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Matching clothing images from customers and online shopping stores has richapplications in E-commerce. Existing algorithms encoded an image as a globalfeature vector and performed retrieval with the global representation. However,discriminative local information on clothes are submerged in this globalrepresentation, resulting in sub-optimal performance. To address this issue, wepropose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, whichlearns similarities between a query and a gallery cloth by using both globaland local representations in multiple scales. The similarity pyramid isrepresented by a Graph of similarity, where nodes represent similaritiesbetween clothing components at different scales, and the final matching scoreis obtained by message passing along edges. In GRNet, graph reasoning is solvedby training a graph convolutional network, enabling to align salient clothingcomponents to improve clothing retrieval. To facilitate future researches, weintroduce a new benchmark FindFashion, containing rich annotations of boundingboxes, views, occlusions, and cropping. Extensive experiments show that GRNetobtains new state-of-the-art results on two challenging benchmarks, e.g.,pushing the top-1, top-20, and top-50 accuracies on DeepFashion to 26%, 64%,and 75% (i.e., 4%, 10%, and 10% absolute improvements), outperformingcompetitors with large margins. On FindFashion, GRNet achieves considerableimprovements on all empirical settings.

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid