Deep video understanding is a difficult task which requires systems to develop a deep analysis and understanding of the relationships between different entities in video, to use known information to reason about other, more hidden information, and to populate a knowledge graph (KG) with all acquired information. To work on this task, a system should take into consideration all available modalities (speech, image/video, and in some cases text). The aim of this new challenge is to push the limits of multimodal extraction, fusion, and analysis techniques to address the problem of analyzing long duration videos holistically and extracting useful knowledge to utilize it in solving different types of queries. The target knowledge includes both visual and non-visual elements. As videos and multimedia data are getting more and more popular and usable by users in different domains, the research, approaches and techniques we aim to be applied in this Grand Challenge will be very relevant in the coming years and near future.
Challenge Overview:

Interested participants are invited to apply their approaches and methods on a novel High-Level Video Understanding (HLVU) dataset being made available by the challenge organizers. These include 10 movies with a Creative Commons license. The dataset will be annotated by human assessors and ground truth (Ontology of relations, entities, actions & events, names and images of all main characters, and Knowledge Graph for 50% of the movies) provided to participating researchers for training and development of their systems. The organizers will support evaluation and scoring of three main query types distributed with the dataset (please refer to the dataset webpage for more details):

- Multiple choice question answering on part of Knowledge Graph for selected movies.
- Possible path analysis between persons / entities of interest in a Knowledge Graph
extracted from selected movies.
- Fill in the Graph Space - Given a partial graph, systems will be asked to fill in the
graph space.

Important Dates

HLVU movie dataset available including preliminary annotations: March 31, 2020
Complete HLVU annotations and development data available: April, 24
Testing queries released: May 29, 2020
Run submissions due to organizers: July 13, 2020
Paper submission deadline: July 13, 2020
Results released back to participants: July 27, 2020
Notification to authors: July 27, 2020
Workshop camera-ready submission: August 10, 2020
ACM Multimedia dates: October 12 - 16, 2020