Summary of Semantic-rearrangement-based Multi-level Alignment For Domain Generalized Segmentation, by Guanlong Jiao et al.
Semantic-Rearrangement-Based Multi-Level Alignment for Domain Generalized Segmentation
by Guanlong Jiao, Chenyangguang Zhang, Haonan Yin, Yu Mo, Biqing Huang, Hui Pan, Yi Luo, Jingxian Liu
First submitted to arxiv on: 21 Apr 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 This paper proposes a new approach to domain generalized semantic segmentation, a crucial computer vision task where models must adapt to unseen target domains using only source data. The authors argue that previous methods focusing on global operations fail to capture regional discrepancies between the source and target domains, leading to inconsistent representations. To overcome this issue, they introduce the Semantic-Rearrangement-based Multi-Level Alignment (SRMA) framework. SRMA consists of a Semantic Rearrangement Module (SRM) that randomizes semantic regions in the source domain, and a Multi-Level Alignment module (MLA) that aligns features across randomized samples at multiple levels. This approach provides a more robust way to handle the source-target domain gap, as demonstrated through extensive experiments on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Domain generalized semantic segmentation is an important computer vision task that requires models to adapt to new environments using only training data from another environment. The paper proposes a new method called SRMA (Semantic-Rearrangement-based Multi-Level Alignment) to solve this problem. SRMA works by randomizing the source domain’s semantic regions and then aligning features across these randomized samples at multiple levels. This helps models learn more consistent representations that can handle differences between environments. |
Keywords
» Artificial intelligence » Alignment » Semantic segmentation
