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 workshop 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 workshop will be very relevant in the coming years and near future.

This workshop will support the following research contributions:
Contributions related (but not limited) to the following topics applied on the available DVU dataset or any external datasets are invited:

Multimodal feature extraction for movies and extended video

Multimodal fusion of computer vision, text/language processing and audio for extended video / movie analysis

Machine Learning methods for movie-based multimodal interaction

Sentiment analysis and multimodal dialogue modeling for movies

Knowledge Graph generation, analysis, and extraction for movies and extended videos


Important Dates:

Complete DVU annotations and development data available: - Available now at https://www-nlpir.nist.gov/projects/trecvid/dvu/dvu.development.dataset/

Workshop paper submission deadline: July 29, 2022

Notification to authors: TBD

Workshop camera-ready submission: TBD

Workshop (hybrid) date: November 7th or 11th, 2022