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LRVS-Fashion: Extending Visual Search with Referring Instructions

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arxiv 2306.02928 v3 pith:UHVERML7 submitted 2023-06-05 cs.CV

Simon Lepage , Jérémie Mary , David Picard This is my paper · ORCID
classification cs.CV
keywords searchfashionvisualdatasetimageimagesindustrylrvs-fashion
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces a new challenge for image similarity search in the context of fashion, addressing the inherent ambiguity in this domain stemming from complex images. We present Referred Visual Search (RVS), a task allowing users to define more precisely the desired similarity, following recent interest in the industry. We release a new large public dataset, LRVS-Fashion, consisting of 272k fashion products with 842k images extracted from fashion catalogs, designed explicitly for this task. However, unlike traditional visual search methods in the industry, we demonstrate that superior performance can be achieved by bypassing explicit object detection and adopting weakly-supervised conditional contrastive learning on image tuples. Our method is lightweight and demonstrates robustness, reaching Recall at one superior to strong detection-based baselines against 2M distractors. The dataset is available at https://huggingface.co/datasets/Slep/LAION-RVS-Fashion .

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Cited by 3 Pith papers

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  3. Controllable Human Image Generation with Personalized Multi-Garments

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    BootComp bootstraps large synthetic multi-garment training data with a decomposition network, then trains a frozen-generator diffusion model that generates humans wearing multiple reference garments with higher report...

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