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What is: 3D ResNet-RS?

SourceRevisiting 3D ResNets for Video Recognition
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

3D ResNet-RS is an architecture and scaling strategy for 3D ResNets for video recognition. The key additions are:

  • 3D ResNet-D stem: The ResNet-D stem is adapted to 3D inputs by using three consecutive 3D convolutional layers. The first convolutional layer employs a temporal kernel size of 5 while the remaining two convolutional layers employ a temporal kernel size of 1.

  • 3D Squeeze-and-Excitation: Squeeze-and-Excite is adapted to spatio-temporal inputs by using a 3D global average pooling operation for the squeeze operation. A SE ratio of 0.25 is applied in each 3D bottleneck block for all experiments.

  • Self-gating: A self-gating module is used in each 3D bottleneck block after the SE module.