Deep learning based methods have achieved remarkable progress in action recognition.
Existing works mainly focus on designing novel deep architectures to learn video …

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

We present a general and flexible video-level framework for learning action models in
videos. This method, called temporal segment network (TSN), aims to model long-range …

Action recognition in videos, which contains many complex and semantic contents, is still a
challenging task in computer vision research. In this paper, we propose a novel attention …

Temporal modeling is key for action recognition in videos. It normally considers both short-
range motions and long-range aggregations. In this paper, we propose a Temporal …

Spatiotemporal and motion features are two complementary and crucial information for
video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn …

Action recognition in videos is a challenging task due to the complexity of the spatio-
temporal patterns to model and the difficulty to acquire and learn on large quantities of video …

Recent years have witnessed the popularity of using recurrent neural network (RNN) for
action recognition in videos. However, videos are of high dimensionality and contain rich …

Deep learning based methods have achieved remarkable progress in action recognition.
Existing works mainly focus on designing novel deep architectures to achieve video …

The existing action recognition methods are mainly based on clip-level classifiers such as
two-stream CNNs or 3D CNNs, which are trained from the randomly selected clips and …