Convolutional neural networks (CNNs) have achieved human-level performance in various
computer vision tasks, such as image classification, object detection & segmentation, etc …

The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown
extremely good results for video human action classification, however, action detection is …

Behavior is an important indicator for understanding the well-being of animals. This process
has been frequently carried out by observing video records to detect changes with statistical …

Detecting actions in videos is a challenging task as video is an information intensive media
with complex variations. Existing approaches predominantly generate action proposals for …

The goal of this paper is to detect the spatio-temporal extent of an action. The two-stream
detection network based on RGB and flow provides state-of-the-art accuracy at the expense …

Spatio-temporal action detection (STAD) aims to classify the actions present in a video and
localize them in space and time. It has become a particularly active area of research in …

The current deep learning based spatio-temporal action localization methods that using
motion information (predominated is optical flow) obtain the state-of-the-art performance …

This paper presents a novel Hierarchical Self-Attention Network (HISAN) to generate spatial-
temporal tubes for action localization in videos. The essence of HISAN is to combine the two …

The spatial independence and temporal continuity of video data as a whole are not fully
investigated for video action detection. To tackle this issue, a deep network architecture is …

This paper addresses the problem of spatiotemporal localization of actions in videos.
Compared to leading approaches, which all learn to localize based on carefully annotated …