Classification of human actions is an ongoing research problem in computer vision. This
review is aimed to scope current literature on data fusion and action recognition techniques …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

In recent years, gesture recognition has been widely used in the fields of intelligent driving,
virtual reality, and human-computer interaction. With the development of artificial …

Existing video understanding approaches, such as 3D convolutional neural networks and
Transformer-Based methods, usually process the videos in a clip-wise manner. Hence huge …

The explosive growth in video streaming gives rise to challenges on performing video
understanding at high accuracy and low computation cost. Conventional 2D CNNs are …

Abstract Spatial-temporal, channel-wise, and motion patterns are three complementary and
crucial types of information for video action recognition. Conventional 2D CNNs are …

Recent progress in fine-grained gesture and action classification, and machine translation,
point to the possibility of automated sign language recognition becoming a reality. A key …

Human action recognition has recently become one ofthe popular research topics in the
computer vision community. Various 3D-CNN based methods have been presented to tackle …

Human action is naturally compositional: humans can easily recognize and perform actions
with objects that are different from those used in training demonstrations. In this paper, we …

Recently, substantial research effort has focused on how to apply CNNs or RNNs to better
capture temporal patterns in videos, so as to improve the accuracy of video classification. In …