Summary of Scaling Properties Of Diffusion Models For Perceptual Tasks, by Rahul Ravishankar et al.
Scaling Properties of Diffusion Models for Perceptual Tasks
by Rahul Ravishankar, Zeeshan Patel, Jathushan Rajasegaran, Jitendra Malik
First submitted to arxiv on: 12 Nov 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 The paper presents a novel approach to visual perception tasks by leveraging iterative computation with diffusion models. The authors unify various tasks such as depth estimation, optical flow, and amodal segmentation under the framework of image-to-image translation, showcasing how diffusion models benefit from scaling training and test-time compute. The study formulates compute-optimal training and inference recipes for these perceptual tasks, achieving competitive performance to state-of-the-art methods using less data and compute. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores new ways for computers to understand visual information by combining two techniques: iterative computation and diffusion models. By uniting different tasks like seeing depth or motion in videos, the authors show how this combination can be powerful for understanding images. The study reveals that these models can work well even with less data and computer power. |
Keywords
» Artificial intelligence » Depth estimation » Diffusion » Inference » Optical flow » Translation
