SlideShare a Scribd company logo

Human parsing

Seminar 27-01-2018 Human Parsing By Yawei Luo

1 of 19
Download to read offline
Human Parsing
Yawei Luo
Problem
description
 Human parsing aims to segment a human image into
multiple semantic parts.
 It is a pixel-wise parsing problem.
 It is a supervised machine learning problem.
Challenges
 Occluded (especially by other people)
 Multi-scale
 Cross-domain
 Label conflict
 Blurry
 Cavity
 …
Main conflict is the desire for both larger
field of view & more accurate location
(Deeper or Denser?)
}
}
Need larger field
of view
Need denser &
more accurate
location
Related works
 Atrous Convolution
e.g. Deeplab
Related works
 Atrous Convolution
e.g. Deeplab
Related works
 Skip Net
e.g. U-net (top)
FCN(bottom)

Recommended

HRNET : Deep High-Resolution Representation Learning for Human Pose Estimation
HRNET : Deep High-Resolution Representation Learning for Human Pose EstimationHRNET : Deep High-Resolution Representation Learning for Human Pose Estimation
HRNET : Deep High-Resolution Representation Learning for Human Pose Estimationtaeseon ryu
 
HardNet: Convolutional Network for Local Image Description
HardNet: Convolutional Network for Local Image DescriptionHardNet: Convolutional Network for Local Image Description
HardNet: Convolutional Network for Local Image DescriptionDmytro Mishkin
 
Review-image-segmentation-by-deep-learning
Review-image-segmentation-by-deep-learningReview-image-segmentation-by-deep-learning
Review-image-segmentation-by-deep-learningTrong-An Bui
 
Paper overview: "Deep Residual Learning for Image Recognition"
Paper overview: "Deep Residual Learning for Image Recognition"Paper overview: "Deep Residual Learning for Image Recognition"
Paper overview: "Deep Residual Learning for Image Recognition"Ilya Kuzovkin
 
Review on cs231 part-2
Review on cs231 part-2Review on cs231 part-2
Review on cs231 part-2Jeong Choi
 
Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural NetworksTianxiang Xiong
 
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Parsing Natural Scenes and Natural Language with Recursive Neural NetworksParsing Natural Scenes and Natural Language with Recursive Neural Networks
Parsing Natural Scenes and Natural Language with Recursive Neural Networksjie cao
 
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis taeseon ryu
 

More Related Content

What's hot

Pr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationPr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationTaeoh Kim
 
PR-108: MobileNetV2: Inverted Residuals and Linear Bottlenecks
PR-108: MobileNetV2: Inverted Residuals and Linear BottlenecksPR-108: MobileNetV2: Inverted Residuals and Linear Bottlenecks
PR-108: MobileNetV2: Inverted Residuals and Linear BottlenecksJinwon Lee
 
Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsLearning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsMathias Niepert
 
Object Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning FrameworkObject Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning FrameworkNader Karimi
 
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...Joonhyung Lee
 
Neural Network as a function
Neural Network as a functionNeural Network as a function
Neural Network as a functionTaisuke Oe
 
Deep Learning Tutorial
Deep Learning Tutorial Deep Learning Tutorial
Deep Learning Tutorial Ligeng Zhu
 
Mobilenetv1 v2 slide
Mobilenetv1 v2 slideMobilenetv1 v2 slide
Mobilenetv1 v2 slide威智 黃
 
Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)
Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)
Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)Universitat Politècnica de Catalunya
 
Tutorial on convolutional neural networks
Tutorial on convolutional neural networksTutorial on convolutional neural networks
Tutorial on convolutional neural networksHojin Yang
 
Modern Convolutional Neural Network techniques for image segmentation
Modern Convolutional Neural Network techniques for image segmentationModern Convolutional Neural Network techniques for image segmentation
Modern Convolutional Neural Network techniques for image segmentationGioele Ciaparrone
 
Offline Character Recognition Using Monte Carlo Method and Neural Network
Offline Character Recognition Using Monte Carlo Method and Neural NetworkOffline Character Recognition Using Monte Carlo Method and Neural Network
Offline Character Recognition Using Monte Carlo Method and Neural Networkijaia
 
PR243: Designing Network Design Spaces
PR243: Designing Network Design SpacesPR243: Designing Network Design Spaces
PR243: Designing Network Design SpacesJinwon Lee
 
