Papers › Attributable Visual Similarity Learning
Attributable Visual Similarity Learning
Borui Zhang, Wenzhao Zheng, Jie zhou, Jiwen Lu
This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplainability by mapping each sample to a single point in the embedding space with a distance metric (e.g., Mahalanobis distance, Euclidean distance). Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph and then infer the overall similarity accordingly. Furthermore, we establish a bottom-up similarity construction and top-down similarity inference framework to infer the similarity based on semantic hierarchy consistency. We first identify unreliable higher-level similarity nodes and then correct them using the most coherent adjacent lower-level similarity nodes, which simultaneously preserve traces for similarity attribution. Extensive experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods and verify the interpretability of our framework. Code is available at https://github.com/zbr17/AVSL.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Metric Learning | CARS196 | ResNet-50 + AVSL | R@1 | 91.5 | #5 of 36 | Archive leaderboard | report |
| Metric Learning | CUB-200-2011 | ResNet-50 + AVSL | R@1 | 71.9 | #6 of 30 | Archive leaderboard | report |
| Metric Learning | Stanford Online Products | ResNet50 + AVSL | R@1 | 79.6 | #26 of 33 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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