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

Abstract We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm
for online action detection, which employs a long-and short-term memory mechanism to …

Online action detection is the task of predicting the action as soon as it happens in a
streaming video. A major challenge is that the model does not have access to the future and …

Abstract Online Action Detection (OAD) is the task of identifying actions in streaming videos
without access to future frames. Much effort has been devoted to effectively capturing long …

This paper addresses a new problem of weakly-supervised online action segmentation in
instructional videos. We present a framework to segment streaming videos online at test time …

We propose a memory efficient method, named Stochastic Backpropagation (SBP), for
training deep neural networks on videos. It is based on the finding that gradients from …

Recently, feature-based methods for Online Action Detection (OAD) have been gaining
traction. However, these methods are constrained by their fixed backbone design, which …

The ability to anticipate possible future human actions is essential for a wide range of
applications, including autonomous driving and human-robot interaction. Consequently …

Online action detection plays a vital role in video action understanding and can be widely
used in various video analysis applications. This task aims to detect actions at the current …

Online Action Detection (OAD), which predicts the ongoing human action from a streaming
video, is an important task in multimedia analysis. Compared with offline action detection …