Summary of Efficient Multi-task Uncertainties For Joint Semantic Segmentation and Monocular Depth Estimation, by Steven Landgraf et al.
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
by Steven Landgraf, Markus Hillemann, Theodor Kapler, Markus Ulrich
First submitted to arxiv on: 16 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 This paper addresses the issue of overconfidence in deep neural networks by introducing a new approach to quantify predictive uncertainty. The authors demonstrate how combining different uncertainty quantification methods with joint semantic segmentation and monocular depth estimation can improve performance and robustness. They also propose EMUFormer, a novel student-teacher distillation method that leverages predictive uncertainties to achieve state-of-the-art results on Cityscapes and NYUv2 while estimating high-quality predictive uncertainties for both tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions by figuring out how much we can trust them. Right now, deep neural networks often get too confident in their answers, which isn’t helpful. To fix this, the researchers combined different ways to measure uncertainty with two important computer vision tasks: identifying objects and estimating depth from a single image. They showed that doing both tasks together helps improve results and makes it easier to understand why they’re making certain predictions. The authors then introduced a new approach called EMUFormer that can estimate these uncertainties efficiently while still achieving great results. |
Keywords
* Artificial intelligence * Depth estimation * Distillation * Semantic segmentation
