A **R(2+1)D** convolutional neural network is a network for action recognition that employs [R(2+1)D](https://paperswithcode.com/method/2-1-d-convolution) convolutions in a [ResNet](https://paperswithcode.com/method/resnet) inspired architecture. The use of these convolutions over regular [3D Convolutions](https://paperswithcode.com/method/3d-convolution) reduces computational complexity, prevents overfitting, and introduces more non-linearities that allow for a better functional relationship to be modeled.

**Point-GNN** is a graph neural network for detecting objects from a LiDAR point cloud. It predicts the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, there is an auto-registration mechanism to reduce translation variance, as well as a box merging and scoring operation to combine detections from multiple vertices accurately.

Point-GNN

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

R(2+1)D

A Closer Look at Spatiotemporal Convolutions for Action Recognition

The **NVAE Encoder Residual Cell** is a [residual connection](https://paperswithcode.com/method/residual-connection) block used in the [NVAE](https://paperswithcode.com/method/nvae) architecture for the encoder. It applies two series of BN-[Swish](https://paperswithcode.com/method/swish)-Conv layers without changing the number of channels.

Source	A Closer Look at Spatiotemporal Convolutions for Action Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com