1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchSteve Linhttps://www.microsoft.com/en-us/research/people/stevelin/Video Swin Transformer - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="3GJg0N2roZ"><a href="https://www.microsoft.com/en-us/research/publication/video-swin-transformer/">Video Swin Transformer</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/video-swin-transformer/embed/#?secret=3GJg0N2roZ" width="600" height="338" title="“Video Swin Transformer” — Microsoft Research" data-secret="3GJg0N2roZ" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built on Transformer layers that globally connect patches across the spatial and temporal dimensions. In this paper, we instead advocate an inductive bias of […]