Abstract: To cope with the large discrepancy between online shopping images and street photos,where the former is taken in ideal conditions of good lighting,clean backgrounds while the latter is captured in uncontrollable conditions,we propose a cross-domain clothing retrieval network based on an attention model.The first is to introduce an attention model to redistribute the proportion of different features on the basis of deep convolutional neural networks,which enhance the significant features of clothing images and suppress the unimportant features maps.The second is to introduce a short connection module and combine with the attention feature maps and the convolutional feature maps to further generate discriminative feature vectors for retrieval.The classification loss function and triplet loss are introduced to constraint training process of network and reduce the retrieval scope based on category information.The standard top-k retrieval precision is adopted as the evaluation index.DeepFashion dataset is selected to compare with the current algorithm for cross-domain clothing retrieval.The algorithm obtained the best retrieval performance(0.503)in the comparison of top-20 retrieval accuracy.Extensive experiments have revealed that the proposed method can effectively deal with the distortion of clothing and complex background interference.Meanwhile,it does not need huge labeled samples.The accuracy of cross-domain clothing retrieval is also improved.