Summary of Tokenunify: Scalable Autoregressive Visual Pre-training with Mixture Token Prediction, by Yinda Chen et al.
TokenUnify: Scalable Autoregressive Visual Pre-training with Mixture Token Prediction
by Yinda Chen, Haoyuan Shi, Xiaoyu Liu, Te Shi, Ruobing Zhang, Dong Liu, Zhiwei Xiong, Feng Wu
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel pretraining method, TokenUnify, is introduced to address the challenges of applying autoregressive next-token prediction to vision tasks. The method integrates random token prediction, next-token prediction, and next-all token prediction to mitigate cumulative errors in visual autoregression. Theoretical evidence demonstrates the effectiveness of TokenUnify in reducing errors. A large-scale electron microscopy (EM) image dataset is assembled, providing a unified benchmark for experimental validation. Leveraging the Mamba network, TokenUnify reduces computational complexity and improves segmentation performance by 45% on downstream EM neuron segmentation tasks compared to existing methods. The method also demonstrates superior scalability over MAE and traditional autoregressive methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TokenUnify is a new way to train computers to see better. Right now, it’s hard to use language models to understand pictures because the data isn’t sequential like words are. This makes mistakes add up quickly. Some people try using masked autoencoders (MAEs) but they can be slow and don’t work well with long sequences of data. TokenUnify combines three different types of predictions to fix these problems. It also comes with a huge dataset of tiny images called electron microscopy (EM) images that are great for testing how well computers can understand pictures. The method is better than others at both understanding EM images and being fast and efficient. |
Keywords
» Artificial intelligence » Autoregressive » Mae » Pretraining » Token
