Konferenzen  >  Informatik  >  Mulitimedia und Computergrafik, Spiele  >  Kanada

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) representation 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 challenge series 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 and contexts, 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.

The ambition of this workshop is to bring together researchers and practitioners from many different disciplines to share ideas and methods on current multimedia/multimodal content analysis research in sports.

The main scope of MADiMa2023 is to bring together researchers from the diverse fields of engineering, computer science and nutrition who investigate the use of information and communication technologies for better monitoring, assessment, and management of food intake. The combined use of multimedia, machine learning algorithms, ubiquitous computing and mobile technologies permits the development of applications and systems able to monitor the dietary behavior, analyze food intake, identify eating patterns, and provide feedback to the user towards healthier nutrition. The researchers will present and demonstrate their latest progress and discuss novel ideas in the field. Besides the technologies used, emphasis will be given to the precise problem definition, the available nutritional databases, the need for benchmarking multimedia databases of packed and unpacked food and the evaluation protocols.

Nahrungsmittelchemie und Agrarwissenschaften,    Physiologie und Metabolismus des Menschen

The ambition of SUMAC is to bring together researchers and practitioners from different disciplines to share ideas and methods on current trends in the analysis, understanding and promotion of heritage contents. These challenges are reflected in the corresponding sub-fields of machine learning, signal processing, multi-modal techniques and human-machine interaction.