Sunday, June 06, 2021

FCN(二):Overview

FCN(二):Overview

2020/12/25

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https://pixabay.com/zh/photos/dog-cat-pets-animals-friends-2606759/

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◎ Abstract

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◎ Introduction

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本論文要解決(它之前研究)的(哪些)問題(弱點)? 

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# SDS。

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◎ Method

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解決方法? 

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# FCN

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具體細節?

https://hemingwang.blogspot.com/2021/04/fcnillustrated.html

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◎ Result

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本論文成果。 

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◎ Discussion

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本論文與其他論文(成果或方法)的比較。 

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成果比較。 

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方法比較。 

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◎ Conclusion 

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◎ Future Work

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後續相關領域的研究。 


# U-Net

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後續延伸領域的研究。


# Panoptic Segmentation。

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◎ References

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# SDS。被引用 983 次。

Hariharan, Bharath, et al. "Simultaneous detection and segmentation." European Conference on Computer Vision. Springer, Cham, 2014.

https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/papers/BharathECCV2014.pdf


# FCN。被引用 19356 次。

Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf


# Panoptic Segmentation

Kirillov, Alexander, et al. "Panoptic segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.

https://openaccess.thecvf.com/content_CVPR_2019/papers/Kirillov_Panoptic_Segmentation_CVPR_2019_paper.pdf


# PFPNet。被引用 171 次。

Kirillov, Alexander, et al. "Panoptic feature pyramid networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.

https://openaccess.thecvf.com/content_CVPR_2019/papers/Kirillov_Panoptic_Feature_Pyramid_Networks_CVPR_2019_paper.pdf


# Superpixel FCN

Yang, Fengting, et al. "Superpixel segmentation with fully convolutional networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Superpixel_Segmentation_With_Fully_Convolutional_Networks_CVPR_2020_paper.pdf


# II Superpixel FCN

Suzuki, Teppei. "Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks." arXiv preprint arXiv:2103.03435 (2021).

https://arxiv.org/pdf/2103.03435.pdf

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