Introduction to Convolutional Neural Networks
Introduction to Convolutional Neural NetworksIntroduction to Convolutional Neural Networks
Introduction to Convolutional Neural NetworksParrotAI
 
convolutional neural network (CNN, or ConvNet)
convolutional neural network (CNN, or ConvNet)convolutional neural network (CNN, or ConvNet)
convolutional neural network (CNN, or ConvNet)RakeshSaran5
 
Big Data Intelligence: from Correlation Discovery to Causal Reasoning
Big Data Intelligence: from Correlation Discovery to Causal Reasoning Big Data Intelligence: from Correlation Discovery to Causal Reasoning
Big Data Intelligence: from Correlation Discovery to Causal Reasoning Wanjin Yu
 
Understanding Convolutional Neural Networks
Understanding Convolutional Neural NetworksUnderstanding Convolutional Neural Networks
Understanding Convolutional Neural NetworksJeremy Nixon
 

What's hot (20)

Pr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentationPr045 deep lab_semantic_segmentation
Pr045 deep lab_semantic_segmentation
 
PR-108: MobileNetV2: Inverted Residuals and Linear Bottlenecks
PR-108: MobileNetV2: Inverted Residuals and Linear BottlenecksPR-108: MobileNetV2: Inverted Residuals and Linear Bottlenecks
PR-108: MobileNetV2: Inverted Residuals and Linear Bottlenecks
 
Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsLearning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for Graphs
 
Object Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning FrameworkObject Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning Framework
 
CNN
CNNCNN
CNN
 
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...
 
Neural Network as a function
Neural Network as a functionNeural Network as a function
Neural Network as a function
 
Deep Learning Tutorial
Deep Learning Tutorial Deep Learning Tutorial
Deep Learning Tutorial
 
Mobilenetv1 v2 slide
Mobilenetv1 v2 slideMobilenetv1 v2 slide
Mobilenetv1 v2 slide
 
Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)
Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)
Image Segmentation (D3L1 2017 UPC Deep Learning for Computer Vision)
 
Deep 3D Visual Analysis - Javier Ruiz-Hidalgo - UPC Barcelona 2017
Deep 3D Visual Analysis - Javier Ruiz-Hidalgo - UPC Barcelona 2017Deep 3D Visual Analysis - Javier Ruiz-Hidalgo - UPC Barcelona 2017
Deep 3D Visual Analysis - Javier Ruiz-Hidalgo - UPC Barcelona 2017
 
Cnn method
Cnn methodCnn method
Cnn method
 
Tutorial on convolutional neural networks
Tutorial on convolutional neural networksTutorial on convolutional neural networks
Tutorial on convolutional neural networks
 
Modern Convolutional Neural Network techniques for image segmentation
Modern Convolutional Neural Network techniques for image segmentationModern Convolutional Neural Network techniques for image segmentation
Modern Convolutional Neural Network techniques for image segmentation
 
Offline Character Recognition Using Monte Carlo Method and Neural Network
Offline Character Recognition Using Monte Carlo Method and Neural NetworkOffline Character Recognition Using Monte Carlo Method and Neural Network
Offline Character Recognition Using Monte Carlo Method and Neural Network
 
PR243: Designing Network Design Spaces
PR243: Designing Network Design SpacesPR243: Designing Network Design Spaces
PR243: Designing Network Design Spaces
 
Introduction to Convolutional Neural Networks
Introduction to Convolutional Neural NetworksIntroduction to Convolutional Neural Networks
Introduction to Convolutional Neural Networks
 
convolutional neural network (CNN, or ConvNet)
convolutional neural network (CNN, or ConvNet)convolutional neural network (CNN, or ConvNet)
convolutional neural network (CNN, or ConvNet)
 
Big Data Intelligence: from Correlation Discovery to Causal Reasoning
Big Data Intelligence: from Correlation Discovery to Causal Reasoning Big Data Intelligence: from Correlation Discovery to Causal Reasoning
Big Data Intelligence: from Correlation Discovery to Causal Reasoning
 
Understanding Convolutional Neural Networks
Understanding Convolutional Neural NetworksUnderstanding Convolutional Neural Networks
Understanding Convolutional Neural Networks
 

Similar to Human parsing

Batch normalization presentation
Batch normalization presentationBatch normalization presentation
Batch normalization presentationOwin Will
 
