We invite submissions on any topics related to Data for Multimodal Foundation Models (DataMFM), including, but not limited to:
Data collection, generation, and curation for multimodal foundation models
Data quality improvement, filtering, and pruning for scalable and efficient multimodal training
Data recipes and mixture design for balancing scale, quality, diversity, and coverage
Synthetic–real hybrid datasets and multimodal data augmentation for robust model development
Benchmark renewal, creation, and evaluation design for trustworthy multimodal applications
Detection and mitigation of dataset contamination in training and evaluation
Cross-modal alignment and grounding across text, image, audio, and video modalities
Fairness, bias reduction, and inclusive representation in multimodal datasets
Data provenance, documentation, licensing, and governance for trustworthy dataset lifecycles
Metrics and frameworks for assessing multimodal data quality, diversity, and contamination
Bridging modality gaps between text-rich and vision-centric domains
Agentic synthetic data generation and self-improving data pipelines driven by multimodal or VLA models
Building sustainable, transparent, and community-driven multimodal data ecosystems for next generation foundation models
Submission Guidelines:
The workshop accepts submissions in three tracks:
(1) Full-length Papers (Archival, Proceedings Track): Up to 8 pages, excluding references; Double-blind review; Accepted papers will appear in the CVPR 2026 Workshop Proceedings;
(2) Short Papers / Extended Abstracts (Non-archival): Up to 4 pages, excluding references; Double-blind review; Intended for work-in-progress, datasets, benchmarks, and early-stage ideas;
(3) CVPR 2026 Accepted Papers (Non-archival, Non-anonymous): Papers accepted to the main CVPR 2026 conference; Presented at the workshop but not included in the workshop proceedings
Submission Site: Proceedings Track: https://openreview.net/group?id=thecvf.com/CVPR/2026/Workshop/DataMFM_Proceedings_Track
Non-archival Track: https://openreview.net/group?id=thecvf.com/CVPR/2026/Workshop/DataMFM_Non-archival
All submissions should use the CVPR 2026 paper template.