Fourth International Workshop on Large Scale Holistic Video Understanding

In Conjunction with CVPR 2023,

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 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 the 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.


The main objective of the workshop is to establish a video benchmark integrating joint recognition of all the semantic concepts, as a single class label per task is often not sufficient to describe the holistic content of a video. The planned panel discussion with world’s leading experts on this problem will be a fruitful input and source of ideas for all participants. Further, we invite the community to help to extend the HVU dataset that will spur research in video understanding as a comprehensive, multi-faceted problem. We as organizers expect to receive valuable feedback from users and from the community on how to improve the benchmark.


  • 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




June 18th - AM


Prospective authors will be invited to submit a regular paper of previously unpublished work (CVPR paper format) or an extended abstract of a published work. The review process will be double blind. All the submissions will be peer-reviewed by the international program committee. Accepted papers will be presented as posters or contributed talks and will be considered non-archival and published via the Open Access versions, provided by the Computer Vision Foundation. Accepted extended abstracts will be presented at the poster session.


June 18th


*You can submit papers in two different formats:

  1. We will accept papers that have not been published elsewhere or have been recently published elsewhere including CVPR 2023. Accepted papers will appear in CVPR proceedings. For submissions of papers, we will follow the Double Blind review process, in that authors do not know the names of the reviewers of their papers, and reviewers do not know the names of the authors. The authors must follow the CVPR 2023 submission policy. Papers are limited to eight pages, including figures and tables, in the CVPR style. Additional pages containing only cited references are allowed. Please refer to the CVPR 2023 website for more information. Papers that are not properly anonymized, or do not use the template, or have more than eight pages (excluding references) will be rejected without review. The deadline for paper submission is March 31st. Notification to the authors by April 14th. The accepted papers must follow the CVPR 2023 camera-ready format as per the instructions are given here but limit your paper to 4-8 pages excluding references.
  2. For submissions of papers that have been published or accepted for publication in a recent venue, we will follow the Single Blind review process, in that authors do not know the names of the reviewers of their papers, but reviewers do know the names of the authors. Authors MUST indicate, in the footnote section on the first page of their submission, which venue their papers have been published or will be published. For example, if the paper will appear at CVPR 2023, the submission should include a footnote on the first page showing "To appear at 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition".
All papers must be formatted using the CVPR template style, which can be obtained at CVPR style.

Submit Your Work

Program Commitee - TBD