Resnet.pdf
Resnet.pdfResnet.pdf
Resnet.pdfYanhuaSi
 
Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018
Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018
Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Resnet.pptx
Resnet.pptxResnet.pptx
Resnet.pptxYanhuaSi
 
Recent Progress on Object Detection_20170331
Recent Progress on Object Detection_20170331Recent Progress on Object Detection_20170331
Recent Progress on Object Detection_20170331Jihong Kang
 
A Novel Approach to Image Denoising and Image in Painting
A Novel Approach to Image Denoising and Image in PaintingA Novel Approach to Image Denoising and Image in Painting
A Novel Approach to Image Denoising and Image in PaintingEswar Publications
 
最近の研究情勢についていくために - Deep Learningを中心に -
最近の研究情勢についていくために - Deep Learningを中心に - 最近の研究情勢についていくために - Deep Learningを中心に -
最近の研究情勢についていくために - Deep Learningを中心に - Hiroshi Fukui
 
Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...
Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...
Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...Universitat Politècnica de Catalunya
 
Efficient de cvpr_2020_paper
Efficient de cvpr_2020_paperEfficient de cvpr_2020_paper
Efficient de cvpr_2020_papershanullah3
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Gaurav Mittal
 
Deep Learning in Computer Vision
Deep Learning in Computer VisionDeep Learning in Computer Vision
Deep Learning in Computer VisionSungjoon Choi
 
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetup
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLucas Theis - Compressing Images with Neural Networks - Creative AI meetup
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLuba Elliott
 
Conception_et_realisation_dun_site_Web_d.pdf
Conception_et_realisation_dun_site_Web_d.pdfConception_et_realisation_dun_site_Web_d.pdf
Conception_et_realisation_dun_site_Web_d.pdfSofianeHassine2
 
ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...
ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...
ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...sipij
 
IEEE 2015 Matlab Projects
IEEE 2015 Matlab ProjectsIEEE 2015 Matlab Projects
IEEE 2015 Matlab ProjectsVijay Karan
 
An introduction to super resolution using deep learning
An introduction to super resolution using deep learningAn introduction to super resolution using deep learning
An introduction to super resolution using deep learningAnil Chandra Naidu Matcha
 
Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...
Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...
Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...Universitat Politècnica de Catalunya
 
IEEE 2015 Matlab Projects
IEEE 2015 Matlab ProjectsIEEE 2015 Matlab Projects
IEEE 2015 Matlab ProjectsVijay Karan
 

Similar to Human parsing (20)

Batch normalization presentation
Batch normalization presentationBatch normalization presentation
Batch normalization presentation
 
Resnet.pdf
Resnet.pdfResnet.pdf
Resnet.pdf
 
Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018
Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018
Transfer Learning and Domain Adaptation - Ramon Morros - UPC Barcelona 2018
 
Resnet.pptx
Resnet.pptxResnet.pptx
Resnet.pptx
 
Recent Progress on Object Detection_20170331
Recent Progress on Object Detection_20170331Recent Progress on Object Detection_20170331
Recent Progress on Object Detection_20170331
 
A Novel Approach to Image Denoising and Image in Painting
A Novel Approach to Image Denoising and Image in PaintingA Novel Approach to Image Denoising and Image in Painting
A Novel Approach to Image Denoising and Image in Painting
 
最近の研究情勢についていくために - Deep Learningを中心に -
最近の研究情勢についていくために - Deep Learningを中心に - 最近の研究情勢についていくために - Deep Learningを中心に -
最近の研究情勢についていくために - Deep Learningを中心に -
 
Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...
Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...
Transfer Learning and Domain Adaptation (DLAI D5L2 2017 UPC Deep Learning for...
 
Efficient de cvpr_2020_paper
Efficient de cvpr_2020_paperEfficient de cvpr_2020_paper
Efficient de cvpr_2020_paper
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)
 
Deep Learning in Computer Vision
Deep Learning in Computer VisionDeep Learning in Computer Vision
Deep Learning in Computer Vision
 
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetup
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLucas Theis - Compressing Images with Neural Networks - Creative AI meetup
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetup
 
Conception_et_realisation_dun_site_Web_d.pdf
Conception_et_realisation_dun_site_Web_d.pdfConception_et_realisation_dun_site_Web_d.pdf
Conception_et_realisation_dun_site_Web_d.pdf
 
ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...
ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...
ADVANCED SINGLE IMAGE RESOLUTION UPSURGING USING A GENERATIVE ADVERSARIAL NET...
 
