Summary of Inference Optimal Vlms Need Only One Visual Token but Larger Models, by Kevin Y. Li et al.
Inference Optimal VLMs Need Only One Visual Token but Larger Models
by Kevin Y. Li, Sachin Goyal, Joao D. Semedo, J. Zico Kolter
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores the trade-off between Vision Language Models (VLMs) and token compression in visual reasoning tasks. VLMs have shown strong capabilities, but their real-world deployment is limited by high latency during inference due to the large number of input tokens from images. The authors investigate the optimal balance between LLM parameters and visual token count to reduce inference costs. They establish scaling laws that capture variations in performance with these factors and reveal a surprising trend: for visual reasoning tasks, the inference-optimal behavior is achieved by using the largest LLM while minimizing visual token count – often to a single token. The authors also highlight the need for approaches tailored to high token compression settings. This research has implications for the development of VLMs that can be efficiently deployed in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re using a computer to understand and make decisions about images, like recognizing objects or scenes. This is called visual understanding. The problem is that these computers need a lot of power to do this, which makes them slow and expensive. Researchers want to find ways to make these computers faster and cheaper by reducing the amount of information they process. They discovered that using fewer “tokens” (small pieces of information) from images can actually improve performance when combined with more powerful computer models. This means we might be able to build better image recognition systems that are also fast and affordable. |
Keywords
» Artificial intelligence » Inference » Scaling laws » Token
