Skeleton-based action recognition models have recently been shown to be vulnerable adversarial attacks. Compared attacks on images, perturbations skeletons are typically bounded a lower dimension of approximately 100 per frame. This lower-dimensional setting makes it more difficult generate imperceptible perturbations. Existing resolve this by exploiting the temporal structure skeleton motion s...

In this paper, a novel video classification method is presented that aims to recognize different categories of third-person videos efficiently. Our motivation achieve light model could be trained with insufficient training data. With intuition, the processing 3-dimensional input broken 1D in temporal dimension on top 2D spatial. The processes related spatial frames are being done by utilizing p...

Recent advances in Computer Vision and Machine Learning empowered the use of image positional data several high-level analyses Sports Science, such as player action classification, recognition complex human movements, tactical analysis team sports. In context sports analysis, allows new developments opportunities by taking into account players’ positions over time. Exploiting its sequence a sys...

Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving inferring the present state to predicting future state. Vision-based action recognition prediction videos are such tasks, where is infer human actions (present state) based upon complete executions, predict (future incomplete executions. These two become particularly prevalent topics recen...

In recent years, with the development of network technology, countless videos are produced every day. Many achievements have also been made in field action recognition computer vision. Training models requires a large number labeled samples, but reality, amount data is scarce, and it extremely difficult to obtain due costs other reasons. The few-shot learning aims solve problem using several sa...