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.
@article{arxiv.2303.05695,
title = {Mode-locking Theory for Long-Range Interaction in Artificial Neural Networks},
author = {Xiuxiu Bai and Shuaishuai Zhao and Yao Gao and Zhe Liu},
journal= {arXiv preprint arXiv:2303.05695},
year = {2023}
}