Summary of Personalizing Multimodal Large Language Models For Image Captioning: An Experimental Analysis, by Davide Bucciarelli et al.
Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis
by Davide Bucciarelli, Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether Large Language Models (LLMs) and Multimodal LLMs, such as GPT-4V and Gemini, can surpass traditional image captioning networks by evaluating their performance on various benchmarks. The authors explore the zero-shot capabilities of these models and their adaptability to different semantic domains through fine-tuning methods like prompt learning, prefix tuning, and low-rank adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that Multimodal LLMs can do a great job with image captioning tasks without any extra training, but they struggle to adapt to new domains while keeping their general abilities. The results have implications for future research in image captioning and the development of more flexible Multimodal LLMs. |
Keywords
» Artificial intelligence » Fine tuning » Gemini » Gpt » Image captioning » Low rank adaptation » Prompt » Zero shot
