Semantic segmentation algorithm for foggy cityscapes images by fusing self-supervised contrastive learning
Image Processing|更新时间:2024-07-26
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Semantic segmentation algorithm for foggy cityscapes images by fusing self-supervised contrastive learning
“In the field of semantic segmentation of foggy street scenes, researchers have proposed an algorithm that integrates self supervised contrastive learning, significantly improving the recognition and segmentation capabilities of the model.”
Chinese Journal of Liquid Crystals and DisplaysVol. 39, Issue 7, Pages: 990-1000(2024)
作者机构:
长春工业大学 计算机科学与工程学院, 吉林 长春 130012
作者简介:
基金信息:
National Natural Science Foundation of China(61803043);Project of Science and Technology Department of Jilin Province(YDZJ202201ZYTS414)
LIU Liwei, WANG Rui, MENG Xutao. Semantic segmentation algorithm for foggy cityscapes images by fusing self-supervised contrastive learning[J]. Chinese journal of liquid crystals and displays, 2024, 39(7): 990-1000.
DOI:
LIU Liwei, WANG Rui, MENG Xutao. Semantic segmentation algorithm for foggy cityscapes images by fusing self-supervised contrastive learning[J]. Chinese journal of liquid crystals and displays, 2024, 39(7): 990-1000. DOI: 10.37188/CJLCD.2023-0200.
Semantic segmentation algorithm for foggy cityscapes images by fusing self-supervised contrastive learning