Summary of Zipvl: Efficient Large Vision-language Models with Dynamic Token Sparsification, by Yefei He et al.
ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification
by Yefei He, Feng Chen, Jing Liu, Wenqi Shao, Hong Zhou, Kaipeng Zhang, Bohan Zhuang
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents ZipVL, an efficient inference framework for large vision-language models (LVLMs) that addresses both the attention mechanism’s computational bottleneck and the key-value cache’s memory bottleneck. The authors leverage the sparsity of attention maps in LVLMs to accelerate computation or compress the KV cache through a dynamic ratio allocation strategy based on layer-specific attention score distributions. This approach improves efficiency for less complex tasks while maintaining high performance for more challenging ones. The framework also includes a sparse attention mechanism that reduces latency and discards less important tokens to alleviate memory bottlenecks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ZipVL is a new way to make large vision-language models work faster and use less memory. These models are used in applications like image recognition and language translation. Right now, they can be slow because the model has to look at lots of information it doesn’t need. The authors created a way to help the model decide what’s important and focus on that. This makes the model work 2.3 times faster and use less memory. |
Keywords
» Artificial intelligence » Attention » Inference » Translation
