In this paper, we propose an efficient and straightforward approach, video you only look
once (VideoYOLO), to capture the overall temporal dynamics from an entire video in a single …

How to leverage the temporal dimension is a key question in video analysis. Recent works
suggest an efficient approach to video feature learning, ie, factorizing 3D convolutions into …

Abstract Convolutional Neural Networks (CNNs) usually use top-level appearance features
of video frames for action recognition. However, these methods discard the implicit …

Deep convolutional networks have achieved great success for visual recognition in still
images. However, for action recognition in videos, the advantage over traditional methods is …

The work in this paper is driven by the question how to exploit the temporal cues available in
videos for their accurate classification, and for human action recognition in particular? Thus …

Encouraged by the success of convolutional neural networks (CNNs) in image classification,
recently much effort is spent on applying the CNNs to the video-based action recognition …

In this paper, a novel video classification method is presented that aims to recognize
different categories of third-person videos efficiently. Our motivation is to achieve a light …

Video-based action recognition with deep neural networks has shown remarkable progress.
However, most of the existing approaches are too computationally expensive due to the …

Typical human actions last several seconds and exhibit characteristic spatio-temporal
structure. Recent methods attempt to capture this structure and learn action representations …

From the frame/clip-level feature learning to the video-level representation building, deep
learning methods in action recognition have developed rapidly in recent years. However …