Despite the success of deep learning for static image understanding, it remains unclear what
are the most effective network architectures for spatial-temporal modeling in videos. In this …

Video action recognition has been a challenging task over the years. The challenge herein
is not only due to the complication in increasing information in videos but also the …

Video action recognition is widely applied in video indexing, intelligent surveillance,
multimedia understanding, and other fields. Recently, it was greatly improved by …

Despite the steady progress in video analysis led by the adoption of convolutional neural
networks (CNNs), the relative improvement has been less drastic as that in 2D static image …

Deep learning models have enjoyed great success for image related computer vision tasks
such as image classification and object detection. For video related tasks such as human …

Video action recognition is widely applied in video indexing, intelligent surveil-lance,
multimedia understanding, and other fields. Recently, it was greatly improved by …

For a long time, learning spatiotemporal features with deep neural networks has been a
difficult task in the field of computer vision. In this paper, we present a novel deep …

3-D convolutional neural networks (3-D-convNets) have been very recently proposed for
action recognition in videos, and promising results are achieved. However, existing 3-D …

Recently, deep convolutional networks (ConvNets) have achieved remarkable progress for
action recognition in videos. Most existing deep frameworks treat a video as an unordered …

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