  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.
