# Region-based Non-local Operation for Video Classification

arXiv: https://arxiv.org/abs/2007.09033 · HTML: https://mlsanity.com/paper/2007.09033/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Group Contextualization for Video Recognition](https://mlsanity.com/paper/2203.09694/) (2022)
- [Attention in Attention: Modeling Context Correlation for Efficient Video Classification](https://mlsanity.com/paper/2204.09303/) (2022)
- [Busy-Quiet Video Disentangling for Video Classification](https://mlsanity.com/paper/2103.15584/) (2021)
- [Two-Stream temporal transformer for video action classification](https://mlsanity.com/paper/2601.14086/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) model long-range dependencies by deeply stacking convolution operations with small window sizes, which makes the optimizations difficult. This paper presents region-based non-local (RNL) operations as a family of self-attention mechanisms, which can directly capture long-range dependencies without using a deep stack of local operations. Given an intermediate feature map, our method recalibrates the feature at a position by aggregating the information from the neighboring regions of all positions. By combining a channel attention module with the proposed RNL, we design an attention chain, which can be integrated into the off-the-shelf CNNs for end-to-end training. We evaluate our method on two video classification benchmarks. The experimental results of our method outperform other attention mechanisms, and we achieve state-of-the-art performance on the Something-Something V1 dataset.