Summary of Test-time Adaptation For Depth Completion, by Hyoungseob Park et al.
Test-Time Adaptation for Depth Completion
by Hyoungseob Park, Anjali Gupta, Alex Wong
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposed online test-time adaptation method for depth completion effectively closes the performance gap in a single pass, addressing the common issue of performance degradation when transferring models trained on source datasets to target testing data due to domain gaps. By preserving features encoding only sparse depth and projecting them as a proxy for source domain features during test time, the method improves model performance by an average of 21.1% over baselines in indoor and outdoor scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to improve how computers can understand images from different environments. Right now, computers struggle when trying to use models they were trained on earlier to understand new pictures. This is because the old training data might not be similar enough to the new picture. The scientists created a special “adapter” that helps computers learn from both old and new data at the same time. This way, computers can understand images better and make more accurate decisions. |