Mnist report ppt
Mnist report pptMnist report ppt
Mnist report ppt
 
Mnist report
Mnist reportMnist report
Mnist report
 
IEEE 2015 Matlab Projects
IEEE 2015 Matlab ProjectsIEEE 2015 Matlab Projects
IEEE 2015 Matlab Projects
 
An introduction to super resolution using deep learning
An introduction to super resolution using deep learningAn introduction to super resolution using deep learning
An introduction to super resolution using deep learning
 
Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...
Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...
Interpretability of Convolutional Neural Networks - Eva Mohedano - UPC Barcel...
 
IEEE 2015 Matlab Projects
IEEE 2015 Matlab ProjectsIEEE 2015 Matlab Projects
IEEE 2015 Matlab Projects
 

Recently uploaded

dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...dkNET
 
Genetic Code. A comprehensive overview..pdf
Genetic Code. A comprehensive overview..pdfGenetic Code. A comprehensive overview..pdf
Genetic Code. A comprehensive overview..pdfmughalgumar440
 
Weak-lensing detection of intracluster filaments in the Coma cluster
Weak-lensing detection of intracluster filaments in the Coma clusterWeak-lensing detection of intracluster filaments in the Coma cluster
Weak-lensing detection of intracluster filaments in the Coma clusterSérgio Sacani
 
American Eclipse A Nation’s Epic Race to Catch the_240225_095603
American Eclipse A Nation’s Epic Race to Catch the_240225_095603American Eclipse A Nation’s Epic Race to Catch the_240225_095603
American Eclipse A Nation’s Epic Race to Catch the_240225_095603SOCIEDAD JULIO GARAVITO
 
Earth and Planetary Science | Volume 01 | Issue 01 | April 2022
Earth and Planetary Science | Volume 01 | Issue 01 | April 2022Earth and Planetary Science | Volume 01 | Issue 01 | April 2022
Earth and Planetary Science | Volume 01 | Issue 01 | April 2022Nan Yang Academy of Sciences
 
The ExoGRAVITY project - observations of exoplanets from the ground with opti...
The ExoGRAVITY project - observations of exoplanets from the ground with opti...The ExoGRAVITY project - observations of exoplanets from the ground with opti...
The ExoGRAVITY project - observations of exoplanets from the ground with opti...Advanced-Concepts-Team
 
Elbow joint - Anatomy of the Elbow joint
Elbow joint - Anatomy of the Elbow jointElbow joint - Anatomy of the Elbow joint
Elbow joint - Anatomy of the Elbow jointTELISHA2
 
Salesforce Starter Package Presentation.
Salesforce Starter Package Presentation.Salesforce Starter Package Presentation.
Salesforce Starter Package Presentation.Naresh Gupta
 
green chemistry, clean sustainable environment.ppt
green chemistry, clean sustainable environment.pptgreen chemistry, clean sustainable environment.ppt
green chemistry, clean sustainable environment.pptRashmiSanghi1
 
Seminario biología molecular Lina Charris
Seminario biología molecular Lina CharrisSeminario biología molecular Lina Charris
Seminario biología molecular Lina CharrisLinaMarcelaCharrisRa
 
commercial production of cellulase enzyme and its uses
commercial production of cellulase enzyme and its usescommercial production of cellulase enzyme and its uses
commercial production of cellulase enzyme and its usesSilpa Selvaraj
 
Volatile Oils-Introduction for pharmacy students and graduates
Volatile Oils-Introduction for pharmacy students and graduatesVolatile Oils-Introduction for pharmacy students and graduates
Volatile Oils-Introduction for pharmacy students and graduatesAhmed Metwaly
 
Study of X - Ray Spectra and its types
Study  of X  - Ray Spectra and its typesStudy  of X  - Ray Spectra and its types
Study of X - Ray Spectra and its typestanishashukla147
 
Automatic Stainer & Screener technique.pptx
Automatic Stainer & Screener technique.pptxAutomatic Stainer & Screener technique.pptx
Automatic Stainer & Screener technique.pptxSagarBhakare1
 
2024 Insilicogen Company English Brochure
2024 Insilicogen Company English Brochure2024 Insilicogen Company English Brochure
2024 Insilicogen Company English BrochureInsilico Gen
 
