Skip-Connected Network with Gram Matrix for Product Image Retrieval

Abstract Designers usually retrieve and review existing relevant product images to draw inspiration when designing a new proposal. Obtaining reasonable retrieval results of a given target is a challenging task due to the large-scale, low-correlation searching process in the product image field. In this paper, an end-to-end skip-connected network is introduced to improve the accuracy of retrieval by directly addressing the above problems. In more detail, skip-connections are employed to combine the multi-resolution feature maps produced by a pre-trained backbone network, which provides important improvements to address the presence of large scale variance problem. And the second-order information of the multi-resolution feature maps is obtained by using gram matrix operation, which allows the network to exploit more representative features and their correlations. Experimental results indicate that the proposed method achieves better retrieval performance when compared to current state-of-the-art techniques on there normal benchmark datasets as well as the product image data.