Introduction to Awesome-Multimodal-LLM
The "Awesome-Multimodal-LLM" project is dedicated to exploring the trends and advancements in the field of large language model (LLM) guided multimodal learning. Multimodal learning involves integrating multiple types of data, such as text, images, videos, and audio, to enhance the capabilities of AI models. This project primarily focuses on the integration of these modalities with large language models to improve their performance and applications.
Key Components of the Project
Multimodalities
- Text
- Vision: Incorporating both images and videos.
- Audio: Considering how sound-based data can be integrated with language models.
Large Language Model Backbones
The backbones are crucial as they form the foundational architecture upon which further developments and refinements are carried out. Some of the notable backbones highlighted in this project include:
- LLaMA, Alpaca, Vicuna, Bloom, GLM, and OPT.
- The emphasis is on models that are open-source and conducive to research, with an allowance for smaller backbones like BART and T5.
Learning Techniques
Several sophisticated techniques are employed to ensure effective learning within these models:
- Full Fine-Tuning: Refining all the parameters of a model to adapt to specific data.
- Parameter-Efficient Tuning: Methods like Adapter and LoRA that adjust only specific parameters for more targeted learning.
- In-Context Learning and Instruction Tuning: Techniques that enable models to understand and execute tasks through given instructions or examples.
Notable Models and Examples
The project highlights several examples of LLM-guided multimodal models and their evaluation techniques. Some prominent examples include:
- OpenFlamingo, MiniGPT-4, Otter, InstructBLIP, BLIVA: These models demonstrate different ways of integrating multimodal inputs for improved language understanding and generation.
- Evaluation on Multimodal LLM: Techniques such as MultiInstruct and POPE assess how well these models perform across different modalities.
Featured Research Papers
Numerous papers contribute to the depth of research and development within this project. Notable mentions among recent publications include:
- BLIVA: Focuses on better handling text-rich visual questions, utilizing backbones like Vicuna-7B and Flan-T5-XXL.
- LLaVA-Med: A quick training method for biomedicine-related language and vision tasks, based on Vicuna-13B.
Tools and Resources
The project also provides a host of useful links and resources for further exploration. These include lists of LLM backbones and vision backbones, open-source LLMs, and multimodal learning toolkits.
Contributions and Community
The project invites contributions from researchers and enthusiasts in the field. Contributions can be made in the form of updates to research papers, developments in models, or any new information about LLM-guided multimodal learning from platforms like Twitter.
Conclusion
The Awesome-Multimodal-LLM project serves as a comprehensive resource for anyone interested in the intersection of large language models and multimodal learning, offering insights into the current research landscape, details about specific models, and avenues for further exploration and contribution.