B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action Recognition.

Human Action Recognition plays a driving engine of many human-computer interaction applications. Most current researches focus on improving the model generalization by integrating multiple homogeneous modalities, including RGB images, human poses, and optical flows. Furthermore, contextual interactions and out-of-context sign languages have been validated to depend on scene category and human per se.

Markerless Rat Behavior Quantification With Cascade Neural Network.

Quantifying rat behavior through video surveillance is crucial for medicine, neuroscience, and other fields. In this paper, we focus on the challenging problem of estimating landmark points, such as the rat's eyes and joints, only with image processing and quantify the motion behavior of the rat. Firstly, we placed the rat on a special running machine and used a high frame rate camera to capture its motion.