AS-MLP: An Axial Shifted MLP Architecture for VisionDownload PDF

29 Sept 2021, 00:30 (edited 16 Mar 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: Architecture Design, MLP, Classification, Detection, Segmentation
  • Abstract: An Axial Shifted MLP architecture (AS-MLP) is proposed in this paper. Different from MLP-Mixer, where the global spatial feature is encoded for information flow through matrix transposition and one token-mixing MLP, we pay more attention to the local features interaction. By axially shifting channels of the feature map, AS-MLP is able to obtain the information flow from different axial directions, which captures the local dependencies. Such an operation enables us to utilize a pure MLP architecture to achieve the same local receptive field as CNN-like architecture. We can also design the receptive field size and dilation of blocks of AS-MLP, \emph{etc}, in the same spirit of convolutional neural networks. With the proposed AS-MLP architecture, our model obtains 83.3\% Top-1 accuracy with 88M parameters and 15.2 GFLOPs on the ImageNet-1K dataset. Such a simple yet effective architecture outperforms all MLP-based architectures and achieves competitive performance compared to the transformer-based architectures (\emph{e.g.}, Swin Transformer) even with slightly lower FLOPs. In addition, AS-MLP is also the first MLP-based architecture to be applied to the downstream tasks (\emph{e.g.}, object detection and semantic segmentation). The experimental results are also impressive. Our proposed AS-MLP obtains 51.5 mAP on the COCO validation set and 49.5 MS mIoU on the ADE20K dataset, which is competitive compared to the transformer-based architectures. Our AS-MLP establishes a strong baseline of MLP-based architecture. Code is available at \url{https://github.com/svip-lab/AS-MLP}.
  • One-sentence Summary: We design the first MLP-based architecture for downstream tasks. It achieves competitive performance compared to the transformer-based architecture, which establishes a new strong baseline of MLP-based architecture.
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