Archival track
Peer-reviewed full papers included in the workshop proceedings, limited to 14 pages plus unlimited references.
AI/CORE Unranked
MADiMa 2026
10th Multimodal AI for Dietary Intelligence, Management, and Assessment Workshop
MADIMA 2026 is a workshop held in conjunction with ACCV 2026 in Osaka, Japan, focused on multimodal artificial intelligence for dietary intelligence, management, and assessment. It brings together researchers from AI, multimedia, nutrition, and health to advance food understanding through vision-language models, sensor integration, and decision-oriented pipelines, with emphasis on robustness, inclusiveness, and clinically meaningful evaluation.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Non-archival track
Extended abstracts not included in the proceedings, submitted for presentation only.
Nectar track
Previously published work not included in the proceedings, submitted for presentation only.
Research areas in scope
01
Topics of Interest
Supervised, semi-supervised, weakly supervised, unsupervised, and self-supervised learning for food recognition and understandingFine-grained food recognition, transfer learning, few-shot learning, long-tailed learning, and learning with noisy labelsFood detection, localization, segmentation, tracking, and instance counting using conventional and foundation modelsLarge language models, vision-language models, and multimodal foundation models for recipe understanding, ingredient recognition, food composition analysis, nutritional content estimation, and database groundingMonocular and stereo depth estimation, three-dimensional reconstruction, and point cloud analysis for food volume and portion size estimationAugmented and virtual reality for interactive food analysis, dietary logging, portion estimation, and nutrition educationTrustworthy and deployable multimodal AI, including explainability, robustness, fairness, privacy, federated learning, and efficient edge deploymentMultimodal food representation learning across images, video, text, speech, depth, wearable devices, and other sensor dataOut-of-distribution food detection, anomaly detection, open-set recognition, open-world recognition, and uncertainty estimationGenerative models for food image and video synthesis, data augmentation, counterfactual generation, and multimodal content generationMultimodal dietary intake assessment using food images, videos, speech, text, barcodes, wearable devices, and mobile or ambient sensorsPersonalized dietary management, including meal planning, food recommendation, intake monitoring, adaptive feedback, and behavioral supportTemporal and contextual modeling of dietary behavior, meal patterns, food diaries, cuisine diversity, and cultural factorsDatasets, benchmarks, evaluation protocols, and metrics for multimodal food understanding and dietary assessment
Policies worth checking twice
- Submissions must be original and not under review at another archival venue during the review period.
- Submissions must not substantially overlap with prior or concurrent published work.
- Posting preprints on arXiv or similar non-peer-reviewed servers is permitted during review, provided anonymity is preserved.
- Double-blind review is enforced for the archival track; authors must anonymize papers and avoid identifying information in text, videos, or supplementary materials.
- Papers must be prepared using the official Springer LNCS style and ACCV 2026 LaTeX template.
- Archival track papers are limited to 14 pages (excluding unlimited references).
- Supplementary materials are allowed up to 50MB in a single file, including code, videos, or additional figures.
- Large Language Models must not be used to generate submissions, replace intellectual contribution, or fabricate results; limited use for language editing is acceptable.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026