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

• URL: http://arxiv.org/abs/2601.14706v1
• Date: Wed, 21 Jan 2026 06:50:23 GMT
• Title: LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval
• Authors: Chao Gao, Siqiao Xue, Yimin Peng, Jiwen Fu, Tingyi Gu, Shanshan Li, Fan Zhou,
• Abstract summary: We present LookBench, a live, holistic and challenging benchmark for fashion image retrieval in real e-commerce settings.<n>LookBench includes both recent product images sourced from live websites and AI-generated fashion images.<n>Our experiments reveal that LookBench poses a significant challenge on strong baselines.
• Score: 28.812948794614034
• License: http://creativecommons.org/licenses/by/4.0/
• 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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