Enviar búsqueda
Cargar
18 cv mil_style_and_identity
•
Descargar como PPTX, PDF
•
0 recomendaciones
•
328 vistas
Z
zukun
Seguir
Tecnología
Empresariales
Denunciar
Compartir
Denunciar
Compartir
1 de 49
Descargar ahora
Recomendados
16 cv mil_multiple_cameras
16 cv mil_multiple_cameras
zukun
12 cv mil_models_for_grids
12 cv mil_models_for_grids
zukun
Lecture27
Lecture27
zukun
Lecture14
Lecture14
zukun
Mit6870 orsu lecture12
Mit6870 orsu lecture12
zukun
Skiena algorithm 2007 lecture20 satisfiability
Skiena algorithm 2007 lecture20 satisfiability
zukun
Lecture24
Lecture24
zukun
ETHZ CV2012: Tutorial openCV
ETHZ CV2012: Tutorial openCV
zukun
Recomendados
16 cv mil_multiple_cameras
16 cv mil_multiple_cameras
zukun
12 cv mil_models_for_grids
12 cv mil_models_for_grids
zukun
Lecture27
Lecture27
zukun
Lecture14
Lecture14
zukun
Mit6870 orsu lecture12
Mit6870 orsu lecture12
zukun
Skiena algorithm 2007 lecture20 satisfiability
Skiena algorithm 2007 lecture20 satisfiability
zukun
Lecture24
Lecture24
zukun
ETHZ CV2012: Tutorial openCV
ETHZ CV2012: Tutorial openCV
zukun
17 cv mil_models_for_shape
17 cv mil_models_for_shape
zukun
20 cv mil_models_for_words
20 cv mil_models_for_words
zukun
09 cv mil_classification
09 cv mil_classification
zukun
08 cv mil_regression
08 cv mil_regression
zukun
15 cv mil_models_for_transformations
15 cv mil_models_for_transformations
zukun
11 cv mil_models_for_chains_and_trees
11 cv mil_models_for_chains_and_trees
zukun
14 cv mil_the_pinhole_camera
14 cv mil_the_pinhole_camera
zukun
13 cv mil_preprocessing
13 cv mil_preprocessing
zukun
07 cv mil_modeling_complex_densities
07 cv mil_modeling_complex_densities
zukun
10 cv mil_graphical_models
10 cv mil_graphical_models
zukun
Graphical Models for chains, trees and grids
Graphical Models for chains, trees and grids
potaters
04 cv mil_fitting_probability_models
04 cv mil_fitting_probability_models
zukun
Common Probability Distibution
Common Probability Distibution
Lukas Tencer
03 cv mil_probability_distributions
03 cv mil_probability_distributions
zukun
machinelearning_slide note this is repdf
machinelearning_slide note this is repdf
JUNHOPARK49
Introduction to Probability
Introduction to Probability
Lukas Tencer
06 cv mil_learning_and_inference
06 cv mil_learning_and_inference
zukun
Project presentation by Debendra Adhikari
Project presentation by Debendra Adhikari
DEBENDRA ADHIKARI
Gabriel Bianconi - Introduction to Face Processing with Computer Vision
Gabriel Bianconi - Introduction to Face Processing with Computer Vision
PyCon Odessa
My lyn tutorial 2009
My lyn tutorial 2009
zukun
ETHZ CV2012: Information
ETHZ CV2012: Information
zukun
Siwei lyu: natural image statistics
Siwei lyu: natural image statistics
zukun
Más contenido relacionado
Similar a 18 cv mil_style_and_identity
17 cv mil_models_for_shape
17 cv mil_models_for_shape
zukun
20 cv mil_models_for_words
20 cv mil_models_for_words
zukun
09 cv mil_classification
09 cv mil_classification
zukun
08 cv mil_regression
08 cv mil_regression
zukun
15 cv mil_models_for_transformations
15 cv mil_models_for_transformations
zukun
11 cv mil_models_for_chains_and_trees
11 cv mil_models_for_chains_and_trees
zukun
14 cv mil_the_pinhole_camera
14 cv mil_the_pinhole_camera
zukun
13 cv mil_preprocessing
13 cv mil_preprocessing
zukun
07 cv mil_modeling_complex_densities
07 cv mil_modeling_complex_densities
zukun
10 cv mil_graphical_models
10 cv mil_graphical_models
zukun
Graphical Models for chains, trees and grids
Graphical Models for chains, trees and grids
potaters
04 cv mil_fitting_probability_models
04 cv mil_fitting_probability_models
zukun
Common Probability Distibution
Common Probability Distibution
Lukas Tencer
03 cv mil_probability_distributions
03 cv mil_probability_distributions
zukun
machinelearning_slide note this is repdf
machinelearning_slide note this is repdf
JUNHOPARK49
Introduction to Probability
Introduction to Probability
Lukas Tencer
06 cv mil_learning_and_inference
06 cv mil_learning_and_inference
zukun
Project presentation by Debendra Adhikari
Project presentation by Debendra Adhikari
