allows us to learn position-specific attention cell designs with zero extra computational cost (see Sec \[sec-differentiable\] for details).

We conduct extensive experiments on two benchmark datasets: Kinetics-600 [@carreira2018short] and Moments in Time (MiT) [@monfort2019moments]. Our discovered attention cells can improve the performance of two backbone networks I3D [@carreira2017quo] and S3D [@xie2018rethinking] by more than 2% on both datasets, and also outperforms non-local blocks – the state-of-the-art manually designed attention cells for videos. Inserting our attention cells into I3D-R50 [@wang2018non] yields state-of-the-art performance on both datasets. Notably, our discovered attention cells can also generalize well across modalities (RGB to optical flow), backbones (e.g., I3D to S3D or I3D to I3D-R50), and datasets (MiT to Kinetics-600 or Kinetics-600 to MiT).

**Contributions:** (1) This is the first attempt to extend NAS beyond discovering convolutional cells to attention cells. (2) We propose a novel search space for spatiotemporal attention cells that use attention operations as the primary building block, which can be seamlessly inserted into existing backbone networks to improve their performance on video classification. (3) We develop a differentiable formulation of the proposed search space, making it possible to learn the attention cell design with back-propagation and learn position-specific attention cell designs with 