Zhang, Z.; Shu, D.; Gu, G.; Hu, W.; Wang, R.; Chen, X.; Yang, B.
RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery. Remote Sens. 2025, 17, 3064.
https://doi.org/10.3390/rs17173064
AMA Style
Zhang Z, Shu D, Gu G, Hu W, Wang R, Chen X, Yang B.
RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery. Remote Sensing. 2025; 17(17):3064.
https://doi.org/10.3390/rs17173064
Chicago/Turabian Style
Zhang, Zhan, Daoyu Shu, Guihe Gu, Wenkai Hu, Ru Wang, Xiaoling Chen, and Bingnan Yang.
2025. "RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery" Remote Sensing 17, no. 17: 3064.
https://doi.org/10.3390/rs17173064
APA Style
Zhang, Z., Shu, D., Gu, G., Hu, W., Wang, R., Chen, X., & Yang, B.
(2025). RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery. Remote Sensing, 17(17), 3064.
https://doi.org/10.3390/rs17173064