CycleMLP: A MLP-like Architecture for Dense Prediction

• URL: http://arxiv.org/abs/2107.10224v1
• Date: Wed, 21 Jul 2021 17:23:06 GMT
• Title: CycleMLP: A MLP-like Architecture for Dense Prediction
• Authors: Shoufa Chen, Enze Xie, Chongjian Ge, Ding Liang, Ping Luo
• Abstract summary: CycleMLP is a versatile backbone for visual recognition and dense predictions. It can cope with various image sizes and achieves linear computational complexity to image size by using local windows. CycleMLP aims to provide a competitive baseline on object detection, instance segmentation, and semantic segmentation for models.
• Score: 26.74203747156439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions, unlike modern MLP architectures, e.g., MLP-Mixer, ResMLP, and gMLP, whose architectures are correlated to image size and thus are infeasible in object detection and segmentation. CycleMLP has two advantages compared to modern approaches. (1) It can cope with various image sizes. (2) It achieves linear computational complexity to image size by using local windows. In contrast, previous MLPs have quadratic computations because of their fully spatial connections. We build a family of models that surpass existing MLPs and achieve a comparable accuracy (83.2%) on ImageNet-1K classification compared to the state-of-the-art Transformer such as Swin Transformer (83.3%) but using fewer parameters and FLOPs. We expand the MLP-like models' applicability, making them a versatile backbone for dense prediction tasks. CycleMLP aims to provide a competitive baseline on object detection, instance segmentation, and semantic segmentation for MLP models. In particular, CycleMLP achieves 45.1 mIoU on ADE20K val, comparable to Swin (45.2 mIOU). Code is available at \url{https://github.com/ShoufaChen/CycleMLP}.

Related papers

• R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition [33.53114929452528]
  Vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community. We present an achieves a view-based 3D object recognition task by considering the communications between patches from different views. With a conceptually simple structure, our R$2$MLP achieves competitive performance compared with existing methods.
  arXiv  Detail & Related papers  (2022-11-20T21:13:02Z)
• GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimation [68.65764751482774]
  GraphMLP is a global-local-graphical unified architecture for 3D human pose estimation. It incorporates the graph structure of human bodies into a model to meet the domain-specific demand of the 3D human pose. It can be extended to model complex temporal dynamics in a simple way with negligible computational cost gains in the sequence length.
  arXiv  Detail & Related papers  (2022-06-13T18:59:31Z)
• MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing [123.43419144051703]
  We present a novel-like 3D architecture for video recognition. The results are comparable to state-of-the-art widely-used 3D CNNs and video.
  arXiv  Detail & Related papers  (2022-06-13T16:21:33Z)
• RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality [113.1414517605892]
  We propose a methodology, Locality Injection, to incorporate local priors into an FC layer. RepMLPNet is the first that seamlessly transfer to Cityscapes semantic segmentation.
  arXiv  Detail & Related papers  (2021-12-21T10:28:17Z)
• Sparse MLP for Image Recognition: Is Self-Attention Really Necessary? [65.37917850059017]
  We build an attention-free network called sMLPNet. For 2D image tokens, sMLP applies 1D along the axial directions and the parameters are shared among rows or columns. When scaling up to 66M parameters, sMLPNet achieves 83.4% top-1 accuracy, which is on par with the state-of-the-art Swin Transformer.
  arXiv  Detail & Related papers  (2021-09-12T04:05:15Z)
• ConvMLP: Hierarchical Convolutional MLPs for Vision [7.874749885641495]
  We propose a hierarchical ConMLP: a light-weight, stage-wise, co-design for visual recognition. We show that ConvMLP can be seamlessly transferred and achieve competitive results with fewer parameters.
  arXiv  Detail & Related papers  (2021-09-09T17:52:57Z)
• Sparse-MLP: A Fully-MLP Architecture with Conditional Computation [7.901786481399378]
  Mixture-of-Experts (MoE) with sparse conditional computation has been proved an effective architecture for scaling attention-based models to more parameters with comparable computation cost. We propose Sparse-MLP, scaling the recent-Mixer model with MoE, to achieve a more-efficient architecture.
  arXiv  Detail & Related papers  (2021-09-05T06:43:08Z)
• AS-MLP: An Axial Shifted MLP Architecture for Vision [50.11765148947432]
  An Axial Shifted architecture (AS-MLP) is proposed in this paper. By axially shifting channels of the feature map, AS-MLP is able to obtain the information flow from different directions. 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.
  arXiv  Detail & Related papers  (2021-07-18T08:56:34Z)
• S$^2$-MLP: Spatial-Shift MLP Architecture for Vision [34.47616917228978]
  Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation. In this paper, we propose a novel pure architecture, spatial-shift (S$2$-MLP)
  arXiv  Detail & Related papers  (2021-06-14T15:05:11Z)
• MLP-Mixer: An all-MLP Architecture for Vision [93.16118698071993]
  We present-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs). Mixer attains competitive scores on image classification benchmarks, with pre-training and inference comparable to state-of-the-art models.
  arXiv  Detail & Related papers  (2021-05-04T16:17:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.