Despite the remarkable success of deep-learning in image and video recognition,
constructing real-time recognition systems for computationally intensive tasks such as spatio …

The deployment of video action recognition models on mobile and embedded devices is
challenging due to the limited computational resources and storage capacity. To address …

With the wide deployment of Internet of Things monitoring terminals, a tremendous number
of videos are accumulated continuously. Big data processing and analysis-based action …

Though action recognition in videos has achieved great success recently, it remains a
challenging task due to the massive computational cost. Designing lightweight networks is a …

3D convolution networks play an important role in extracting spatiotemporal features in
video action recognition. However, it usually brings a large number of paramters, which …

Video understanding usually requires expensive computation that prohibits its deployment,
yet videos contain significant spatiotemporal redundancy that can be exploited. In particular …

Intelligent terminal based action recognition is important to smart cities. However, due to the
dependency on training data and high complexity of extracting information, the existing …

Abstract Convolutional Neural Networks (CNNs) have been the dominant model for video
action recognition. Due to the huge memory and compute demand, popular action …

Efficiently modeling spatial–temporal information in videos is crucial for action recognition.
To achieve this goal, state-of-the-art methods typically employ the convolution operator and …

Video action recognition is a vital task in the field of computer vision. A great deal of
redundant information is generated along with original video data in the process of depth …