  Visual long-range interaction refers to modeling dependencies between distant
feature points or blocks within an image, which can significantly enhance the
model's robustness. Both CNN and Transformer can establish long-range
interactions through layering and patch calculations. However, the underlying
mechanism of long-range interaction in visual space remains unclear. We propose
the mode-locking theory as the underlying mechanism, which constrains the phase
and wavelength relationship between waves to achieve mode-locked interference
waveform. We verify this theory through simulation experiments and demonstrate
the mode-locking pattern in real-world scene models. Our proposed theory of
long-range interaction provides a comprehensive understanding of the mechanism
behind this phenomenon in artificial neural networks. This theory can inspire
the integration of the mode-locking pattern into models to enhance their
robustness.
