用分段函数与小波变换增强ViT，提升图像识别精度与效率
Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer
- 用样条函数替代MLP，结合小波变换实现多尺度特征提取
- 在ImageNet、COCO、ADE20K上均达顶尖性能，检测与分割效果显著
- 适合关注模型轻量化与多尺度建模的研究者与工程师
本研究针对视觉Transformer（ViT）中多层感知机（MLP）的固有局限，提出混合柯尔莫戈洛夫-阿诺德网络增强型ViT（Hyb-KAN ViT）框架。该框架融合基于小波的谱分解与样条优化激活函数，突破以往研究对ViT架构预设模块化结构及小波函数边缘检测能力忽视的问题。提出两个核心模块：高效KAN（Eff-KAN）以样条函数取代MLP层；小波KAN（Wav-KAN）利用正交小波变换实现多分辨率特征提取。两者系统集成于ViT编码器层与分类头中，强化空间-频率建模并缓解计算瓶颈。在ImageNet-1K（图像识别）、COCO（目标检测与实例分割）和ADE20K（语义分割）上的实验表明，Hyb-KAN ViT达到当前最优性能。消融实验验证了小波驱动的谱先验在分割任务中的有效性，以及样条基效率在检测任务中的优势。该框架为视觉架构中参数效率与多尺度表征的平衡提供了新范式。
原文摘要 · Abstract (English)
This study addresses the inherent limitations of Multi-Layer Perceptrons (MLPs) in Vision Transformers (ViTs) by introducing Hybrid Kolmogorov-Arnold Network (KAN)-ViT (Hyb-KAN ViT), a novel framework that integrates wavelet-based spectral decomposition and spline-optimized activation functions, prior work has failed to focus on the prebuilt modularity of the ViT architecture and integration of edge detection capabilities of Wavelet functions. We propose two key modules: Efficient-KAN (Eff-KAN), which replaces MLP layers with spline functions and Wavelet-KAN (Wav-KAN), leveraging orthogonal wavelet transforms for multi-resolution feature extraction. These modules are systematically integrated in ViT encoder layers and classification heads to enhance spatial-frequency modeling while mitigating computational bottlenecks. Experiments on ImageNet-1K (Image Recognition), COCO (Object Detection and Instance Segmentation), and ADE20K (Semantic Segmentation) demonstrate state-of-the-art performance with Hyb-KAN ViT. Ablation studies validate the efficacy of wavelet-driven spectral priors in segmentation and spline-based efficiency in detection tasks. The framework establishes a new paradigm for balancing parameter efficiency and multi-scale representation in vision architectures.
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