Name
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. 1 1 Code: https://github.com/zbr17/AVSL. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.00738 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Recognition: detection,categorization,retrieval, Explainable computer vision, Representation learning | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Borui Zhang | 1 | 0 | 0.34 |
| Wenzhao Zheng | 2 | 15 | 2.91 |
| Jie Zhou | 3 | 2103 | 190.17 |
| Jiwen Lu | 4 | 3105 | 153.88 |