This work addresses the task of action recognition in video sequences. In real world
applications, this task is quite challenging due to the complex background of video content …

By extracting spatial and temporal characteristics in one network, the two-stream ConvNets
can achieve the state-of-the-art performance in action recognition. However, such a …

Convolutional Neural Networks have achieved excellent successes for object recognition in
still images. However, the improvement of Convolutional Neural Networks over the …

Convolutional neural networks (CNNs) have shown an effective way to learn spatiotemporal
representation for action recognition in videos. However, most traditional action recognition …

Combining multiple information streams has shown obvi-ous improvements in video action
recognition. Most exist-ing works handle each stream independently or perform a simple …

Action recognition has been a heated topic in computer vision for its wide application in
vision systems. Previous approaches achieve improvement by fusing the modalities of the …

In recent years, action recognition has become a popular and challenging task in computer
vision. Nowadays, two-stream networks with appearance stream and motion stream can …

Long-term temporal information yields crucial cues for video action understanding. Previous
researches always rely on sequential models such as recurrent networks, memory units …

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

Action recognition has attracted growing interest recently. It suffers from the problem that
complex and diverse environments may disturb the extraction of action features. Existing …