A R(2+1)D convolutional neural network is a network for action recognition that employs R(2+1)D convolutions in a ResNet inspired architecture. The use of these convolutions over regular 3D Convolutions reduces computational complexity, prevents overfitting, and introduces more non-linearities that allow for a better functional relationship to be modeled.
Source: A Closer Look at Spatiotemporal Convolutions for Action RecognitionPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Action Recognition | 8 | 21.62% |
Retrieval | 4 | 10.81% |
Video Retrieval | 4 | 10.81% |
Temporal Action Localization | 3 | 8.11% |
Optical Flow Estimation | 2 | 5.41% |
Self-Supervised Action Recognition | 2 | 5.41% |
Self-Supervised Learning | 2 | 5.41% |
Video Recognition | 2 | 5.41% |
Action Classification | 2 | 5.41% |