Pairwise Matching of Intermediate Representations for Fine-grained Explainability

• URL: http://arxiv.org/abs/2503.22881v1
• Date: Fri, 28 Mar 2025 21:13:43 GMT
• Title: Pairwise Matching of Intermediate Representations for Fine-grained Explainability
• Authors: Lauren Shrack, Timm Haucke, Antoine Salaün, Arjun Subramonian, Sara Beery,
• Abstract summary: We propose a new explainability method (PAIR-X) to generate fine-grained, highly-localized pairwise visual explanations.<n>By improving interpretability, PAIR-X enables humans to better distinguish correct and incorrect matches.
• Score: 7.415710605852485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The differences between images belonging to fine-grained categories are often subtle and highly localized, and existing explainability techniques for deep learning models are often too diffuse to provide useful and interpretable explanations. We propose a new explainability method (PAIR-X) that leverages both intermediate model activations and backpropagated relevance scores to generate fine-grained, highly-localized pairwise visual explanations. We use animal and building re-identification (re-ID) as a primary case study of our method, and we demonstrate qualitatively improved results over a diverse set of explainability baselines on 35 public re-ID datasets. In interviews, animal re-ID experts were in unanimous agreement that PAIR-X was an improvement over existing baselines for deep model explainability, and suggested that its visualizations would be directly applicable to their work. We also propose a novel quantitative evaluation metric for our method, and demonstrate that PAIR-X visualizations appear more plausible for correct image matches than incorrect ones even when the model similarity score for the pairs is the same. By improving interpretability, PAIR-X enables humans to better distinguish correct and incorrect matches. Our code is available at: https://github.com/pairx-explains/pairx

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