Robust semantic segmentation method of urban scenes in snowy environment-Reference-Cited by-同舟云学术

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Robust semantic segmentation method of urban scenes in snowy environment

Published:2024-04-29 Issue:3 Volume:35 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Yin Hanqi,Yin Guisheng,Sun Yiming,Zhang Liguo,Tian Ye

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00138-024-01540-4.pdf

Reference52 articles.

1. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)

2. Kim, E., Medioni, G.: Urban scene understanding from aerial and ground lidar data. Mach. Vis. Appl. 22(4), 691–703 (2011)

3. Gupta, S., Dileep, A.D., Thenkanidiyoor, V.: Recognition of varying size scene images using semantic analysis of deep activation maps. Mach. Vis. Appl. 32(2), 52 (2021)

4. Tan, X., Xu, K., Cao, Y., Zhang, Y., Ma, L., Lau, R.W.: Night-time scene parsing with a large real dataset. IEEE Trans. Image Process. 30, 9085–9098 (2021)

5. Xie, Z., Wang, S., Xu, K., Zhang, Z., Tan, X., Xie, Y., Ma, L.: Boosting night-time scene parsing with learnable frequency. IEEE Trans. Image Process. 32, 2386–2398 (2023)

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