Multiscale Attention via Wavelet Neural Operators for Vision Transformers
This addresses the computational bottleneck in vision transformers for long sequences in high-resolution vision, offering a more efficient alternative to existing attention mechanisms.
The paper tackles the quadratic complexity of standard self-attention in vision transformers for high-resolution images by introducing Multiscale Wavelet Attention (MWA), which achieves linear complexity and shows significant improvement over Fourier-based methods like AFNO and GFN in classification tasks on CIFAR and Tiny-ImageNet.
Transformers have achieved widespread success in computer vision. At their heart, there is a Self-Attention (SA) mechanism, an inductive bias that associates each token in the input with every other token through a weighted basis. The standard SA mechanism has quadratic complexity with the sequence length, which impedes its utility to long sequences appearing in high resolution vision. Recently, inspired by operator learning for PDEs, Adaptive Fourier Neural Operators (AFNO) were introduced for high resolution attention based on global convolution that is efficiently implemented via FFT. However, the AFNO global filtering cannot well represent small and moderate scale structures that commonly appear in natural images. To leverage the coarse-to-fine scale structures we introduce a Multiscale Wavelet Attention (MWA) by leveraging wavelet neural operators which incurs linear complexity in the sequence size. We replace the attention in ViT with MWA and our experiments with CIFAR and Tiny-ImageNet classification demonstrate significant improvement over alternative Fourier-based attentions such as AFNO and Global Filter Network (GFN).