Web: http://arxiv.org/abs/2209.09760

Sept. 21, 2022, 1:13 a.m. | Li Zhang, Mohan Chen, Anurag Arnab, Xiangyang Xue, Philip H.S. Torr

cs.CV updates on arXiv.org arxiv.org

Modelling long-range dependencies is critical for scene understanding tasks
in computer vision. Although convolution neural networks (CNNs) have excelled
in many vision tasks, they are still limited in capturing long-range structured
relationships as they typically consist of layers of local kernels. A
fully-connected graph, such as the self-attention operation in Transformers, is
beneficial for such modelling, however, its computational overhead is
prohibitive. In this paper, we propose a dynamic graph message passing network,
that significantly reduces the computational complexity compared …

arxiv graph networks

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