Papers › Gate-Shift Networks for Video Action Recognition

Gate-Shift Networks for Video Action Recognition

1 Dec 2019CVPR 2020 6arXiv:1912.00381archive 2025-07-28

Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz

Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity.

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Tasks

Action Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Recognition Something-Something V1 GSM Ensemble InceptionV3 (ImageNet pretrained) Top 1 Accuracy 55.16 #27 of 74 Archive leaderboard report
Action Recognition Something-Something V1 GSM InceptionV3 (16 frames, ImageNet pretrained) Top 1 Accuracy 51.68 #47 of 74 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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