  The nonlocal-based blocks are designed for capturing long-range
spatial-temporal dependencies in computer vision tasks. Although having shown
excellent performance, they still lack the mechanism to encode the rich,
structured information among elements in an image or video. In this paper, to
theoretically analyze the property of these nonlocal-based blocks, we provide a
new perspective to interpret them, where we view them as a set of graph filters
generated on a fully-connected graph. Specifically, when choosing the Chebyshev
graph filter, a unified formulation can be derived for explaining and analyzing
the existing nonlocal-based blocks (e.g., nonlocal block, nonlocal stage,
double attention block). Furthermore, by concerning the property of spectral,
we propose an efficient and robust spectral nonlocal block, which can be more
robust and flexible to catch long-range dependencies when inserted into deep
neural networks than the existing nonlocal blocks. Experimental results
demonstrate the clear-cut improvements and practical applicabilities of our
method on image classification, action recognition, semantic segmentation, and
person re-identification tasks.
