18_Style_And_Identity
展开查看详情
1.Computer vision: models, learning and inference Chapter 18 Models for style and identity
2.Identity and Style 2 2 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Identity differs, but images similar Identity same, but images quite different
3.Structure 3 3 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Factor analysis review Subspace identity model Linear discriminant analysis Nonlinear models Asymmetric bilinear model Symmetric bilinear model Applications
4.Factor analysis review 4 4 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Generative equation: Probabilistic form: Marginal density:
5.Factor analysis 5 5 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
6.Factor analysis review 6 6 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince EStep: MStep:
7.Factor analysis vs. Identity model 7 7 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Each color is a different identity multiple images lie in similar part of subspace
8.Subspace identity model 8 8 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Generative equation: Probabilistic form: Marginal density:
9.Subspace identity model 9 9 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
10.Factor analysis vs. subspace identity 10 10 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Factor analysis Subspace identity model
11.Learning subspace identity model 11 11 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince EStep: Extract moments :
12.Learning subspace identity model 12 12 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince MStep: E Step:
13.Subspace identity model 13 13 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
14.Subspace identity model 14 14 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
15.Inference by comparing models 15 15 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Model 1 – Faces match (identity shared) : Model 2 – Faces dont match (identities differ) : Both models have standard form of factor analyzer
16.Inference by comparing models 16 16 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Compute likelihood (e.g. for model zero) where Compute posterior probability using Bayes rule
17.Face Recognition Tasks PROBE … GALLERY ? 1. CLOSED SET FACE IDENTIFICATION … GALLERY PROBE ? NO MATCH 2. OPEN SET FACE IDENTIFICATION PROBE ? NO MATCH 3. FACE VERIFICATION 4. FACE CLUSTERING ? 17 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
18.Inference by comparing models 18 18 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
19.Relation between models 19 19 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
20.Structure 20 20 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Factor analysis review Subspace identity model Linear discriminant analysis Nonlinear models Asymmetric bilinear model Symmetric bilinear model Applications
21.Probabilistic linear discriminant analysis 21 21 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Generative equation: Probabilistic form:
22.Probabilistic linear discriminant analysis 22 22 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
23.Learning 23 23 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince EStep 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
24.Probabilistic linear discriminant analyis 24 24 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
25.Inference 25 25 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 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
26.Example results (XM2VTS database) 26 26 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
27.Structure 27 27 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Factor analysis review Subspace identity model Linear discriminant analysis Nonlinear models Asymmetric bilinear model Symmetric bilinear model Applications
28.Nonlinear models (mixture) 28 28 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Mixture model can describe nonlinear manifold. Introduce variable c i which represents which cluster To be the same identity, must also belong to the same cluster
29.Nonlinear models (kernel) 29 29 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Pass hidden variable through nonlinear function f[ ]. Leads to kernelized algorithm Identity equivalent of GPLVM

Graph Features in Spark 3.0  Integrating Graph Querying and Algorithms in Spark Graphg
Spark开源社区

Updates from Project Hydrogen  Unifying StateoftheArt AI and Big Data in Apache Spark
Spark开源社区

Tensorflow Faster RCNN 2.0
GDG

Deep learning and gene computing acceleration with alluxio in kubernetes
Alluxio

tf.data: TensorFlow Input Pipeline
Alluxio