用递归小卷积实现多频特征，参数和计算量几乎不随感受野增大而增长。
RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations
- 通过递归分解小卷积构建多频特征，线性扩展参数与感受野。
- 参数仅增长ℓ+2倍，计算量最多增加5/3倍，远优于传统卷积的指数增长。
- 适合追求高效紧凑模型的视觉任务，尤其在资源受限场景下表现优异。
视觉变换器（ViTs）的进展表明全局建模能力的优势，促使大核卷积被广泛用于扩大有效感受野（ERF）。然而，参数量和计算复杂度（FLOPs）随核大小呈二次增长，带来显著的效率与优化挑战。本文提出RecConv，一种递归分解策略，利用小核卷积高效构建多频表示。RecConv建立参数增长与分解层级ℓ之间的线性关系，使基核大小为k、分解ℓ层时的有效感受野达到k×2^ℓ，同时保持FLOPs恒定。具体而言，参数仅膨胀ℓ+2倍，最大计算量增加5/3倍，相比标准与深度卷积的指数增长（4^ℓ）有巨大优势。RecNeXt-M3在相同FLOPs下比RepViT-M1.1在COCO上提升1.9 AP^{box}。该方法为跨模态高效紧凑网络设计提供了新路径。代码与模型见https://github.com/suous/RecNeXt。
原文摘要 · Abstract (English)
Recent advances in vision transformers (ViTs) have demonstrated the advantage of global modeling capabilities, prompting widespread integration of large-kernel convolutions for enlarging the effective receptive field (ERF). However, the quadratic scaling of parameter count and computational complexity (FLOPs) with respect to kernel size poses significant efficiency and optimization challenges. This paper introduces RecConv, a recursive decomposition strategy that efficiently constructs multi-frequency representations using small-kernel convolutions. RecConv establishes a linear relationship between parameter growth and decomposing levels which determines the effective receptive field $k\times 2^\ell$ for a base kernel $k$ and $\ell$ levels of decomposition, while maintaining constant FLOPs regardless of the ERF expansion. Specifically, RecConv achieves a parameter expansion of only $\ell+2$ times and a maximum FLOPs increase of $5/3$ times, compared to the exponential growth ($4^\ell$) of standard and depthwise convolutions. RecNeXt-M3 outperforms RepViT-M1.1 by 1.9 $AP^{box}$ on COCO with similar FLOPs. This innovation provides a promising avenue towards designing efficient and compact networks across various modalities. Codes and models can be found at https://github.com/suous/RecNeXt.
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