Summary of Freepruner: a Training-free Approach For Large Multimodal Model Acceleration, by Bingxin Xu et al.
freePruner: A Training-free Approach for Large Multimodal Model Acceleration
by Bingxin Xu, Yuzhang Shang, Yunhao Ge, Qian Lou, Yan Yan
First submitted to arxiv on: 23 Nov 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 A new approach to accelerating Large Multimodal Models (LMMs) without retraining or fine-tuning is proposed. The method, called freePruner, reduces computational demands by selectively removing tokens from the model while preserving important semantic and visual information. This is achieved through a two-stage token selection strategy that identifies pivotal tokens for high-level semantics and complementary tokens for low-level visual details. The approach demonstrates a 2x acceleration in training-free settings with comparable performance on mainstream visual question-answering benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Multimodal Models are super smart at understanding pictures, but they take up too many computer resources to use. Researchers have tried different ways to make them run faster, but most of these methods require a lot of extra work and training data. The new method, called freePruner, is special because it can be used on any Large Multimodal Model without needing more training or data. It works by carefully choosing which parts of the model are most important and keeping those, while getting rid of the less important parts. This makes the models run faster and use fewer computer resources, making them easier to use. |
Keywords
» Artificial intelligence » Fine tuning » Question answering » Semantics » Token
