Revisiting the Ordering of Channel and Spatial Attention: A Comprehensive Study on Sequential and Parallel Designs

TL;DR

This paper systematically compares different channel and spatial attention configurations in deep learning, revealing how their effectiveness varies with data scale and task type, and providing guidelines for designing attention modules.

Contribution

It offers a comprehensive evaluation of 18 attention topologies under a unified framework, establishing principles for selecting attention structures based on data scale and task.

Findings

Channel-Multi-scale Spatial best for few-shot tasks

Parallel learnable fusion superior in medium-scale tasks

Parallel structures with dynamic gating excel in large-scale tasks

Abstract

Attention mechanisms have become a core component of deep learning models, with Channel Attention and Spatial Attention being the two most representative architectures. Current research on their fusion strategies primarily bifurcates into sequential and parallel paradigms, yet the selection process remains largely empirical, lacking systematic analysis and unified principles. We systematically compare channel-spatial attention combinations under a unified framework, building an evaluation suite of 18 topologies across four classes: sequential, parallel, multi-scale, and residual. Across two vision and nine medical datasets, we uncover a "data scale-method-performance" coupling law: (1) in few-shot tasks, the "Channel-Multi-scale Spatial" cascaded structure achieves optimal performance; (2) in medium-scale tasks, parallel learnable fusion architectures demonstrate superior results; (3) in large-scale tasks, parallel structures with dynamic gating yield the best performance. Additionally, experiments indicate that the "Spatial-Channel" order is more stable and effective for fine-grained classification, while residual connections mitigate vanishing gradient problems across varying data scales. We thus propose scenario-based guidelines for building future attention modules. Code is open-sourced at https://github.com/DWlzm.