Summary of Neuroexplicit Diffusion Models For Inpainting Of Optical Flow Fields, by Tom Fischer and Pascal Peter and Joachim Weickert and Eddy Ilg
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields
by Tom Fischer, Pascal Peter, Joachim Weickert, Eddy Ilg
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Deep learning has transformed computer vision by enabling large-scale neural networks with millions of parameters. However, training these models demands massive datasets and results in intransparent models that can struggle to generalize. In contrast, PDE-based approaches embed domain knowledge into mathematical equations and rely on few manually chosen hyperparameters, making them transparent by construction. This paper bridges the gap between model- and data-driven approaches by combining PDE-based methods with convolutional neural networks (CNNs) to obtain the best of both worlds. The proposed joint architecture is demonstrated for inpainting optical flow fields, showcasing that the combined approach outperforms fully explicit and fully data-driven baselines in terms of reconstruction quality, robustness, and required training data. Our method sets a new state-of-the-art for inpainting optical flow fields from random masks, outperforming GAN and Probabilistic Diffusion baselines by 47% and 42%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about combining two different approaches to computer vision: deep learning and math-based methods. Deep learning has made huge progress in this field, but it often requires a lot of data and can be tricky to understand. Math-based methods, on the other hand, are transparent and can work well with less data. The authors combine these two approaches to create a new way of doing computer vision that takes advantage of both strengths. They test their method by trying to fill in missing parts of optical flow fields (which are like movies of what’s moving in each frame). Their approach works better than other methods they tried, and it uses less training data. |
Keywords
» Artificial intelligence » Deep learning » Diffusion » Gan » Optical flow
