Summary of Language-based Depth Hints For Monocular Depth Estimation, by Dylan Auty and Krystian Mikolajczyk
Language-Based Depth Hints for Monocular Depth Estimation
by Dylan Auty, Krystian Mikolajczyk
First submitted to arxiv on: 22 Mar 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 A novel approach to monocular depth estimation (MDE) is proposed, leveraging natural language as an explicit prior about the structure of the world. The assumption is made that human language encodes the likely distribution in depth-space of various objects. A language model is trained and used to extract this implicit bias, which is then provided as an input to an instance segmentation model. This approach demonstrates improved performance on the NYUD2 dataset compared to a baseline and random controls. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a new way to help computers understand how deep objects are in pictures taken from one camera angle. They used language, like what we speak, to teach computers about the world. This helped the computer make better guesses about how far away things are. The team tested their idea on some old data and it worked better than other methods. |
Keywords
» Artificial intelligence » Depth estimation » Instance segmentation » Language model
