AttZoom: Attention Zoom for Better Visual Features

• URL: http://arxiv.org/abs/2508.03625v1
• Date: Tue, 05 Aug 2025 16:42:08 GMT
• Title: AttZoom: Attention Zoom for Better Visual Features
• Authors: Daniel DeAlcala, Aythami Morales, Julian Fierrez, Ruben Tolosana,
• Abstract summary: We present Attention Zoom, a model-agnostic spatial attention mechanism designed to improve feature extraction in convolutional neural networks (CNNs)<n>Our method introduces a standalone layer that spatially emphasizes high-importance regions in the input.<n>Visual analyses using Grad-CAM and spatial warping reveal that our method encourages fine-grained and diverse attention patterns.
• Score: 15.682871615735019
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present Attention Zoom, a modular and model-agnostic spatial attention mechanism designed to improve feature extraction in convolutional neural networks (CNNs). Unlike traditional attention approaches that require architecture-specific integration, our method introduces a standalone layer that spatially emphasizes high-importance regions in the input. We evaluated Attention Zoom on multiple CNN backbones using CIFAR-100 and TinyImageNet, showing consistent improvements in Top-1 and Top-5 classification accuracy. Visual analyses using Grad-CAM and spatial warping reveal that our method encourages fine-grained and diverse attention patterns. Our results confirm the effectiveness and generality of the proposed layer for improving CCNs with minimal architectural overhead.

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