Research in human action recognition has accelerated significantly since the introduction of
powerful machine learning tools such as Convolutional Neural Networks (CNNs). However …

Current state-of-the-art models for video action recognition are mostly based on expensive
3D ConvNets. This results in a need for large GPU clusters to train and evaluate such …

In this paper, we introduce a novel framework for video-based action recognition, In this
paper, we introduce a novel framework for video-based action recognition, which …

We investigate architectures of discriminatively trained deep Convolutional Networks
(ConvNets) for action recognition in video. The challenge is to capture the complementary …

The two-stream CNNs prove very successful for video-based action recognition. However,
the classical two-stream CNNs are time costly, mainly due to the bottleneck of calculating …

Deep learning models for video-based action recognition usually generate features for short
clips (consisting of a few frames); such clip-level features are aggregated to video-level …

In this article, we study the problem of video-based action recognition. We improve the
action recognition performance by finding an effective temporal and appearance …

Recognizing actions in videos is not a trivial task because video is an information-intensive
media and includes multiple modalities. Moreover, on each modality, an action may only …

Recently, as the application of the convolutional neural network in artificial intelligence is
becoming increasingly diversified, a growing number of neural network methods are put …

The Two-stream convolution neural network (CNN) has proven a great success in action
recognition in videos. The main idea is to train the two CNNs in order to learn spatial and …