Deep Search with Attribute-aware Deep Network

Junshi Huang, Wei Xia, Shuicheng YAN

Abstract: In this demo, we present Deep Search, an attribute-aware fashion-related retrieval system, based on the convolutional neural network by taking clothes as a concrete example. In this system, the visual appearance and semantic attributes of products can be seamlessly integrated into our model in a "dividing and combining" manner. Rather than modelling one attribute per classifier, we design a holistic tree-structure network for exploiting multiple attributes simultaneously, with each branch of network corresponding to one attribute. Therefore, the high-level feature from the conjunction layer of the network can comprehensively preserve both the visual and semantic information of products. The promising retrieval results indicate the great potential of neural feature for attribute-aware retrieval task. The supplementary slides can be found in http://goo.gl/zpobN6.