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.

**Local Prior Matching** is a semi-supervised objective for speech recognition that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. The LPM objective minimizes the cross entropy between the local prior and the model distribution, and is minimized when $q\_{y\mid{x}} = \bar{p}\_{y\mid{x}}$. Intuitively, LPM encourages the ASR model to assign posterior probabilities proportional to the linguistic probabilities of the proposed hypotheses.

Semi-Supervised Speech Recognition via Local Prior Matching

Spatial & Temporal Attention

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

**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.

Source	An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com