Summary of From Training-free to Adaptive: Empirical Insights Into Mllms’ Understanding Of Detection Information, by Qirui Jiao et al.
From Training-Free to Adaptive: Empirical Insights into MLLMs’ Understanding of Detection Information
by Qirui Jiao, Daoyuan Chen, Yilun Huang, Yaliang Li, Ying Shen
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers investigate how training affects the comprehension of multimodal large language models (MLLMs) when infused with textual detection information. The authors examine various training strategies, including no training, retraining, and fine-tuning, to determine which approach leads to superior results. They find that fine-tuning a pre-trained MLLM improves performance by 6.71% across 10 benchmarks, enabling the model to retain enhancements even when detection models are swapped. The study also explores the interchangeability of detection models and releases codes for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well multimodal language models work with information from image recognition. It tries different ways to make these models better, like not training them at all or retraining them after they’re already trained. They find that making small changes to a model that’s already good can make it even better by 6.7%. This new knowledge can help us create language models that are really good at understanding lots of different types of information. |
Keywords
» Artificial intelligence » Fine tuning
