Summary of A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation, by Steven Landgraf et al.
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation
by Steven Landgraf, Markus Hillemann, Theodor Kapler, Markus Ulrich
First submitted to arxiv on: 27 May 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 The abstract proposes evaluating three methods – Monte Carlo Dropout, Deep Sub-Ensembles, and Deep Ensembles – for joint semantic segmentation and monocular depth estimation in deep neural networks. These models are crucial in safety-critical applications like autonomous driving and industrial inspection, but often suffer from overconfidence and poor explainability, particularly with out-of-domain data. The study reveals that Deep Ensembles perform best in out-of-domain scenarios and highlights the benefits of multi-task learning for uncertainty quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to make deep neural networks better at tasks like recognizing objects and estimating distances. These models are super important for things like self-driving cars and inspecting factories, but sometimes they get too confident or don’t explain themselves well. The researchers look at three ways to fix this: Monte Carlo Dropout, Deep Sub-Ensembles, and Deep Ensembles. They find that the last one works best when dealing with new or unexpected data. |
Keywords
» Artificial intelligence » Depth estimation » Dropout » Multi task » Semantic segmentation
