Summary of Geopos: a Minimal Positional Encoding For Enhanced Fine-grained Details in Image Synthesis Using Convolutional Neural Networks, by Mehran Hosseini and Peyman Hosseini
GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks
by Mehran Hosseini, Peyman Hosseini
First submitted to arxiv on: 3 Jan 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 tackles the long-standing issue of image generative models struggling to recreate intricate geometric features, such as those found in human hands and fingers. Despite advancements in model sizes and training datasets, all models – including denoising diffusion models, Generative Adversarial Networks (GAN), and Variational AutoEncoders (VAE) – continue to fall short. The authors propose augmenting convolution layers with a single input channel that incorporates the relative n-dimensional Cartesian coordinate system, significantly improving image quality for these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to draw a picture of your hand, but the tools just aren’t cooperating. That’s basically what’s happening in the world of artificial intelligence when it comes to generating images of hands and fingers. For years, scientists have been working on ways to make computers better at creating realistic pictures, but they’ve hit a roadblock. They’ve tried making their models bigger and more diverse, but nothing seems to be working. This paper tries to fix the problem by giving computer vision tools an extra set of instructions that will help them understand how things are arranged in space. |
Keywords
* Artificial intelligence * Diffusion * Gan
