Attentive Convolution: Unifying the Expressivity of Self-Attention with Convolutional Efficiency

TL;DR

This paper introduces Attentive Convolution (ATConv), a novel convolutional operator inspired by self-attention principles, achieving high efficiency and performance in vision tasks by integrating adaptive routing and lateral inhibition.

Contribution

The paper proposes ATConv, a new convolutional operator that incorporates self-attention principles, improving efficiency and accuracy over traditional convolutions and existing self-attention methods.

Findings

ATConv outperforms various self-attention mechanisms with only 3x3 kernels.

AttNet achieves 84.4% ImageNet-1K Top-1 accuracy with 27M parameters.

Replacing self-attention with ATConv reduces FID in diffusion models.

Abstract

Self-attention (SA) has become the cornerstone of modern vision backbones for its powerful expressivity over traditional Convolutions (Conv). However, its quadratic complexity remains a critical bottleneck for practical applications. Given that Conv offers linear complexity and strong visual priors, continuing efforts have been made to promote the renaissance of Conv. However, a persistent performance chasm remains, highlighting that these modernizations have not yet captured the intrinsic expressivity that defines SA. In this paper, we re-examine the design of the CNNs, directed by a key question: what principles give SA its edge over Conv? As a result, we reveal two fundamental insights that challenge the long-standing design intuitions in prior research (e.g., Receptive field). The two findings are: (1) \textit{Adaptive routing}: SA dynamically regulates positional information flow according to semantic content, whereas Conv employs static kernels uniformly across all positions. (2) \textit{Lateral inhibition}: SA induces score competition among token weighting, effectively suppressing redundancy and sharpening representations, whereas Conv filters lack such inhibitory dynamics and exhibit considerable redundancy. Based on this, we propose \textit{Attentive Convolution} (ATConv), a principled reformulation of the convolutional operator that intrinsically injects these principles. Interestingly, with only $3\times3$ kernels, ATConv consistently outperforms various SA mechanisms in fundamental vision tasks. Building on ATConv, we introduce AttNet, a CNN family that can attain \textbf{84.4\%} ImageNet-1K Top-1 accuracy with only 27M parameters. In diffusion-based image generation, replacing all SA with the proposed $3\times 3$ ATConv in SiT-XL/2 reduces ImageNet FID by 0.15 in 400k steps with faster sampling. Code is available at: github.com/price112/Attentive-Convolution.