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Learning to Segment Human Body Parts with Synthetically Trained Deep
  Convolutional Networks
v1v2v3 (latest)

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
ArXiv (abs)PDFHTML

Papers citing "Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks"

16 / 16 papers shown
Title
Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection
Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection
Fanqing Lin
Brian Price
Tony R. Martinez
EgoV
68
16
0
14 Nov 2020
Hierarchical Multi-Scale Attention for Semantic Segmentation
Hierarchical Multi-Scale Attention for Semantic Segmentation
Andrew Tao
Karan Sapra
Bryan Catanzaro
SSeg
75
452
0
21 May 2020
Building an Aerial-Ground Robotics System for Precision Farming: An
  Adaptable Solution
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
Alberto Pretto
Stéphanie Aravecchia
Wolfram Burgard
Nived Chebrolu
C. Dornhege
...
C. Stachniss
Achim Walter
W. Winterhalter
Xiaolong Wu
Juan I. Nieto
62
92
0
08 Nov 2019
Learning Multi-scale Features for Foreground Segmentation
Learning Multi-scale Features for Foreground Segmentation
Long Ang Lim
H. Keles
SSeg
52
179
0
04 Aug 2018
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zongwei Zhou
M. R. Siddiquee
Nima Tajbakhsh
Jianming Liang
SSeg
105
6,162
0
18 Jul 2018
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by
  Domain Randomization
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Jonathan Tremblay
Aayush Prakash
David Acuna
M. Brophy
Varun Jampani
Cem Anil
Thang To
Eric Cameracci
Shaad Boochoon
Stan Birchfield
OOD
78
818
0
18 Apr 2018
On Pre-Trained Image Features and Synthetic Images for Deep Learning
On Pre-Trained Image Features and Synthetic Images for Deep Learning
Stefan Hinterstoißer
Vincent Lepetit
Paul Wohlhart
K. Konolige
VLMObjD
47
230
0
29 Oct 2017
Dual Path Networks
Dual Path Networks
Yunpeng Chen
Jianan Li
Huaxin Xiao
Xiaojie Jin
Shuicheng Yan
Jiashi Feng
91
830
0
06 Jul 2017
Rethinking Atrous Convolution for Semantic Image Segmentation
Rethinking Atrous Convolution for Semantic Image Segmentation
Liang-Chieh Chen
George Papandreou
Florian Schroff
Hartwig Adam
SSeg
232
8,488
0
17 Jun 2017
LinkNet: Exploiting Encoder Representations for Efficient Semantic
  Segmentation
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
Abhishek Chaurasia
Eugenio Culurciello
SSeg
76
1,384
0
14 Jun 2017
Domain Randomization for Transferring Deep Neural Networks from
  Simulation to the Real World
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Joshua Tobin
Rachel Fong
Alex Ray
Jonas Schneider
Wojciech Zaremba
Pieter Abbeel
259
2,972
0
20 Mar 2017
Automatic Model Based Dataset Generation for Fast and Accurate Crop and
  Weeds Detection
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
M. D. Cicco
Ciro Potena
Giorgio Grisetti
Alberto Pretto
62
124
0
09 Dec 2016
Pyramid Scene Parsing Network
Pyramid Scene Parsing Network
Hengshuang Zhao
Jianping Shi
Xiaojuan Qi
Xiaogang Wang
Jiaya Jia
VOSSSeg
665
12,033
0
04 Dec 2016
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet
MDEBDLPINN
1.4K
14,608
0
07 Oct 2016
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated
  Annotations for Real World Tasks?
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
117
617
0
06 Oct 2016
Holistically-Nested Edge Detection
Holistically-Nested Edge Detection
Saining Xie
Zhuowen Tu
144
3,495
0
24 Apr 2015
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