Revisiting Shape and Texture Reliance with Category-Separability-Calibrated Suppression
Feature-suppression evaluations infer model reliance on shape or texture from the accuracy loss caused by attenuating each type of information. Such losses, however, conflate feature reliance with the amount of category-relevant information removed by the corresponding transformation. Because shape and texture are suppressed using different operators, their effects are not directly comparable. We introduce the Semantic Degradation Index (SDI), which quantifies the suppression-induced reduction in category separability relative to clean images in a fixed clean-reference discriminative space constructed from handcrafted features. On an ImageNet16-like benchmark, we use SDI to compare Gaussi...
| Authors | Ning Jiang, Tianyi Luo, Zhengyong Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 July 2026 |
| Last updated at source | 13 August 2026 |
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