Lookbench: A Live And Holistic Open Benchmark For Fashion Image Retrieval

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 b

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyai2026lookbench

Related papers

• A Strong Baseline For Fashion Retrieval With Person Re-identification Models (2020)8.09
• Performance-efficiency Trade-off For Fashion Image Retrieval (2025)0.00
• Visual Product Search Benchmark (2026)0.00
• Methods And Advancement Of Content-based Fashion Image Retrieval: A Review (2023)0.00
• Training And Challenging Models For Text-guided Fashion Image Retrieval (2022)0.00
• Looksync: Large-scale Visual Product Search System For Ai-generated Fashion Looks (2025)0.00
• Benchmarking Image Embeddings For E-commerce: Evaluating Off-the Shelf Foundation Models, Fine-tuning Strategies And Practical Trade-offs (2025)0.00
• Efficient Discovery And Effective Evaluation Of Visual Perceptual Similarity: A Benchmark And Beyond (2023)4.52