Summary of Language-guided Instance-aware Domain-adaptive Panoptic Segmentation, by Elham Amin Mansour et al.
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation
by Elham Amin Mansour, Ozan Unal, Suman Saha, Benjamin Bejar, Luc Van Gool
First submitted to arxiv on: 4 Apr 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 The paper proposes a novel instance-aware cross-domain mixing strategy (IMix) to improve panoptic quality in unsupervised domain adaptation (UDA) for autonomous driving and AR/VR applications. IMix enhances instance segmentation performance by inserting high-confidence predicted instances from the target domain onto source images, while reducing injected confirmation bias. However, this comes at the cost of degraded semantic performance due to catastrophic forgetting. To mitigate this issue, the authors employ CLIP-based domain alignment (CDA) to regularize their semantic branch and achieve state-of-the-art results on popular panoptic UDA benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us create better models for self-driving cars and virtual reality games by making them work well in different situations without needing lots of labeled data. It does this by mixing information from a labeled source and an unlabeled target domain to improve the accuracy of object detection. This is important because it can help make autonomous vehicles safer and more reliable. |
Keywords
» Artificial intelligence » Alignment » Domain adaptation » Instance segmentation » Object detection » Unsupervised
