mlsanityData to 13 Oct 2026

Attributable Visual Similarity Learning

arXiv 2203.14932 ↗First posted 28 Mar 2022cs.CV cs.LGcited by 12

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 38 of its 60 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

38 of 60 references.

References · 38

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26 more
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Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 4; 2025: 3.

12 papers.

  1. 2023Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-trainingcited by 36
  2. 2025FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasingcited by 14
  3. 2023Introspective Deep Metric Learningcited by 7
  4. 2024Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learningcited by 6
  5. 2022Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimizationcited by 5
  6. 2024Globally Correlation-Aware Hard Negative Generationcited by 5
  7. 2025Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learningcited by 4
  8. 2024A Unified Deep Semantic Expansion Framework for Domain-Generalized Person Re-identificationcited by 3
  9. 2022Probabilistic Deep Metric Learning for Hyperspectral Image Classificationcited by 2
  10. 2025Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selectioncited by 1
  11. 2022Introspective Deep Metric Learning for Image Retrievalcited by 0
  12. 2024Explainable Metric Learning for Deflating Data Biascited by 0