This repository is a curated survey of multimodal learning research, targeting researchers and practitioners in AI and machine learning. It provides a comprehensive, organized collection of papers, datasets, and resources covering various aspects of multimodal learning, facilitating efficient exploration and understanding of the field.

How It Works

The collection is structured by topic and chronology, categorizing resources into surveys, datasets, and specific research areas like Vision and Language Pre-training (VLP). Each entry includes links to papers, code, and project pages, often with annotations indicating novelty (🌱), foundational status (📌), state-of-the-art performance (🚀), or dataset contributions (👑). This structured approach allows users to quickly identify relevant and impactful research.

Quick Start & Requirements

This is a curated list of research papers and resources, not a software library. No installation or execution is required. Users can directly access the listed papers and code repositories.

Highlighted Details

• Comprehensive coverage of multimodal learning, including surveys, datasets, and specific task-oriented research.
• Resources are organized by topic (e.g., Vision-Language Pre-training, Multimodal Synthesis) and publication year.
• Entries are annotated with symbols indicating novelty, SOTA status, and dataset contributions.
• Includes links to papers, code, project pages, and demos for many entries.

Maintenance & Community

The repository is maintained by Yutong Zhou. Contact information for the maintainer is provided for inquiries.

Licensing & Compatibility

The repository itself is a collection of links and does not have a specific license. The licenses of the linked papers and code repositories vary.

Limitations & Caveats

As a survey, the content is dependent on the research landscape and may not include the absolute latest publications immediately. The quality and availability of linked code and project pages depend on the original authors.