Efficient Parallel Multi-Scale Detail and Semantic Encoding Network for Lightweight Semantic Segmentation-Reference-Cited by-同舟云学术

Efficient Parallel Multi-Scale Detail and Semantic Encoding Network for Lightweight Semantic Segmentation

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Liu Xiao¹^ORCID,Shi Xiuya¹^ORCID,Chen Lufei¹^ORCID,Qing Linbo¹^ORCID,Ren Chao¹^ORCID

Affiliation:

1. Sichuan University, Chengdu, China

Funder

Cooperation Science and Technology Project of Sichuan University and Dazhou City

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611848

Reference40 articles.

1. Vijay Badrinarayanan , Alex Kendall , and Roberto Cipolla . 2017 . Segnet: A deep convolutional encoder-decoder architecture for image segmentation . IEEE transactions on pattern analysis and machine intelligence, Vol. 39 , 12 (2017), 2481--2495. Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, Vol. 39, 12 (2017), 2481--2495.

2. Liang-Chieh Chen , George Papandreou , Iasonas Kokkinos , Kevin Murphy , and Alan L Yuille . 2014. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 ( 2014 ). Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2014. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 (2014).

3. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

4. Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes

5. Rethinking BiSeNet For Real-time Semantic Segmentation