Summary of Pipa: Pixel- and Patch-wise Self-supervised Learning For Domain Adaptative Semantic Segmentation, by Mu Chen et al.
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
by Mu Chen, Zhedong Zheng, Yi Yang, Tat-Seng Chua
First submitted to arxiv on: 14 Nov 2022
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 PiPa framework for domain adaptive semantic segmentation tackles a crucial gap in existing Unsupervised Domain Adaptation (UDA) methods by leveraging intra-image pixel-wise correlations and patch-wise semantic consistency against different contexts. The framework, which combines self-supervised learning with pixel- and patch-wise features, aims to extract domain-invariant knowledge from labeled source domains like video games and transfer it to unlabeled target domains like real-world scenarios, reducing annotation expenses. PiPa’s unique approach involves encouraging discriminative pixel-wise features with intra-class compactness and inter-class separability while motivating robust feature learning of identical patches against different contexts or fluctuations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The PiPa framework for domain adaptive semantic segmentation is a new way to improve the generalization of learned models to other domains. It helps computers understand images better by looking at how pixels work together within an image, rather than just focusing on how they differ between images. This makes it easier for computers to recognize objects in real-world scenarios without needing lots of labeled data. |
Keywords
» Artificial intelligence » Domain adaptation » Generalization » Self supervised » Semantic segmentation » Unsupervised
