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.
Fashion and Apparel Classification using Convolutional Neural Networks
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abstract
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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Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation
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.