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.
|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|