This repository is a curated list of research papers, workshops, tutorials, and news related to multimodal machine learning. It serves as a comprehensive resource for researchers and practitioners interested in the intersection of different data modalities like text, vision, and audio. The goal is to provide a centralized, organized collection of cutting-edge advancements in the field.

How It Works

The repository categorizes multimodal research into core areas such as Representation Learning, Multimodal Fusion, and Alignment, as well as applications like Visual Question Answering and Multimodal Machine Translation. It also tracks recent news and developments from leading AI research labs like OpenAI and Google, highlighting key models and APIs. The structure facilitates easy navigation and discovery of relevant information.

Quick Start & Requirements

This is a curated list, not a software package. No installation or specific requirements are needed beyond a web browser to access the information.

Highlighted Details

• Comprehensive coverage of core multimodal research areas.
• Tracks recent news and developments from major AI labs (OpenAI, Google).
• Includes extensive lists of workshops, tutorials, and relevant papers.
• Organized by topic for efficient information retrieval.

Maintenance & Community

The repository is a fork of Paul Liang's original work and encourages community contributions via pull requests. It is actively maintained by Eurus-Holmes.

Licensing & Compatibility

The repository itself is not licensed as a software project. The content is for informational purposes.

Limitations & Caveats

As a curated list, it does not provide code or implementations. The information is dependent on the frequency of updates by the maintainer and community contributions.