**PP-YOLOv2** is an object detector that extends upon [PP-YOLO](https://www.paperswithcode.com/method/pp-yolo) with several refinements:

- A [Path Aggregation Network](https://paperswithcode.com/method/pafpn) is included for the FPN to compose bottom-up paths.
- [Mish Activation functions](https://paperswithcode.com/method/mish) are used.
- The input size is expanded.
- An IoU aware branch is calculated with a soft label format.

Spatial & temporal attention combines the advantages of spatial attention and temporal attention as it adaptively selects both important regions and key frames. Some works compute temporal attention and spatial attention separately, while others produce joint spatio & temporal attention maps. Further works focusing on capturing pairwise relations.

Spatial & Temporal Attention

An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data

PP-YOLOv2

PP-YOLOv2: A Practical Object Detector

**Energy Based Processes** extend energy based models to exchangeable data while allowing neural network parameterizations of the energy function. They extend the previously separate stochastic process and latent variable model perspectives in a common framework. The result is a generalization of [Gaussian processes](https://paperswithcode.com/method/gaussian-process) and Student-t processes that exploits EBMs for greater flexibility.

Source	PP-YOLOv2: A Practical Object Detector
Year	2000
Data Source	Papers with Code archive (CC BY-SA)