- General Chairs:
- Stavroula Mougiakakou,
- Giovanni Maria Farinella,
- Keiji Yanai,
- Dario Allegra
It is our great pleasure to welcome you to the 7th International Workshop on Multimedia Assisted Dietary Management -- MADiMa 2022. After the success of the past MADiMa workshops, we would like to present to you the MADiMa 2022 to be held with the 30th ACM International Conference in Multimedia 2022 in Lisbon, Portugal. For the second time, MADiMa and the International Workshop on Multimedia for Cooking, Eating, and related APPlications (CEA) are organized at the same place. The workshop provides a platform, in which researchers, students, and industry players can meet in order to explore and discuss state of the art in research and technology, to investigate the challenges faced during the design and development of multimedia assisted dietary assessment and management systems, as well as to exchange ideas in future research trends.
The call for papers attracted submissions from Asia, Europe, and the United States. The workshop had in total 11 submissions. A double-blind review process yielded to 10 papers that were accepted in this year's program. The workshop is complemented by three invited speakers: Dr. Arindam Ghosh, from Oviva AG, will present the mediPiatto project, in which an AI-based end-to-end automatic system was developed to estimate the Mediterranean Diet Adherence of users, Oliver Amft, from University of Freiburg, Germany, will discuss about the use of sensors and wearable devices in automated dietary monitoring and technology-based dietary intervention, and George Hadjigeorgiou, co-founder of ZOE, will present his mission to improve the health of millions by moving the world from calorie counting and foods that poison our health to the truth of personalized advice based on how food affects our bodies and health. We believe that using OpenReview together with Microsoft CMT (or a similar tool) will raise the scientific standards and extend the scientific impact of future ACM Multimedia editions.
Proceeding Downloads
The Quest towards Automated Dietary Monitoring & Intervention in Free-living
In the first part of this talk, I will review the hunt for sensors that started of the field of automated dietary monitoring (ADM) and continues to play a role in shaping current research. Moreover, I will describe the eyeglasses-based sensors that we ...
Real Scale Hungry Networks: Real Scale 3D Reconstruction of a Dish and a Plate using Implicit Function and a Single RGB-D Image
The management of dietary calorie content using information technology has become an essential topic in the multimedia field of research in recent years. Therefore, many researchers and companies are conducting research and developing applications. Many ...
Chewing Detection from Commercial Smart-glasses
Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored. ...
Learning Multi-Subset of Classes for Fine-Grained Food Recognition
Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we ...
mediPiatto: Using AI to Assess and Improve Mediterranean Diet Adherence
Numerous studies have demonstrated the benefits of Mediterranean Diet Adherence (MDA) to improved long-term weight loss outcomes, positive effects on cardiovascular health, and decrease in complications among diabetic patients. However, manual ...
Text-based Image Editing for Food Images with CLIP
Recently, the large-scale language-image pre-trained model, such as CLIP, has drawn much attention due to its remarkable ability for various tasks, including classification and image synthesis. The combination of CLIP and GAN can be used for text-based ...
World Food Atlas for Food Navigation
Food plays a central role in agriculture, public wellness, public health, culinary art, and culture. Food-related data is available in varying formats and with different access levels ranging from private datasets to publicly downloadable data. Every ...
SetMealAsYouLike: Sketch-based Set Meal Image Synthesis with Plate Annotations
By using semantic segmentation dataset with pixel-wise annotation for training GANs, image generation from a given mask image drawn by a user is possible. However, regarding mask-based food image synthesis, the existing food segmentation datasets have ...
DepthGrillCam: A Mobile Application for Real-time Eating Action Recording Using RGB-D Images
An automatic meal recording is one of typical applications of image recognition technology. In fact, some mobile apps on meal recording have been released so far. Most of the apps assume that a user takes a meal photo before start eating. However, this ...
Simulating Personal Food Consumption Patterns using a Modified Markov Chain
Food image classification serves as the foundation of image-based dietary assessment to predict food categories. Since there are many different food classes in real life, conventional models cannot achieve sufficiently high accuracy. Personalized ...
Automatic Classification of High vs. Low Individual Nutrition Literacy Levels from Loyalty Card Data in Switzerland
The increasingly prevalent diet-related non-communicable diseases (NCDs) constitute a modern health pandemic. Higher nutrition literacy (NL) correlates with healthier diets, which in turn has favorable effects on NCDs. Assessing and classifying people's ...
AI-Assisted Food Intake Activity Recognition Using 3D mmWave Radars
The automatic recognition of when and for how long a person is eating a certain food or drinking has applications in telecare, smarthome data monetization, and diet control. Existing food recognition systems either recognize the type of the food, but ...
Index Terms
- Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| MADiMa '22 | 10 | 9 | 90% |
| MADiMa '16 | 14 | 7 | 50% |
| Overall | 24 | 16 | 67% |