Second Large Scale Holistic Video Understanding Workshop Call for Papers
Second Large Scale Holistic Video Understanding Workshop @CVPR'21
https://holistic-video-understanding.github.io/workshops/cvpr2021.html
CVPR Dates: June 19-25, 2021 / Workshop Date: TBD
PAPER SUBMISSION IS NOW OPEN!
PAPER and ABSTRACT SUBMISSION DEADLINE: March 31, 2021
ACCEPTANCE NOTIFICATION: April 14, 2021
CAMERA READY: April 18, 2021
Please submit papers via CMT: https://cmt3.research.microsoft.com/HVU2021
WORKSHOP REGISTRATION: In conjunction with CVPR’21
OVERVIEW:
In the last years, we have seen 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
a humanoid robot, a combination of current state-of-the-art methods
should be used. Therefore, in this workshop, we intend to introduce
holistic video understanding as a new challenge for the video
understanding efforts. This challenge focuses on the recognition of
scenes, objects, actions, attributes, and events in the real-world
user-generated videos. To be able to address such tasks, we also
introduce our new dataset named Holistic Video Understanding (HVU
dataset) that is organized hierarchically in a semantic taxonomy of
holistic video understanding. Almost all of the real-world conditioned
video datasets are targeting human action or sport recognition. So,
our new dataset can help the vision community and bring more attention
to bring more interesting solutions for holistic video
understanding. The workshop is tailored to bringing together ideas
around multi-label and multi-task recognition of different semantic
concepts in the real-world videos. And the research efforts can be
tried on our new dataset. HVU Dataset:
https://github.com/holistic-video-understanding
Topics:
Large scale video understanding
Multi-Modal learning from videos
Multi-concept recognition from videos
Multi-task deep neural networks for videos
Learning holistic representation from videos
Weakly supervised learning from web videos
Object, scene and event recognition from videos
Unsupervised video visual representation learning
Unsupervised and self-supervised learning with videos
INVITED SPEAKERS:
Cordelia Schmid, Google AI
Joao Carreira, Google DeepMind
Carl Vondrick, Columbia University
Dima Damen, University of Bristol
Sanja Fidler, University of Toronto
Kristen Grauman, University of Texas at Austin
For questions about the HVU workshop, please contact
fayyaz@iai.uni-bonn.de. Also, follow HVU on Twitter for the latest
news: https://twitter.com/LSHVU or
https://holistic-video-understanding.github.io/
Organizers:
Mohsen Fayyaz, University of Bonn
Ali Diba, KU Leuven
Vivek Sharma, Harvard, MIT
Juergen Gall, University of Bonn
Ehsan Adeli, Stanford University
Rainer Stiefelhagen, KIT
Luc Van Gool, ETH Zurich & KU Leuven
David Ross, Google AI
Manohar Paluri, Facebook AI