Twins: Revisiting the Design of Spatial Attention in Vision TransformersDownload PDF

21 May 2021, 20:41 (modified: 22 Jan 2022, 10:05)NeurIPS 2021 PosterReaders: Everyone
Keywords: Vision Transformers, image classification
TL;DR: Two simple and effective designs of vision transformer, which is on par with the Swin transformer
Abstract: Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully devised yet simple spatial attention mechanism performs favorably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins- PCPVT and Twins-SVT. Our proposed architectures are highly efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks including image-level classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks.
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Code: https://github.com/Meituan-AutoML/Twins
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