Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We investigate this relationship by evaluating 11 computer vision models representing increasing levels of depth and complexity within the ResNet, DenseNet, and Vision Transformer families, trained from scratch or pretrained, across three image datasets with ground-truth segmentation masks. For each model, we generate explanations using five post-hoc explainable AI methods and quantify mask alignment using two localisation metrics: Relevance Rank Accuracy (Arras et al., 2022) and the proposed Dual-Polarity Precision, whi...
| Authors | Mateusz Cedro, Marcin Chlebus |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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