PNL: Efficient long-range dependencies extraction with pyramid non-local module for action recognition.

Long-range spatiotemporal dependencies capturing plays an essential role in improving video features for action recognition. The previously introduced non-local block, inspired by the non-local means, is designed to address this challenge and have shown excellent performance. However, the non-local block brings significant increase in computation cost to the original network. It also lacks the ability to model regional correlations in videos. To address the above limitations, we propose the Pyramid Non-Local (PNL) module, which extends the original non-local block by incorporating regional correlations at multiple scales through a pyramid structured module. This extension upscales the effectiveness of the original non-local block by attending to the interaction between different regions. Empirical results prove the effectiveness and efficiency of our PNL module, which achieves state-of-the-art performance of 83.09 % on the Mini-Kinetics dataset, with decreased computational cost compared to the non-local block. [ABSTRACT FROM AUTHOR]