English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Very recently, a variety of vision transformer architectures for denseprediction tasks have been proposed and they show that the design of spatialattention is critical to their success in these tasks. In this work, we revisitthe design of the spatial attention and demonstrate that a carefully-devisedyet simple spatial attention mechanism performs favourably against thestate-of-the-art schemes. As a result, we propose two vision transformerarchitectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architecturesare highly-efficient and easy to implement, only involving matrixmultiplications that are highly optimized in modern deep learning frameworks.More importantly, the proposed architectures achieve excellent performance on awide range of visual tasks, including image level classification as well asdense detection and segmentation. The simplicity and strong performance suggestthat our proposed architectures may serve as stronger backbones for many visiontasks. Our code is released at https://github.com/Meituan-AutoML/Twins .

Twins: Revisiting the Design of Spatial Attention in Vision Transformers