Summary of Perception Tokens Enhance Visual Reasoning in Multimodal Language Models, by Mahtab Bigverdi et al.
Perception Tokens Enhance Visual Reasoning in Multimodal Language Models
by Mahtab Bigverdi, Zelun Luo, Cheng-Yu Hsieh, Ethan Shen, Dongping Chen, Linda G. Shapiro, Ranjay Krishna
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 introduces Perception Tokens, intrinsic image representations that assist reasoning tasks where language is insufficient. The authors propose AURORA, a training method that augments Multimodal Language Models (MLMs) with perception tokens for improved reasoning over visual inputs. This allows MLMs to produce intermediate depth or object detection outputs, enabling them to solve problems effectively. The method leverages a VQVAE to transform intermediate image representations into tokenized format and achieves notable improvements across counting benchmarks (+10.8% on BLINK, +11.3% on CVBench, and +8.3% on SEED-Bench). It also improves on relative depth (+6% on BLINK). The paper paves the way for more effective visual reasoning capabilities of MLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to help computers understand pictures better. Right now, computers can talk about what they see, but they struggle with tasks that require understanding 3D shapes or detecting objects in 2D images. To fix this, the authors created special tokens that give computers more information about the picture. These tokens act like prompts that help computers reason about what they see. The authors also developed a new way to train these tokens, called AURORA, which improves computers’ ability to count objects and understand depth. With this technology, computers can do many things better, like detect objects in pictures and understand 3D shapes. |
Keywords
» Artificial intelligence » Object detection