DEBENDRA ADHIKARI
Gabriel Bianconi - Introduction to Face Processing with Computer Vision
Gabriel Bianconi - Introduction to Face Processing with Computer Vision
PyCon Odessa
Similar a 18 cv mil_style_and_identity
(19)
17 cv mil_models_for_shape
17 cv mil_models_for_shape
20 cv mil_models_for_words
20 cv mil_models_for_words
09 cv mil_classification
09 cv mil_classification
08 cv mil_regression
08 cv mil_regression
15 cv mil_models_for_transformations
15 cv mil_models_for_transformations
11 cv mil_models_for_chains_and_trees
11 cv mil_models_for_chains_and_trees
14 cv mil_the_pinhole_camera
14 cv mil_the_pinhole_camera
13 cv mil_preprocessing
13 cv mil_preprocessing
07 cv mil_modeling_complex_densities
07 cv mil_modeling_complex_densities
10 cv mil_graphical_models
10 cv mil_graphical_models
Graphical Models for chains, trees and grids
Graphical Models for chains, trees and grids
04 cv mil_fitting_probability_models
04 cv mil_fitting_probability_models
Common Probability Distibution
Common Probability Distibution
03 cv mil_probability_distributions
03 cv mil_probability_distributions
machinelearning_slide note this is repdf
machinelearning_slide note this is repdf
Introduction to Probability
Introduction to Probability
06 cv mil_learning_and_inference
06 cv mil_learning_and_inference
Project presentation by Debendra Adhikari
Project presentation by Debendra Adhikari
Gabriel Bianconi - Introduction to Face Processing with Computer Vision
Gabriel Bianconi - Introduction to Face Processing with Computer Vision
Más de zukun
My lyn tutorial 2009
My lyn tutorial 2009
zukun
ETHZ CV2012: Information
ETHZ CV2012: Information
zukun
Siwei lyu: natural image statistics
Siwei lyu: natural image statistics
zukun
Lecture9 camera calibration
Lecture9 camera calibration
zukun
Brunelli 2008: template matching techniques in computer vision
Brunelli 2008: template matching techniques in computer vision
zukun
Modern features-part-4-evaluation
Modern features-part-4-evaluation
zukun
Modern features-part-3-software
Modern features-part-3-software
zukun
Modern features-part-2-descriptors
Modern features-part-2-descriptors
zukun
Modern features-part-1-detectors
Modern features-part-1-detectors
zukun
Modern features-part-0-intro
Modern features-part-0-intro
zukun
Lecture 02 internet video search
Lecture 02 internet video search
zukun
Lecture 01 internet video search
Lecture 01 internet video search
zukun
Lecture 03 internet video search
Lecture 03 internet video search
zukun
Icml2012 tutorial representation_learning
Icml2012 tutorial representation_learning
zukun
Advances in discrete energy minimisation for computer vision
Advances in discrete energy minimisation for computer vision
zukun
Gephi tutorial: quick start
Gephi tutorial: quick start
zukun
EM algorithm and its application in probabilistic latent semantic analysis
EM algorithm and its application in probabilistic latent semantic analysis
zukun
Object recognition with pictorial structures
Object recognition with pictorial structures
zukun
Iccv2011 learning spatiotemporal graphs of human activities
Iccv2011 learning spatiotemporal graphs of human activities
zukun
Icml2012 learning hierarchies of invariant features
Icml2012 learning hierarchies of invariant features
zukun
Más de zukun
(20)
My lyn tutorial 2009
My lyn tutorial 2009
ETHZ CV2012: Information
ETHZ CV2012: Information
Siwei lyu: natural image statistics
Siwei lyu: natural image statistics
Lecture9 camera calibration
Lecture9 camera calibration
Brunelli 2008: template matching techniques in computer vision
Brunelli 2008: template matching techniques in computer vision
Modern features-part-4-evaluation
Modern features-part-4-evaluation
Modern features-part-3-software
Modern features-part-3-software
Modern features-part-2-descriptors
Modern features-part-2-descriptors
Modern features-part-1-detectors
Modern features-part-1-detectors
Modern features-part-0-intro
Modern features-part-0-intro
Lecture 02 internet video search
Lecture 02 internet video search
Lecture 01 internet video search
Lecture 01 internet video search
