Introducing Awesome_Matching_Pretraining_Transfering
Overview
Awesome_Matching_Pretraining_Transfering is an insightful project dedicated to exploring various advanced topics within the realm of machine learning and artificial intelligence. This project serves as a continuously updated tutorial, providing a comprehensive look into areas such as Large Multi-Modality Models, Parameter-Efficient Finetuning, Vision-Language Pretraining, and Conventional Image-Text Matching. It is designed to impart a preliminary understanding to those interested in these cutting-edge technologies.
Log Updates
- 2024.07.11: An extensive update comprising over 50 new papers. Note that the Large Multi-Modality Model section is scheduled for further expansion.
- 2024.03.09: Introduction of a new section on Large Multi-Modality Models.
- 2023.05.25: Added a section focused on Parameter-Efficient Finetuning.
- 2021.07.10: Launch of the Vision-Language Pretraining section.
- 2020.11.01: Initiation of the Conventional Image-Text Matching section.
Detailed Catalogue
Large Multi-Modality Model
This section explores models that integrate multiple input types, such as text and images, to enhance understanding and generation capabilities.
- Large Language Model
- Large Vision Model
- Large Region Multimodal Model
- Large Image Multimodal Model
- Large Video Multimodal Model
- Large Model Distillation
- Includes related surveys and benchmarks for deeper insights.
Parameter-Efficient Finetuning
Learn about optimizing models with minimal parameter adjustments. This includes various tuning techniques that enhance performance without exhaustive resource use.
- Prompt Tuning
- Adapter Tuning
- Partially Tuning
- Side Tuning
- Unified Tuning
- Discover different application scenarios through published resources.
Vision-Language Pretraining
Focuses on pretraining models to deal with visual and textual data, aiding in the understanding and generation of multimodal content.
- Image-Language Pretraining
- Video-Language Pretraining
- Extensive datasets to support model training and validation.
Conventional Image-Text Matching
Addresses various techniques and challenges associated with linking textual and visual content, a cornerstone of computer vision and natural language processing.
- Generic-Feature Extraction
- Cross-Modal Interaction
- Similarity Measurement
- Advanced learning methodologies including adversarial and uncertainty learning.
- Performance analysis on datasets like Flickr and MSCOCO.
Additional Resources
- Large Foundation Model
- Multi-Modality Model
- Transfer Learning
- Graph Learning
- Fewshot Learning
These resources are invaluable for anyone interested in the theoretical and practical aspects of modern AI models.
Licensing and Contact
The project is licensed under the MIT license, ensuring open access and collaboration. For any inquiries or further information, contact via email at [email protected].
This project provides a gateway to the evolving landscape of machine learning, making it an essential resource for researchers, developers, and enthusiasts aiming to stay ahead in the fast-growing field of AI.