Overview of 3D ResNet-RS Architecture and Scaling Strategy for Video Recognition
Video recognition involves the use of deep learning networks to analyze video content and classify them into appropriate categories. One such architecture and scaling strategy used for video recognition is the 3D ResNet-RS.
3D ResNet-RS involves the use of three key additions to the original ResNet-D architecture:
1. 3D ResNet-D Stem
The ResNet-D stem is adapted for 3D inputs in the 3D ResNet-RS architecture by using three consecutive 3D convolutional layers. The first convolutional layer applies a temporal kernel size of 5, while the other two convolutional layers use a temporal kernel size of 1.
This adaptation allows for the efficient processing of 3D video inputs, as the model learns from the spatio-temporal features of the videos through the three convolutional layers.
2. 3D Squeeze-and-Excitation
In the 3D ResNet-RS architecture, the squeeze-and-excitation operation is adapted for spatio-temporal inputs through the use of a 3D global average pooling operation for the squeeze operation.
This helps to ensure that the network learns the most important features from the video data and focuses on them while discarding less important information.
3. Self-gating
The third addition to the 3D ResNet-RS architecture is the use of self-gating modules in each 3D bottleneck block, following the SE module.
The self-gating module enhances the flow of information within the network by selectively gating the input and output of each block.
The combination of these three additions to the original ResNet-D architecture enhances the performance of the network in video recognition tasks and enables it to learn and process features from spatio-temporal data efficiently and accurately.
The 3D ResNet-RS architecture and scaling strategy is an effective approach to video recognition tasks, enabling the efficient processing of complex spatio-temporal data. Its adaptability to 3D inputs, use of the 3D Squeeze-and-Excitation operation, and inclusion of self-gating modules make it a powerful tool in deep learning for video recognition.
