Fashion Image Retrieval based on Parallel Branched Attention Network

Abstract

With the increase in vision-associated applications in e-commerce, image retrieval has become an emerging application in computer vision. Matching the exact user clothes from the database images is challenging due to noisy background, wide variation in orientation and lighting conditions, shape deformations, and the variation in the quality of the images between query and refined shop images. Most existing solutions tend to miss out on either incorporating low-level features or doing it effectively within their networks. Addressing the issue, we propose an attention-based multiscale deep Convolutional Neural Network (CNN) architecture called Parallel Attention ResNet (PAResNet50). It includes other supplementary branches with attention layers to extract low-level discriminative features and uses both high-level and low-level features for the notion of visual similarity. The attention layer focuses on the local discriminative regions and ignores the noisy background. Image retrieval output shows that our approach is robust to different lighting conditions. Experimental results on two public datasets show that our approach effectively locates the important region and significantly improves retrieval accuracy over simple network architectures without attention.