Beyond Fixation: Dynamic Window Visual Transformer

Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a fixed single-scale window for modeling by default, ignoring the impact of window size on model performance. How-ever, this may limit the modeling potential of these window-based models for multi-scale information. In this paper, we propose a novel method, named Dynamic Window Vision Transformer (DW-ViT). The dynamic window strategy proposed by DW- ViT goes beyond the model that employs a fixed single window setting. To the best of our knowl-edge, we are the first to use dynamic multi-scale windows to explore the upper limit of the effect of window settings on model performance. In DW- ViT, multi-scale information is obtained by assigning windows of different sizes to different head groups of window multi-head self-attention. Then, the information is dynamically fused by assigning different weights to the multi-scale window branches. We con-ducted a detailed performance evaluation on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with re-lated state-of-the-art (SoTA) methods, DW- ViT obtains the best performance. Specifically, compared with the current SoTA Swin Transformers [31], DW-ViT has achieved con-sistent and substantial improvements on all three datasets with similar parameters and computational costs. In addition, DW-ViT exhibits good scalability and can be easily inserted into any window-based visual transformers. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code release: https://github.com/pzhren/DW-ViT. This work was done when the first author interned at Dark Matter AI..