AttZoom: Attention Zoom for Better Visual Features
This addresses the need for better visual feature extraction in CNNs with a model-agnostic approach, though it appears incremental as it builds on existing attention methods.
The paper tackled the problem of improving feature extraction in CNNs by introducing Attention Zoom, a modular spatial attention mechanism, and showed consistent accuracy improvements on CIFAR-100 and TinyImageNet datasets.
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.