MMPL-Net: multi-modal prototype learning for one-shot RGB-D segmentation-Reference-Cited by-同舟云学术

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MMPL-Net: multi-modal prototype learning for one-shot RGB-D segmentation

Published:2023-02-28 Issue:14 Volume:35 Page:10297-10310
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Shan Dexing,Zhang Yunzhou^ORCID,Liu Xiaozheng,Liu Shitong,Coleman Sonya A.,Kerr Dermot

Funder

National Natural Science Foundation of China

Major Science and Technology Projects of Liaoning Province

Fundation of Key Laboratory of Equipment Reliability

Fundation of Key Laboratory of Aerospace System Simulation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-08235-3.pdf

Reference66 articles.

1. Bao Y et al (2021) Visible and thermal images fusion architecture for few-shot semantic segmentation. J Vis Commun Image Represent 80:103306. https://doi.org/10.1016/j.jvcir.2021.103306

2. Bachmann R, Mizrahi D, Atanov A, Zamir A (2022) Multimae: Multi-modal multi-task masked autoencoders. arXiv preprint arXiv:2204.01678

3. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

4. Cai Z, Shao L (2017) Rgb-d data fusion in complex space. In: 2017 IEEE International Conference on Image Processing (ICIP), pp 1965–1969

5. Cao J, Leng H, Lischinski D, Cohen-Or D, Tu C, Li Y (2021) Shapeconv: shape-aware convolutional layer for indoor rgb-d semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 7088–7097

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