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Register a new userVision Permutator: A Permutable MLP-Like Architecture for Visual Recognition
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Added by
Qibin Hou
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In this paper, we present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections. This allows Vision Permutator to capture long-range dependencies along one spatial direction and meanwhile preserve precise positional information along the other direction. The resulting position-sensitive outputs are then aggregated in a mutually complementing manner to form expressive representations of the objects of interest. We show that our Vision Permutators are formidable competitors to convolutional neural networks (CNNs) and vision transformers. Without the dependence on spatial convolutions or attention mechanisms, Vision Permutator achieves 81.5% top-1 accuracy on ImageNet without extra large-scale training data (e.g., ImageNet-22k) using only 25M learnable parameters, which is much better than most CNNs and vision transformers under the same model size constraint. When scaling up to 88M, it attains 83.2% top-1 accuracy. We hope this work could encourage research on rethinking the way of encoding spatial information and facilitate the development of MLP-like models. Code is available at https://github.com/Andrew-Qibin/VisionPermutator.
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Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary. We present MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs). MLP-Mixer contains two types of layers: one with MLPs applied independently to image patches (i.e. mixing the per-location features), and one with MLPs applied across patches (i.e. mixing spatial information). When trained on large datasets, or with modern regularization schemes, MLP-Mixer attains competitive scores on image classification benchmarks, with pre-training and inference cost comparable to state-of-the-art models. We hope that these results spark further research beyond the realms of well established CNNs and Transformers.
An Axial Shifted MLP architecture (AS-MLP) is proposed in this paper. Different from MLP-Mixer, where the global spatial feature is encoded for the information flow through matrix transposition and one token-mixing MLP, we pay more attention to the local features communication. 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, etc, just like designing those of convolution kernels. 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 (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 (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. Code is available at https://github.com/svip-lab/AS-MLP.
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}.
Visual recognition has been dominated by convolutional neural networks (CNNs) for years. Though recently the prevailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification, their performance is still inferior to that of the latest SOTA CNNs if no extra data are provided. In this work, we try to close the performance gap and demonstrate that attention-based models are indeed able to outperform CNNs. We find a major factor limiting the performance of ViTs for ImageNet classification is their low efficacy in encoding fine-level features into the token representations. To resolve this, we introduce a novel outlook attention and present a simple and general architecture, termed Vision Outlooker (VOLO). Unlike self-attention that focuses on global dependency modeling at a coarse level, the outlook attention efficiently encodes finer-level features and contexts into tokens, which is shown to be critically beneficial to recognition performance but largely ignored by the self-attention. Experiments show that our VOLO achieves 87.1% top-1 accuracy on ImageNet-1K classification, which is the first model exceeding 87% accuracy on this competitive benchmark, without using any extra training data In addition, the pre-trained VOLO transfers well to downstream tasks, such as semantic segmentation. We achieve 84.3% mIoU score on the cityscapes validation set and 54.3% on the ADE20K validation set. Code is available at url{https://github.com/sail-sg/volo}.
This paper presents Hire-MLP, a simple yet competitive vision MLP architecture via hierarchical rearrangement. Previous vision MLPs like MLP-Mixer are not flexible for various image sizes and are inefficient to capture spatial information by flattening the tokens. Hire-MLP innovates the existing MLP-based models by proposing the idea of hierarchical rearrangement to aggregate the local and global spatial information while being versatile for downstream tasks. Specifically, the inner-region rearrangement is designed to capture local information inside a spatial region. Moreover, to enable information communication between different regions and capture global context, the cross-region rearrangement is proposed to circularly shift all tokens along spatial directions. The proposed Hire-MLP architecture is built with simple channel-mixing MLPs and rearrangement operations, thus enjoys high flexibility and inference speed. Experiments show that our Hire-MLP achieves state-of-the-art performance on the ImageNet-1K benchmark. In particular, Hire-MLP achieves an 83.4% top-1 accuracy on ImageNet, which surpasses previous Transformer-based and MLP-based models with better trade-off for accuracy and throughput.
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