Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis
Davide Bucciarelli, Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara·December 04, 2024
Summary
The paper evaluates Multimodal Large Language Models (LLMs) for image captioning, comparing their zero-shot and fine-tuning capabilities. Results show impressive zero-shot performance, but maintaining generalization while adapting to specific domains through fine-tuning remains challenging. The study discusses implications for future research in image captioning and the development of more adaptable Multimodal LLMs.
Introduction
Background
Overview of Multimodal Large Language Models (LLMs)
Importance of image captioning in AI applications
Objective
To assess the zero-shot and fine-tuning capabilities of Multimodal LLMs in image captioning
To identify challenges in maintaining generalization and adapting to specific domains
Method
Data Collection
Selection of datasets for image captioning tasks
Description of image and text data used
Data Preprocessing
Techniques for preparing images and text for model training
Handling of data for zero-shot and fine-tuning scenarios
Results
Zero-shot Performance
Evaluation metrics for zero-shot image captioning
Analysis of model performance without domain-specific training
Fine-tuning Challenges
Strategies for fine-tuning Multimodal LLMs on specific domains
Assessment of generalization vs. domain adaptation
Discussion
Implications for Future Research
Directions for improving Multimodal LLMs in image captioning
Potential for enhancing model adaptability and generalization
Challenges and Limitations
Analysis of current limitations in Multimodal LLMs for image captioning
Suggestions for overcoming challenges in fine-tuning and generalization
Conclusion
Summary of Findings
Recap of zero-shot and fine-tuning performance
Future Directions
Recommendations for future research in Multimodal LLMs for image captioning
Outlook on advancements in model adaptability and generalization
Basic info
papers
computer vision and pattern recognition
computation and language
multimedia
artificial intelligence
Advanced features