Summary of Clip Can Understand Depth, by Dunam Kim et al.
CLIP Can Understand Depth
by Dunam Kim, Seokju Lee
First submitted to arxiv on: 5 Feb 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 A novel approach for monocular depth estimation leveraging CLIP’s pre-trained vision-language alignment is proposed. By adapting CLIP without fine-tuning its original alignment, the model learns to understand depth by jointly training a compact deconvolutional decoder with a tiny learnable embedding matrix named mirror as a static prompt. This approach yields impressive performance on NYU Depth v2 and KITTI datasets, outperforming previous CLIP-based models while matching state-of-the-art vision-only results. The study also demonstrates the effectiveness of refining prior knowledge through minimal adjustments to suboptimal foundation models like CLIP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a new way to use special AI models that can understand pictures and text together! Scientists took one of these models, called CLIP, and made some small changes to help it predict how deep things are in a picture. This is important because it helps the model learn from just one picture, not many like before. The team tested their new approach on two big datasets and found that it worked really well! They also showed that this way of using AI models can be helpful for other tasks too. |
Keywords
* Artificial intelligence * Alignment * Decoder * Depth estimation * Embedding * Fine tuning * Prompt
