Summary of Token Turing Machines Are Efficient Vision Models, by Purvish Jajal et al.
Token Turing Machines are Efficient Vision Models
by Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou, George K. Thiruvathukal, James C. Davis, Yung-Hsiang Lu
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposes Vision Token Turing Machines (ViTTMs), an efficient and low-latency, memory-augmented Vision Transformer (ViT) for non-sequential computer vision tasks like image classification and segmentation. Building on Neural Turing Machines and Token Turing Machines, ViTTMs create two token sets: process tokens that pass through encoder blocks and read-write from memory tokens, allowing information storage and retrieval. This approach reduces inference time while maintaining accuracy. The authors compare their ViTTM-B model to the state-of-the-art ViT-B on ImageNet-1K, achieving 56% faster median latency (234.1ms) with an accuracy of 82.9%, and on ADE20K semantic segmentation, they achieve a higher mIoU (45.17) at 26.8 FPS (+94%). The paper contributes to the development of efficient computer vision models for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make computers understand images and videos more efficiently. They called it Vision Token Turing Machines, or ViTTMs. It’s like a special kind of computer that can remember things from earlier and use that information to help with tasks like recognizing objects in pictures. The authors tested their idea on big image databases and found that it was faster and just as accurate as other state-of-the-art models. This could be useful for many applications, such as self-driving cars or medical imaging. |
Keywords
* Artificial intelligence * Encoder * Image classification * Inference * Semantic segmentation * Token * Vision transformer * Vit
