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Convolution has been arguably the most important feature transform for modernneural networks, leading to the advance of deep learning. Recent emergence ofTransformer networks, which replace convolution layers with self-attentionblocks, has revealed the limitation of stationary convolution kernels andopened the door to the era of dynamic feature transforms. The existing dynamictransforms, including self-attention, however, are all limited for videounderstanding where correspondence relations in space and time, i.e., motioninformation, are crucial for effective representation. In this work, weintroduce a relational feature transform, dubbed the relational self-attention(RSA), that leverages rich structures of spatio-temporal relations in videos bydynamically generating relational kernels and aggregating relational contexts.Our experiments and ablation studies show that the RSA network substantiallyoutperforms convolution and self-attention counterparts, achieving the state ofthe art on the standard motion-centric benchmarks for video action recognition,such as Something-Something-V1 & V2, Diving48, and FineGym.

Relational Self-Attention: What's Missing in Attention for Video Understanding