HIGHLIGHTS
- who: Xunpeng Yi et al. from the Electronic Information School, Wuhan University, Wuhan, China have published the article: Swin-MFA: A Multi-Modal Fusion Attention Network Based on Swin-Transformer for Low-Light Image Human Segmentation, in the Journal: Sensors 2022, 22, x FOR PEER REVIEW of /2022/
- what: In Section 4.1, the authors compare Swin-MFA with various feature-fusion methods, and the experiment proves that the feature-fusion attention block performs better than other traditional methods. In Section 4.3, the authors compare the methods with classic image segmentation methods, such as . . .
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