Summary of A Spark Of Vision-language Intelligence: 2-dimensional Autoregressive Transformer For Efficient Finegrained Image Generation, by Liang Chen et al.
A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation
by Liang Chen, Sinan Tan, Zefan Cai, Weichu Xie, Haozhe Zhao, Yichi Zhang, Junyang Lin, Jinze Bai, Tianyu Liu, Baobao Chang
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 model architecture called the 2-Dimensional Autoregression (DnD) Transformer is introduced to address the information loss bottleneck of vector-quantization (VQ) autoregressive image generation. The DnD-Transformer predicts more codes for an image by incorporating a new autoregression direction, model depth, in addition to sequence length direction. Compared to traditional 1D autoregression and previous work utilizing similar 2D image decomposition such as RQ-Transformer, the DnD-Transformer is an end-to-end model that can generate higher quality images with the same backbone model size and sequence length, offering a new optimization perspective for autoregressive image generation. The proposed approach also demonstrates potential in generating images with rich text and graphical elements in a self-supervised manner, showcasing understanding of these combined modalities. This capability is unprecedented for popular vision generative models such as diffusion models when trained solely on images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The DnD-Transformer model helps to solve the problem of losing information during image generation by adding a new direction called “model depth”. This means the model can predict more codes for an image, resulting in higher quality images. The new approach is different from previous methods that use 2D image decomposition and can even generate images with text and graphics. |
Keywords
» Artificial intelligence » Autoregressive » Image generation » Optimization » Quantization » Self supervised » Transformer
