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2102.01460
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Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks
2 February 2021
Alessandro Saviolo
Matteo Bonotto
D. Evangelista
Marco Imperoli
Jacopo Lazzaro
Emanuele Menegatti
Alberto Pretto
3DH
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Papers citing
"Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks"
16 / 16 papers shown
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Stéphanie Aravecchia
Wolfram Burgard
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C. Stachniss
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H. Keles
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04 Aug 2018
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
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M. R. Siddiquee
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18 Jul 2018
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
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Aayush Prakash
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Cem Anil
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Eric Cameracci
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Stan Birchfield
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On Pre-Trained Image Features and Synthetic Images for Deep Learning
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Vincent Lepetit
Paul Wohlhart
K. Konolige
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29 Oct 2017
Dual Path Networks
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Jianan Li
Huaxin Xiao
Xiaojie Jin
Shuicheng Yan
Jiashi Feng
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06 Jul 2017
Rethinking Atrous Convolution for Semantic Image Segmentation
Liang-Chieh Chen
George Papandreou
Florian Schroff
Hartwig Adam
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17 Jun 2017
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
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Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Joshua Tobin
Rachel Fong
Alex Ray
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Pieter Abbeel
259
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20 Mar 2017
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
M. D. Cicco
Ciro Potena
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09 Dec 2016
Pyramid Scene Parsing Network
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Jianping Shi
Xiaojuan Qi
Xiaogang Wang
Jiaya Jia
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Xception: Deep Learning with Depthwise Separable Convolutions
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Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
Matthew Johnson-Roberson
Charlie Barto
Rounak Mehta
S. N. Sridhar
Karl Rosaen
Ram Vasudevan
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Holistically-Nested Edge Detection
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Zhuowen Tu
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