Summary of Visionzip: Longer Is Better but Not Necessary in Vision Language Models, by Senqiao Yang et al.
VisionZip: Longer is Better but Not Necessary in Vision Language Models
by Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, Jiaya Jia
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 introduces VisionZip, a method that reduces visual token redundancy in vision-language models, improving efficiency and performance. By selecting informative tokens for input to the language model, VisionZip outperforms previous state-of-the-art methods by at least 5% across nearly all settings. The proposed approach also significantly enhances model inference speed, enabling faster prefilling times. Moreover, the authors analyze the causes of visual token redundancy and encourage the community to focus on extracting better visual features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VisionZip is a new way to make vision-language models more efficient. These models are good at understanding pictures and videos, but they use a lot of computing power because they have lots of extra information in their “visual tokens”. The authors of this paper found that most of this extra information isn’t actually useful for the model’s job. So, they created VisionZip to pick out just the important parts of the visual tokens. This makes the models work faster and better. |
Keywords
» Artificial intelligence » Inference » Language model » Token
