# Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification Source: [Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:44.728Z Authors: Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang Published: 2020-10-11T17:55:06.000Z ## Abstract The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and high memory footprint. Inspired by the fact that not all regions in an image are task-relevant, we propose a novel framework that performs efficient image classification by processing a sequence of relatively small inputs, which are strategically selected from the original image with reinforcement learning. Such a dynamic decision process naturally facilitates adaptive inference at test time, i.e., it can be terminated once the model is sufficiently confident about its prediction and thus avoids further redundant computation. Notably, our framework is general and flexible as it is compatible with most of the state-of-the-art light-weighted CNNs (such as MobileNets, EfficientNets and RegNets), which can be conveniently deployed as the backbone feature extractor. Experiments on ImageNet show that our method consistently improves the computational efficiency of a wide variety of deep models. For example, it further reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 20% without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet (Accuracy and Input Resolution)](): Image Classification; ImageNet; Top-1 Accuracy: 75.93. - [Image Classification on ImageNet 1K (val) (Standard Accuracy)](): Image Classification; ImageNet-1k (val); Top-1 Accuracy: 73.8. ## Original sources and implementation [Original paper]() [PDF]()