·Tingyi Gu·Shanshan Li·Fan Zhou
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Abstract

In this paper, we present LookBench (We use the term "look" to reflect retrieval that mirrors how people shop -- finding the exact item, a close substitute, or a visually consistent alternative.), a live, holistic and challenging benchmark for fashion image retrieval in real e-commerce settings. LookBench includes both recent product images sourced from live websites and AI-generated fashion images, reflecting contemporary trends and use cases. Each test sample is time-stamped and we intend to update the benchmark periodically, enabling contamination-aware evaluation aligned with declared training cutoffs. Grounded in our fine-grained attribute taxonomy, LookBench covers single-item and outfit-level retrieval across. Our experiments reveal that LookBench poses a significant challenge on strong baselines, with many models achieving below $60\%$ Recall@1. Our proprietary model achieves the best performance on LookBench, and we release an open-source counterpart that ranks second, with both models attaining state-of-the-art results on legacy Fashion200K evaluations. LookBench is designed to be updated semi-annually with new test samples and progressively harder task variants, providing a durable measure of progress. We publicly release our leaderboard, dataset, evaluation code, and trained models.

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Gensmo. ai, Chao Gao, Siqiao Xue, Jiwen Fu, Tingyi Gu, Shanshan Li, Fan Zhou. 2026-01-21. LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval. https://arxiv.org/abs/2601.14706

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