  Clothing retrieval is a challenging problem in computer vision. With the
advance of Convolutional Neural Networks (CNNs), the accuracy of clothing
retrieval has been significantly improved. FashionNet[1], a recent study,
proposes to employ a set of artificial features in the form of landmarks for
clothing retrieval, which are shown to be helpful for retrieval. However, the
landmark detection module is trained with strong supervision which requires
considerable efforts to obtain. In this paper, we propose a self-learning
Visual Attention Model (VAM) to extract attention maps from clothing images.
The VAM is further connected to a global network to form an end-to-end network
structure through Impdrop connection which randomly Dropout on the feature maps
with the probabilities given by the attention map. Extensive experiments on
several widely used benchmark clothing retrieval data sets have demonstrated
the promise of the proposed method. We also show that compared to the trivial
Product connection, the Impdrop connection makes the network structure more
robust when training sets of limited size are used.
