Nonlocal neural networks [25] have been proposed and shown to be effective in
several computer vision tasks, where the nonlocal operations can directly capture
long-range dependencies in the feature space. In this paper, we study the nature of
diffusion and damping effect of nonlocal networks by doing spectrum analysis on
the weight matrices of the well-trained networks, and then propose a new formulation of the nonlocal block. The new block not only learns the nonlocal interactions
but also has stable dynamics, thus allowing deeper nonlocal structures. Moreover,
we interpret our formulation from the general nonlocal modeling perspective, where
we make connections between the proposed nonlocal network and other nonlocal
models, such as nonlocal diffusion process and Markov jump process.
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A Review of Local-to-Nonlocal Coupling Methods in Nonlocal Diffusion and Nonlocal Mechanics