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Computer Science > Computer Vision and Pattern Recognition

arXiv:2206.00272 (cs)
[Submitted on 1 Jun 2022]

Title:Vision GNN: An Image is Worth Graph of Nodes

Authors:Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, Enhua Wu
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    Abstract: Network architecture plays a key role in the deep
    learning-based computer vision system. The widely-used
    convolutional neural network and transformer treat the image as a
    grid or sequence structure, which is not flexible to capture
    irregular and complex objects. In this paper, we propose to
    represent the image as a graph structure and introduce a new
    Vision GNN (ViG) architecture to extract graph-level feature for
    visual tasks. We first split the image to a number of patches
    which are viewed as nodes, and construct a graph by connecting
    the nearest neighbors. Based on the graph representation of
    images, we build our ViG model to transform and exchange
    information among all the nodes. ViG consists of two basic
    modules: Grapher module with graph convolution for aggregating
    and updating graph information, and FFN module with two linear
    layers for node feature transformation. Both isotropic and
    pyramid architectures of ViG are built with different model
    sizes. Extensive experiments on image recognition and object
    detection tasks demonstrate the superiority of our ViG
    architecture. We hope this pioneering study of GNN on general
    visual tasks will provide useful inspiration and experience for
    future research. The PyTroch code will be available at this https
    URL and the MindSpore code will be avaiable at this https URL.

Comments: tech report
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2206.00272 [cs.CV]
          (or arXiv:2206.00272v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2206.00272
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Kai Han [view email]
[v1] Wed, 1 Jun 2022 07:01:04 UTC (5,884 KB)
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