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Fashion and Apparel Classification using Convolutional Neural Networks

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arxiv 1811.04374 v1 pith:AMUX7XQG submitted 2018-11-11 cs.CV

classification cs.CV
keywords classificationapparelconvolutionaldifferentfashionmodelsnetworksneural
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We present an empirical study of applying deep Convolutional Neural Networks (CNN) to the task of fashion and apparel image classification to improve meta-data enrichment of e-commerce applications. Five different CNN architectures were analyzed using clean and pre-trained models. The models were evaluated in three different tasks person detection, product and gender classification, on two small and large scale datasets.

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  1. Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation

    cs.LG 2025-08 conditional novelty 5.0 of 10

    FGAT combines hierarchical user-outfit-item graphs, multimodal item embeddings, and attention weighting to improve personalized outfit recommendation over the HFGN baseline on the POG dataset.

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