Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

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 …

Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate
impressive practical success in many different application domains, eg in autonomous …

Human activity recognition has gained an increasing relevance in computer vision and it can
be tackled with either non-hierarchical or hierarchical approaches. The former, also known …

Abstract Deep Recurrent Neural Network architectures, though remarkably capable at
modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while …

Recently dense trajectories were shown to be an efficient video representation for action
recognition and achieved state-of-the-art results on a variety of datasets. This paper …

In this work we improve training of temporal deep models to better learn activity progression
for activity detection and early detection. Conventionally, when training a Recurrent Neural …

This paper introduces a video representation based on dense trajectories and motion
boundary descriptors. Trajectories capture the local motion information of the video. A dense …

The temporal action localization research aims to discover action instances from untrimmed
videos, representing a fundamental step in the field of intelligent video understanding. With …

While activity recognition is a current focus of research the challenging problem of fine-
grained activity recognition is largely overlooked. We thus propose a novel database of 65 …