Attributable Visual Similarity Learning

Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu

Abstract: This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and ex-plainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplain-ability by mapping each sample to a single point in the em-bedding space with a distance metric (e.g., Mahalanobis distance, Euclidean distance). Motivated by the human se-mantic similarity cognition, we propose a generalized simi-larity learning paradigm to represent the similarity between two images with a graph and then infer the overall simi-larity accordingly. Furthermore, we establish a bottom-up similarity construction and top-down similarity inference framework to infer the similarity based on semantic hier-archy consistency. We first identify unreliable higher-level similarity nodes and then correct them using the most co-herent adjacent lower-level similarity nodes, which simulta-neously preserve traces for similarity attribution. Extensive experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improve-ments over existing deep similarity learning methods and verify the interpretability of our framework. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://github.com/zbr17/AVSL.