3D ResNet-RS
3D ResNet-RS
**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](https://paperswithcode.com/method/resnet-d) stem is adapted to 3D inputs by using three consecutive [3D convolutional layers](https://paperswithcode.com/method/3d-convolution). 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](https://paperswithcode.com/method/squeeze-and-excitation-block) is adapted to spatio-temporal inputs by using a 3D [global average pooling](https://paperswithcode.com/method/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.
Revisiting 3D ResNets for Video Recognition
https://paperswithcode.com/paper/revisiting-3d-resnets-for-video-recognition
Revisiting 3D ResNets for Video Recognition