Large Scale Holistic Video Understanding Tutorial

In Conjunction with CVPR 2020, Seattle, Washington, U.S.

Holistic Video Understanding is a joint project of the KU Leuven, University of Bonn, KIT, ETH, and the HVU team.


In the last years, we have seen a tremendous progress in the capabilities of computer systems to classify video clips taken from the Internet or to analyze human actions in videos. There are lots of works in video recognition field focusing on specific video understanding tasks, such as, action recognition, scene understanding, etc. There have been great achievements in such tasks, however, there has not been enough attention toward the holistic video understanding task as a problem to be tackled. Current systems are expert in some specific fields of the general video understanding problem. However, for real world applications, such as, analyzing multiple concepts of a video for video search engines and media monitoring systems or providing an appropriate definition of the surrounding environment of an humanoid robot, a combination of current state-of-the-art methods should be used. Therefore in this tutorial, we intend to put effort into introducing the holistic video understanding as a new challenge in the computer vision field. This challenge focuses on the recognition of scenes, objects, actions, attributes, and events in the real world and user-generated videos. We also aim to cover the most important aspects of video recognition and understanding in the tutorial course work.


  • Holistic video recognition
  • Future prediction in videos
  • Large scale video understanding
  • Multiple category recognition in videos
  • Object and activity detection in videos
  • Weakly supervised learning from web videos
  • Learning visual representation from videos
  • Unsupervised and self-supervised learning with videos
  • 3D/2D Deep Neural Networks for action and activity recognition



Time Speaker Description
09:00 Overview
09:05 Chen Sun Action Recognition in Videos and Action Forecasting
10:00 Ivan Laptev Dynamic scene understanding
11:00 Andreas Geiger Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences
12:00 Juergen Gall Introduction to Holistic Video Understanding
An Introduction to Temporal Action Segmentation - From Fully Supervised Learning to Unsupervised Learning
13:00 Cees Snoek Knowledge-supervised video understanding
14:00 Raquel Urtasun Holistic Understanding for Self-Driving
15:00 Christoph Feichtenhofer Efficient Video Recognition
16:00 Carl Vondrick Learning from Unlabeled Video
17:00 David Ross Context & Attention for Detecting Objects and Actions in Video