ConvMLP: Hierarchical Convolutional MLPs for Vision

• URL: http://arxiv.org/abs/2109.04454v1
• Date: Thu, 9 Sep 2021 17:52:57 GMT
• Title: ConvMLP: Hierarchical Convolutional MLPs for Vision
• Authors: Jiachen Li, Ali Hassani, Steven Walton and Humphrey Shi
• Abstract summary: 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.
• Score: 7.874749885641495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods. However, most adopt spatial MLPs which take fixed dimension inputs, therefore making it difficult to apply them to downstream tasks, such as object detection and semantic segmentation. Moreover, single-stage designs further limit performance in other computer vision tasks and fully connected layers bear heavy computation. To tackle these problems, we propose ConvMLP: a hierarchical Convolutional MLP for visual recognition, which is a light-weight, stage-wise, co-design of convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1 accuracy on ImageNet-1k with 9M parameters and 2.4G MACs (15% and 19% of MLP-Mixer-B/16, respectively). Experiments on object detection and semantic segmentation further show that visual representation learned by ConvMLP can be seamlessly transferred and achieve competitive results with fewer parameters. Our code and pre-trained models are publicly available at https://github.com/SHI-Labs/Convolutional-MLPs.

Related papers

• BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons [37.28828605119602]
  This paper studies the problem of designing compact binary architectures for vision multi-layer perceptrons (MLPs) We find that previous binarization methods perform poorly due to limited capacity of binary samplings. We propose to improve the performance of binary mixing and channel mixing (BiMLP) model by enriching the representation ability of binary FC layers.
  arXiv  Detail & Related papers  (2022-12-29T02:43:41Z)
• MDMLP: Image Classification from Scratch on Small Datasets with MLP [7.672827879118106]
  Recently, the attention mechanism has become a go-to technique for natural language processing and computer vision tasks. Recently, theMixer and other-based architectures, based simply on multi-layer perceptrons (MLPs), are also powerful compared to CNNs and attention techniques.
  arXiv  Detail & Related papers  (2022-05-28T16:26:59Z)
• Efficient Language Modeling with Sparse all-MLP [53.81435968051093]
  All-MLPs can match Transformers in language modeling, but still lag behind in downstream tasks. We propose sparse all-MLPs with mixture-of-experts (MoEs) in both feature and input (tokens) We evaluate its zero-shot in-context learning performance on six downstream tasks, and find that it surpasses Transformer-based MoEs and dense Transformers.
  arXiv  Detail & Related papers  (2022-03-14T04:32:19Z)
• Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs [84.3235981545673]
  Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks. We present Mix-Shift-MLP which makes the size of the local receptive field used for mixing increase with respect to the amount of spatial shifting. MS-MLP achieves competitive performance in multiple vision benchmarks.
  arXiv  Detail & Related papers  (2022-02-14T06:53:48Z)
• 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)
• 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)
• CycleMLP: A MLP-like Architecture for Dense Prediction [26.74203747156439]
  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.
  arXiv  Detail & Related papers  (2021-07-21T17:23:06Z)
• 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.