**Temporally Consistent Spatial Augmentation** is a video data augmentation technique used for contrastive learning in the [Contrastive Video Representation Learning](https://paperswithcode.com/method/cvrl) framework. It fixes the randomness of spatial augmentation across frames; this prevents spatial augmentation hurting learning if applied independently across frames, because in that case it breaks the natural motion. In contrast, having temporally consistent spatial augmentation does not break the natural motion in the frames.

**RotatE** is a method for generating graph embeddings which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. The RotatE model is trained using a [self-adversarial negative sampling](https://paperswithcode.com/method/self-adversarial-negative-sampling) technique.

RotatE

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Temporally Consistent Spatial Augmentation

Spatiotemporal Contrastive Video Representation Learning

**CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.

Source	Spatiotemporal Contrastive Video Representation Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com