MaxViT: Multi-Axis Vision Transformer (ECCV 2022) - GitHub
Jul 21, 2022 · MaxViT is a family of hybrid (CNN + ViT) image classification models, that achieves better performances across the board for both parameter and FLOPs efficiency than both SoTA ConvNets and Transformers. They can also scale well on large dataset sizes like ImageNet-21K.
[2204.01697] MaxViT: Multi-Axis Vision Transformer - arXiv.org
Apr 4, 2022 · We also present a new architectural element by effectively blending our proposed attention model with convolutions, and accordingly propose a simple hierarchical vision backbone, dubbed MaxViT, by simply repeating the basic building block over multiple stages.
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exibility and scalability of Max-SA, we are able to build a novel vision backbone, which we call MaxViT, by simply stacking alternative layers of Max-SA with MBConv [35] in a hierarchical architecture, as shown in Figure2.
MaxVit — Torchvision main documentation
MaxVit — Torchvision main documentation. The MaxVit transformer models are based on the MaxViT: Multi-Axis Vision Transformer paper. Model builders. The following model builders can be used to instantiate an MaxVit model with and without pre-trained weights. All the model builders internally rely on the torchvision.models.maxvit.MaxVit base class.
[2204.01697] MaxViT: Multi-Axis Vision Transformer - ar5iv
In this paper, we present a new type of Transformer module, called multi-axis self-attention (Max-SA), that capably serves as a basic architecture component which can perform both local and global spatial interactions in a single block.
MaxViT: Multi-axis Vision Transformer | SpringerLink
A generic strong Transformer backbone, MaxViT, that can capture both local and global spatial interactions throughout every stage of the network. A novel stand-alone multi-axis attention module composed of blocked local and dilated global attention, enjoying global perception in linear complexity.
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