Video recognition has been dominated by the end-to-end learning paradigm–first initializing
a video recognition model with weights of a pretrained image model and then conducting …

Abstract We present Mobile Video Networks (MoViNets), a family of computation and
memory efficient video networks that can operate on streaming video for online inference …

Human action recognition is an important field in computer vision that has attracted
remarkable attention from researchers. This survey aims to provide a comprehensive …

To effectively comprehend human actions, we have developed a Deep Neural Network
(DNN) that utilizes inner spatiotemporal non-locality to capture meaningful semantic context …

Over the past few years, technologies in the field of computer vision have greatly advanced.
The use of deep neural networks, together with the development of computing capabilities …

The dual stream-based human action recognition model offers the advantage of high
recognition accuracy, but the algorithm is less robust in case of lighting changes. The human …

Audio-visual correlation learning aims to capture and understand natural phenomena
between audio and visual data. The rapid growth of Deep Learning propelled the …

Deep convolutional neural networks have been leveraged to achieve huge improvements in
video understanding and human activity recognition performance in the past decade …

Human action recognition is one of the most challenging tasks in computer vision due to
background noise interference and video frame redundancy. Therefore, we propose a two …

This work addresses the growing concerns about security and passenger safety on public
transportation. With the increasing demand for public transport and the rise in road traffic …