Summary of Large Language Models Can Understanding Depth From Monocular Images, by Zhongyi Xia and Tianzhao Wu
Large Language Models Can Understanding Depth from Monocular Images
by Zhongyi Xia, Tianzhao Wu
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper demonstrates that large language models (LLMs) can effectively estimate depth from monocular images with minimal supervision, using efficient resource utilization and a consistent neural network architecture. The LLM-MDE framework employs two strategies: cross-modal reprogramming and an adaptive prompt estimation module to align vision representations with text prototypes and generate prompts based on monocular images. Experiments on real-world datasets confirm the effectiveness and superiority of LLM-MDE in few- and zero-shot tasks while minimizing resource use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can estimate depth from monocular images with minimal supervision, using efficient resource utilization and a consistent neural network architecture. This is useful for computer vision applications. The LLM-MDE framework uses two strategies to enhance the pretrained LLM’s capability for depth estimation. The results show that this method excels in few- and zero-shot tasks while minimizing resource use. |
Keywords
» Artificial intelligence » Depth estimation » Neural network » Prompt » Zero shot