Lecture 03 internet video search
Lecture 03 internet video search
Icml2012 tutorial representation_learning
Icml2012 tutorial representation_learning
Advances in discrete energy minimisation for computer vision
Advances in discrete energy minimisation for computer vision
Gephi tutorial: quick start
Gephi tutorial: quick start
EM algorithm and its application in probabilistic latent semantic analysis
EM algorithm and its application in probabilistic latent semantic analysis
Object recognition with pictorial structures
Object recognition with pictorial structures
Iccv2011 learning spatiotemporal graphs of human activities
Iccv2011 learning spatiotemporal graphs of human activities
Icml2012 learning hierarchies of invariant features
Icml2012 learning hierarchies of invariant features
Último
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
The Digital Insurer
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
apidays
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
MIND CTI
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Gabriella Davis
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
jfdjdjcjdnsjd
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
UK Journal
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
Martijn de Jong
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
Andrey Devyatkin
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
The Digital Insurer
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
lior mazor
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
DianaGray10
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
wesley chun
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
Igalia
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Miguel Araújo
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
Último
(20)
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
18 cv mil_style_and_identity
1.
Computer vision: models, learning
and inference Chapter 18 Models for style and identity Please send errata to s.prince@cs.ucl.ac.uk
2.
Identity and Style
Identity differs, but images similar Identity same, but images quite different Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 2
3.
Structure •
Factor analysis review • Subspace identity model • Linear discriminant analysis • Non-linear models • Asymmetric bilinear model • Symmetric bilinear model • Applications Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 3
4.
Factor analysis review Generative
equation: Probabilistic form: Marginal density: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 4
5.
Factor analysis Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince 5
6.
Factor analysis review E-Step: M-Step:
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 6
7.
Factor analysis vs.
Identity model • Each color is a different identity • multiple images lie in similar part of subspace Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 7
8.
Subspace identity model Generative
equation: Probabilistic form: Marginal density: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 8
9.
Subspace identity model
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 9
10.
Factor analysis vs.
subspace identity Factor analysis Subspace identity model Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 10
11.
Learning subspace identity
model E-Step: Extract moments: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 11
12.
Learning subspace identity
model E-Step: M-Step: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 12
13.
Subspace identity model
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 13
14.
Subspace identity model
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 14
15.
Inference by comparing
models Model 1 – Faces match (identity shared): Model 2 – Faces dont match (identities differ): Both models have standard form of factor analyzer Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 15
16.
Inference by comparing
models Compute likelihood (e.g. for model zero) where Compute posterior probability using Bayes rule Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 16
17.