Safety_and_Health_Programs_v-03-01-17.pptx
Safety_and_Health_Programs_v-03-01-17.pptxSafety_and_Health_Programs_v-03-01-17.pptx
Safety_and_Health_Programs_v-03-01-17.pptxmohamedsami2233
 
Presentacion Mariana Arango- biología molecular
Presentacion Mariana Arango- biología molecularPresentacion Mariana Arango- biología molecular
Presentacion Mariana Arango- biología molecularmarianaarangop
 
Ento-322, Agrochemicals for agriculture usee
Ento-322, Agrochemicals for agriculture useeEnto-322, Agrochemicals for agriculture usee
Ento-322, Agrochemicals for agriculture useeDrAnita Sharma
 
Seminario biología molecular Lina Charris
Seminario biología molecular Lina CharrisSeminario biología molecular Lina Charris
Seminario biología molecular Lina CharrisLinaMarcelaCharrisRa
 

Recently uploaded (20)

dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
 
Genetic Code. A comprehensive overview..pdf
Genetic Code. A comprehensive overview..pdfGenetic Code. A comprehensive overview..pdf
Genetic Code. A comprehensive overview..pdf
 
Weak-lensing detection of intracluster filaments in the Coma cluster
Weak-lensing detection of intracluster filaments in the Coma clusterWeak-lensing detection of intracluster filaments in the Coma cluster
Weak-lensing detection of intracluster filaments in the Coma cluster
 
American Eclipse A Nation’s Epic Race to Catch the_240225_095603
American Eclipse A Nation’s Epic Race to Catch the_240225_095603American Eclipse A Nation’s Epic Race to Catch the_240225_095603
American Eclipse A Nation’s Epic Race to Catch the_240225_095603
 
Earth and Planetary Science | Volume 01 | Issue 01 | April 2022
Earth and Planetary Science | Volume 01 | Issue 01 | April 2022Earth and Planetary Science | Volume 01 | Issue 01 | April 2022
Earth and Planetary Science | Volume 01 | Issue 01 | April 2022
 
The ExoGRAVITY project - observations of exoplanets from the ground with opti...
The ExoGRAVITY project - observations of exoplanets from the ground with opti...The ExoGRAVITY project - observations of exoplanets from the ground with opti...
The ExoGRAVITY project - observations of exoplanets from the ground with opti...
 
Elbow joint - Anatomy of the Elbow joint
Elbow joint - Anatomy of the Elbow jointElbow joint - Anatomy of the Elbow joint
Elbow joint - Anatomy of the Elbow joint
 
REGULATION OF METABOLISM IN PLANTS AND THE DIFFERENT MECHANISMS
REGULATION OF METABOLISM IN PLANTS  AND THE DIFFERENT MECHANISMSREGULATION OF METABOLISM IN PLANTS  AND THE DIFFERENT MECHANISMS
REGULATION OF METABOLISM IN PLANTS AND THE DIFFERENT MECHANISMS
 
Salesforce Starter Package Presentation.
Salesforce Starter Package Presentation.Salesforce Starter Package Presentation.
Salesforce Starter Package Presentation.
 
green chemistry, clean sustainable environment.ppt
green chemistry, clean sustainable environment.pptgreen chemistry, clean sustainable environment.ppt
green chemistry, clean sustainable environment.ppt
 
Seminario biología molecular Lina Charris
Seminario biología molecular Lina CharrisSeminario biología molecular Lina Charris
Seminario biología molecular Lina Charris
 
commercial production of cellulase enzyme and its uses
commercial production of cellulase enzyme and its usescommercial production of cellulase enzyme and its uses
commercial production of cellulase enzyme and its uses
 
Volatile Oils-Introduction for pharmacy students and graduates
Volatile Oils-Introduction for pharmacy students and graduatesVolatile Oils-Introduction for pharmacy students and graduates
Volatile Oils-Introduction for pharmacy students and graduates
 
Study of X - Ray Spectra and its types
Study  of X  - Ray Spectra and its typesStudy  of X  - Ray Spectra and its types
Study of X - Ray Spectra and its types
 
Automatic Stainer & Screener technique.pptx
Automatic Stainer & Screener technique.pptxAutomatic Stainer & Screener technique.pptx
Automatic Stainer & Screener technique.pptx
 
2024 Insilicogen Company English Brochure
2024 Insilicogen Company English Brochure2024 Insilicogen Company English Brochure
2024 Insilicogen Company English Brochure
 
