Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames
by watching unlabeled videos via deep convolutional network has made significant progress …

Recent studies demonstrated that the average heart rate (HR) can be measured from facial
videos based on non-contact remote photoplethysmography (rPPG). However for many …

Abstract Convolutional Neural Networks (CNN) have been regarded as a powerful class of
models for visual recognition problems. Nevertheless, the convolutional filters in these …

Many believe that the successes of deep learning on image understanding problems can be
replicated in the realm of video understanding. However, due to the scale and temporal …

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

In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector--a
progressive learning framework for spatio-temporal action detection in videos. Starting from …

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 …

Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep
convolutional network has made significant process recently. Current state-of-the-art (SOTA) …