Face Recognition Tasks
GALLERY PROBE … ? 1. CLOSED SET FACE IDENTIFICATION GALLERY PROBE … NO ? 2. OPEN SET MATCH FACE IDENTIFICATION PROBE NO MATCH ? 3. FACE VERIFICATION ? 4. FACE CLUSTERING Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 17
18.
Inference by comparing
models Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 18
19.
Relation between models
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 19
20.
Structure •
Factor analysis review • Subspace identity model • Linear discriminant analysis • Non-linear models • Asymmetric bilinear model • Symmetric bilinear model • Applications Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 20
21.
Probabilistic linear
discriminant analysis Generative equation: Probabilistic form: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 21
22.
Probabilistic linear discriminant
analysis Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 22
23.
Learning E-Step
– write out all images of same person as system of equations – Has standard form of factor analyzer – Use standard EM equation M-Step – write equation for each individual data point – Has standard form of factor analyzer – Use standard EM equation Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 23
24.
Probabilistic linear discriminant
analyis Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 24
25.
Inference Model 1 –
Faces match (identity shared): Model 2 – Faces dont match (identities differ): Both models have standard form of factor analyzer Compute likelihood in standard way Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 25
26.
Example results (XM2VTS
database) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 26
27.
Structure •
Factor analysis review • Subspace identity model • Linear discriminant analysis • Non-linear models • Asymmetric bilinear model • Symmetric bilinear model • Applications Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 27
28.
Non-linear models (mixture) Mixture
model can describe non- linear manifold. Introduce variable ci which represents which cluster To be the same identity, must also belong to the same cluster Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 28
29.
Non-linear models (kernel) •
Pass hidden variable through non-linear function f[ ]. • Leads to kernelized algorithm • Identity equivalent of GPLVM Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 29
30.
Structure •
Factor analysis review • Subspace identity model • Linear discriminant analysis • Non-linear models • Asymmetric bilinear model • Symmetric bilinear model • Applications Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 30
31.
Asymmetric bilinear model •
Introduce style variable sij • indicates conditions in which data was observed • Example: lighting, pose, expression face recognition Asymmetric bilinear model • Introduce style variable sij • indicates conditions in which data was observed • Example: lighting, pose, expression face recognition Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 31
32.
Asymmetric bilinear model
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 32
33.
Asymmetric bilinear model Generative
equation: Probabilistic form: Marginal density: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 33
34.
Learning E-Step: M-Step:
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 34
35.
Asymmetric bilinear model
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 35
36.
Inference – inferring
style Likelihood of style Prior over style Compute posterior over style using Bayes’ rule Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 36
37.
Inference – inferring
identity Likelihood of identity Prior over identity Compute posterior over identity using Bayes’ rule Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 37
38.
Inference – comparing
identities Model 1 – Faces match (identity shared): Model 2 – Faces dont match (identities differ): Both models have standard form of factor analyzer Compute likelihood in standard way, combine with prior in Bayes rule Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 38
39.
Inference – Style
translation • Compute distribution over identity • Generate in new style Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 39
40.
Structure •
Factor analysis review • Subspace identity model • Linear discriminant analysis • Non-linear models • Asymmetric bilinear model • Symmetric bilinear model • Applications Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 40
41.
Symmetric bilinear model Generative
equation: Probabilistic form: Mean can also depend on style... Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 41
42.
Symmetric bilinear model
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 42
43.
Inference – translating
style or identity Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 43
44.
Multilinear models Extension of
symmetric bilinear model to more than two factors e.g., Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 44
45.
Structure •
Factor analysis review • Subspace identity model • Linear discriminant analysis • Non-linear models • Asymmetric bilinear model • Symmetric bilinear model • Applications Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 45
46.
Face recognition Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince 46
47.
Tensortextures Computer vision: models,
learning and inference. ©2011 Simon J.D. Prince 47
48.
Synthesizing animation Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince 48
49.
Discussion • Generative models •
Explain data as combination of identity and style factors • In identity recognition, we build models where identity was same or different • Other forms of inference such as style translation also possible Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 49
Descargar ahora