Beyond Fixation: Dynamic Window Visual Transformer

TL;DR

This paper introduces DW-ViT, a dynamic multi-scale window visual transformer that enhances model performance by adaptively adjusting window sizes for better multi-scale information modeling, outperforming fixed-window approaches.

Contribution

The paper presents the first use of dynamic multi-scale windows in visual transformers, significantly improving performance over fixed-window models like Swin Transformer.

Findings

DW-ViT outperforms state-of-the-art methods on ImageNet-1K, ADE20K, and COCO.

It achieves consistent improvements with similar parameters and computational costs.

DW-ViT is scalable and easily integrable into existing window-based transformers.

Abstract

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. However, 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 knowledge, 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 conducted a detailed performance evaluation on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with related state-of-the-art (SoTA) methods, DW-ViT obtains the best performance. Specifically, compared with the current SoTA Swin Transformers \cite{liu2021swin}, DW-ViT has achieved consistent 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.