Monday, November 25, 2019

RefineNet

RefineNet

2019/11/18

前言:

RefineNet 可以參考 RefineDet 的兩路設計。首先第一路的卷積網路可以產生不同大小的特徵圖,再進入第二路的 Refine 模組運算。模組主要由殘差卷積單元、多解析融合、鍊式殘差池化、以及輸出層構成。

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# RefineNet

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# RefineDet

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# RefineNet

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// Review  RefineNet — Multi-path Refinement Network (Semantic Segmentation)

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# RefineNet

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References

# RefineNet
Lin, Guosheng, et al. "Refinenet: Multi-path refinement networks for high-resolution semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_RefineNet_Multi-Path_Refinement_CVPR_2017_paper.pdf 

# RefineDet
Zhang, Shifeng, et al. "Single-shot refinement neural network for object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single-Shot_Refinement_Neural_CVPR_2018_paper.pdf

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Review  RefineNet — Multi-path Refinement Network (Semantic Segmentation)
https://towardsdatascience.com/review-refinenet-multi-path-refinement-network-semantic-segmentation-5763d9da47c1

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语义分割之RefineNet - 知乎
https://zhuanlan.zhihu.com/p/37805109

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