Deep Video Understanding, ACM Multimedia 2020 Grand Challenge, Call for Papers

Call for Participation : 
Deep Video Understanding, ACM Multimedia 2020 Grand Challenge, 
October 12-16, 2020

Challenge website:


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.


Each Grand Challenge Submitted paper should be formated as 4-page
short paper and will be included in the main conference proceeding.

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: June 29, 2020
Results released back to participants: July 13, 2020
Paper submission deadline: TBD
Notification to authors: TBD
Workshop camera-ready submission: August 7, 2020
ACM Multimedia dates: October 12 - 16, 2020