CVPR2026 Workshop on Emerging Directions in Data for Multimodal Foundation Models

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.