🤖 AI Summary
This work addresses the lack of systematic analysis and unified design principles in existing channel-spatial attention fusion strategies. Under a consistent experimental framework, the authors construct and comprehensively evaluate 18 channel-spatial attention topologies, spanning serial, parallel, multi-scale, and residual architectures. Extensive experiments across diverse vision and medical imaging datasets reveal a coupling relationship among data scale, architectural design, and performance. The study proposes practical guidelines for attention module construction tailored to data regime size: cascaded channel–multi-scale spatial attention excels in small-sample tasks; learnable parallel fusion achieves optimal results at medium scales; and large-scale scenarios benefit from parallel structures augmented with dynamic gating. Additionally, the work validates the advantage of spatial-before-channel ordering for fine-grained classification and demonstrates the efficacy of residual connections in mitigating gradient vanishing.
📝 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.