SOURCE-LINKED INTELLIGENCE

Image-Scale Robustness and Visual Recognition Performance: A Cross-Architecture Analysis

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

The sensitivity of visual recognition models to changes in image scale is well established, yet the factors governing this sensitivity across heterogeneous architectures remain unclear. In this work, we investigate whether scale robustness exhibits a common quantitative structure across modern vision models. We evaluate 20 pretrained ImageNet-1K classifiers spanning seven architectural families, including convolutional, mobile, efficient, and Transformer-based architectures. By systematically reducing input image scale, we construct scale-accuracy response curves and define a characteristic sc

Read original source ↗ Open in workspace

recordType

paper

region

Global