Summary of Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation, by Shuting Zhao et al.
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation
by Shuting Zhao, Chenkang Du, Kristin Qi, Xinrong Chen, Xinhan Di
First submitted to arxiv on: 1 Oct 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 This paper proposes a new learning framework for endoscopic depth estimation that adapts depth foundation models using full-parameter and parameter-efficient methods. The approach involves adapting the subspace of attention, convolution, and multi-layer perception simultaneously within different sub-spaces. A memory-efficient optimization is also proposed to further improve performance in the united subspace. Initial experiments on the SCARED dataset show significant improvements over state-of-the-art models, with Sq Rel, Abs Rel, RMSE, and RMSE log metrics improving from 10.2% to 4.1%. The framework’s full-parameter adaptation allows for more extensive exploration of the model’s parameter space, leading to better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops new ways to make depth estimation models work better for endoscopy. Currently, these models are limited because they only search a small part of their possible settings and change how they learn during training. The authors propose a new approach that looks at all the model’s settings and makes adjustments for better results. They test this on a dataset called SCARED and see big improvements in the quality of the estimates. This is important for doctors to get accurate pictures of what’s going inside people. |
Keywords
» Artificial intelligence » Attention » Depth estimation » Optimization » Parameter efficient