Safety_and_Health_Programs_v-03-01-17.pptx
Safety_and_Health_Programs_v-03-01-17.pptxSafety_and_Health_Programs_v-03-01-17.pptx
Safety_and_Health_Programs_v-03-01-17.pptx
 
Presentacion Mariana Arango- biología molecular
Presentacion Mariana Arango- biología molecularPresentacion Mariana Arango- biología molecular
Presentacion Mariana Arango- biología molecular
 
Ento-322, Agrochemicals for agriculture usee
Ento-322, Agrochemicals for agriculture useeEnto-322, Agrochemicals for agriculture usee
Ento-322, Agrochemicals for agriculture usee
 
Seminario biología molecular Lina Charris
Seminario biología molecular Lina CharrisSeminario biología molecular Lina Charris
Seminario biología molecular Lina Charris
 

Human parsing

  • 2. Problem description  Human parsing aims to segment a human image into multiple semantic parts.  It is a pixel-wise parsing problem.  It is a supervised machine learning problem.
  • 3. Challenges  Occluded (especially by other people)  Multi-scale  Cross-domain  Label conflict  Blurry  Cavity  … Main conflict is the desire for both larger field of view & more accurate location (Deeper or Denser?) } } Need larger field of view Need denser & more accurate location
  • 4. Related works  Atrous Convolution e.g. Deeplab
  • 5. Related works  Atrous Convolution e.g. Deeplab
  • 6. Related works  Skip Net e.g. U-net (top) FCN(bottom)
  • 7. Related works Edge + Pixel Voting e.g. CoCNN
  • 8. Baseline ASPP 3*256*256 20*256*256 20*256*256 64*128*128 fake real 256*64*64 512*32*32 1024*16*16 8192*16*16 2048*16*16 DeeplabV2 Resnet101 Block Resnet101 Block with Atrous Conv Tensor Transfer Upsampling
  • 9. Two GANs  Patch GAN focuses on low-level and local features, which guarantees sharp and clear labelmaps.  Pose GAN focuses on high-level and global features, which helps generating labelmaps that consist with human pose priors.
  • 10. ASPP Patch D Patch GAN loss Shallow NLL loss Deep NLL loss Resize Concat Totalloss Copy 3*256*256 20*256*256 20*256*256 3*256*256 20*16*16 64*128*128 20*16*16 fake real fake 256*64*64 512*32*32 real 1024*16*16 8192*16*16 2048*16*16 Resnet101 Block Resnet101 Block with Atrous Conv Tensor Transfer Upsampling
  • 13. ASPP Patch D Pose D Patch GAN loss Shallow NLL loss Deep NLL loss Pose GAN loss Resize Concat Concat Totalloss Copy 3*256*256 19*16*16 20*256*256 20*256*256 3*256*256 19*16*16 20*16*16 64*128*128 Openpose 20*16*16 fake real fake 256*64*64 512*32*32 real 1024*16*16 8192*16*16 2048*16*16 Resnet101 Block Resnet101 Block with Atrous Conv Tensor Transfer Upsampling Resize Concat
  • 14. Real: 1 ⋯ 1 ⋮ ⋱ ⋮ 1 ⋯ 1 Fake: 0 ⋯ 0 ⋮ ⋱ ⋮ 0 ⋯ 0 Real: 1 Fake: 0 Patch GAN Pose GAN Difference between two discriminator RGB image Pose Label map Feature map
  • 17. Experimental result with Two GANs (LIP): Total loss
  • 18. Experimental result with Two GANs (LIP): D_loss and G_loss
  • 19. Contributions  We propose an effective PP-GAN for human parsing, which employs two conditional GANs as supplementary supervisions on shallow, fine layers and deep, coarse layers of the network respectively. Our model explicitly divides the human parsing into "what" and "where" subtasks in an unified framework and boosts the parsing performance on both image level and semantic level.  To our best knowledge, it is the first attempt to integrate human pose information into a conditional GAN framework for human parsing task, which significantly reduces the structural error of parsing results.  In the proposed framework, discrimination process is naturally divided into two easier tasks and two different discriminators are employed. The experiments demonstrate that multiple discriminators, which only focus on their own areas, prevail over single discriminator which is prone to saturate when facing with complex task.  The proposed PP-GAN significantly surpasses the previous methods on both challenging LIP and XXX benchmark datasets.