Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Most existing action quality assessment methods rely on the deep features of an entire video
to predict the score, which is less reliable due to the non-transparent inference process and …

To understand human behaviors, action recognition based on videos is a common
approach. Compared with image-based action recognition, videos provide much more …

Few-shot action recognition aims to recognize novel action classes using only a small
number of labeled training samples. In this work, we propose a novel approach that first …

The availability of vast video collections and the accuracy of ML models has generated
significant interest in video analytics systems. Since naively processing all frames using …

We introduce the task of spotting temporally precise, fine-grained events in video (detecting
the precise moment in time events occur). Precise spotting requires models to reason …

Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory
contains information not only about the winning and losing of each point, but also how it was …

Few-shot action recognition aims to address the high cost and impracticality of manually
labeling complex and variable video data in action recognition. It requires accurately …

Deep learning has the potential to revolutionize sports performance, with applications
ranging from perception and comprehension to decision. This paper presents a …

Few-shot action recognition aims to recognize few-labeled novel action classes and attracts
growing attentions due to practical significance. Human skeletons provide explainable and …