Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …

Human activity recognition systems are developed as part of a framework to enable
continuous monitoring of human behaviours in the area of ambient assisted living, sports …

Abstract We propose Anticipative Video Transformer (AVT), an end-to-end attention-based
video modeling architecture that attends to the previously observed video in order to …

Temporal action detection (TAD) aims to determine the semantic label and the temporal
interval of every action instance in an untrimmed video. It is a fundamental and challenging …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

We propose TAL-Net, an improved approach to temporal action localization in video that is
inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key …

Deciphering human behaviors to predict their future paths/trajectories and what they would
do from videos is important in many applications. Motivated by this idea, this paper studies …

Spatiotemporal predictive learning, though long considered to be a promising self-
supervised feature learning method, seldom shows its effectiveness beyond future video …

This paper focuses on temporal localization of actions from untrimmed videos. Existing
methods typically involve training classifiers for a pre-defined list of actions and applying the …

We address the problem of activity detection in continuous, untrimmed video streams. This is
a difficult task that requires extracting meaningful spatio-temporal features to capture …