[CFP] 9th Internation Workshop on Multimedia Assisted Dietary Management (MADiMa) (deadline: 8/30)

[Keiji YANAI]

Image-MLの皆様，

マルチメディア技術を用いた食事管理 に関するワークショップ
8th International Workshop on Multimedia Assisted Dietary
Management (MADiMa) の 論文募集のご案内をお送り致します．

インドのコルカタで 12/1-5 に開催されるICPR の workshop として
開催予定です．Workshopは 12/1 に開催予定です．

食事画像の認識・生成や応用アプリ，クロスモーダルレシピ検索, LLMやLMMの
食事への応用など，食事に関する様々なトピックが対象となっています．

論文締切は 8月30日となっています．
詳しくは，以下のCFP および https://www.madima.org/ を御覧ください．

以上，よろしくお願い致します．

電通大 柳井

===================================================
Call For Papers (apologies for multiple copies)
===================================================

The 9th International Workshop on Multimedia Assisted Dietary
Management (MADiMa2024) is organized in conjunction with the 27th
International Conference on Pattern Recognition (ICPR2024).

Website: www.madima.org
Place: Kolkata, India
Date: 1st December 2024
Submission Deadline: 30th August 2024

RATIONALE
=========
Recent advancements in artificial intelligence, wearable technologies,
big data analytics, and healthcare technology in general have ushered
in the era of mHealth, revolutionizing healthcare services. This
transformation is not only reshaping the way we approach healthcare
but also paving the way for personalized nutrition, healthier
lifestyles to prevent diseases and achieve effective self-management
of chronic conditions. A significant challenge in nutrition research
is acquiring high-quality, precise nutrition information in an
economically viable manner. Mobile and wearable technologies, with
their flexibility and efficiency, coupled with the capabilities of
artificial intelligence enable the analysis of large volumes of
multi-level and heterogeneous data, pattern detection, risk
prediction, and intervention guidance. This fusion holds immense
potential for promoting healthier dietary habits and behaviors and
facilitating communication between caregivers and care recipients. The
need for accurate, automatic, real-time, and personalised dietary
advice has been recently complemented by advances in artificial
intelligence and computer vision, permitting the development of
end-to-end pipelines for food content analysis. The proposed solutions
rely on the analysis of multimedia content captured by wearable
sensors, smartphone cameras, barcode scanners, RFID readers and IR
sensors, along with already established nutritional and recipe
databases and may require some user input. In the field of nutritional
management, multimedia not only bridges diverse information and
communication technologies, but also computer science with medicine,
nutrition, and dietetics. This confluence brings new challenges and
opportunities on dietary monitoring, assessment, and management.

SCOPE
=====
The main scope of MADiMa2024 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.

TOPICS
======
Topics of interest include (but are not limited to) the following:
- Supervised food recognition (Fine-grained/Transfer/Noisy-label Learning)
- Unsupervised food recognition (Self-supervised/Multi-modal Learning)
- Out-of-distribution food detection (Anomaly Detection, Open-Set Learning)
- Food image synthesis with generative models (GANs, Diffusion Models, etc.)
- Food detection and segmentation (vision foundation models, SAM, etc.)
- LLMs/LVMs to automatize food composition analysis (recipe
comprehension, database reading, nutritional content estimation, etc.)
- Monocular/Binocular depth estimation from mobile/static sensors
- 3D point cloud processing and analysis for food volume estimation
- Augmented/Virtual reality for food analysis and portion estimation
- Benchmarks, evaluation protocols, and metrics for the above topics

IMPORTANT DATES
================
- Paper submission deadline: August 30th, 2024
- Notification of acceptance: September 20th, 2024
- Camera ready deadline: September 26th, 2024

KEYNOTE SPEAKERS
=================
- Prof. Ramesh Jain, University of California, Irvine (CONFIRMED)
Topic: Conversational Personal Food Agents
- Prof. Georges Dedousis, Harokopio University of Athens (CONFIRMED)
Topic: The Importance of Multimodal Data Analysis in Obesity
Prevention and Management
- Prof. Dima Damen, University of Bristol and Google DeepMind (TENTATIVE)

WORKSHOP CHAIRS
=================
- Stavroula Mougiakakou, University of Bern, Switzerland
- Keiji Yanai, The University of Electro-Communications, Tokyo, Japan
- Dario Allegra, University of Catania, Italy
- Yoko Yamakata, The University of Tokyo, Japan
- Lorenzo Brigato, University of Bern, Switzerland

For more information, please visit the workshop's website at www.madima.org.

The workshop chairs,

Stavroula Mougiakakou
Keiji Yanai
Dario Allegra
Yoko Yamakata
Lorenzo Brigato