Summary of Priordiffusion: Leverage Language Prior in Diffusion Models For Monocular Depth Estimation, by Ziyao Zeng et al.
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation
by Ziyao Zeng, Jingcheng Ni, Daniel Wang, Patrick Rim, Younjoon Chung, Fengyu Yang, Byung-Woo Hong, Alex Wong
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 explores the potential of leveraging language priors learned by text-to-image diffusion models to address ambiguity and visual nuisance in monocular depth estimation. The authors argue that language prior can enhance monocular depth estimation by leveraging geometric prior aligned with language description, which is learned during text-to-image pre-training. They propose PriorDiffusion, a pre-trained text-to-image diffusion model that takes both image and text description to infer affine-invariant depth through denoising process. This approach achieves state-of-the-art zero-shot performance and faster convergence speed compared to other diffusion-based depth estimators on NYUv2, KITTI, ETH3D, and ScanNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of AI model that can understand language and images. It tries to make computers better at understanding the world by using what it has learned from text and images. The authors think that this way of learning can help computers be more accurate when trying to figure out how deep things are in an image, even if they don’t have all the information. |
Keywords
» Artificial intelligence » Depth estimation » Diffusion » Diffusion model » Zero shot
