Human activity recognition (HAR) is one of the most important and challenging problems in
the computer vision. It has critical application in wide variety of tasks including gaming …

Although widely used in many applications, accurate and efficient human action recognition
remains a challenging area of research in the field of computer vision. Most recent surveys …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

How do humans recognize the action" opening a book"? We argue that there are two
important cues: modeling temporal shape dynamics and modeling functional relationships …

We address temporal action localization in untrimmed long videos. This is important
because videos in real applications are usually unconstrained and contain multiple action …

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

Abstract We use Long Short Term Memory (LSTM) networks to learn representations of
video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed …

Understanding human actions in visual data is tied to advances in complementary research
areas including object recognition, human dynamics, domain adaptation and semantic …

We propose a simple, yet effective approach for spatiotemporal feature learning using deep
3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised …

We propose an approach to learn spatio-temporal features in videos from intermediate
visual representations we call" percepts" using Gated-Recurrent-Unit Recurrent Networks …