Learning specific class segmentation from diverse data
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Learning Specific-Class Segmentation from Diverse Data. M. Pawan Kuma r, Haitherm Turki , Dan Preston and Daphne Koller at ICCV 2011. VGG reading group, 29 Nov 2011, presented by Varun Gulshan. Semantic image segmentation. Main idea.

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Learning specific class segmentation from diverse data

Learning Specific-Class Segmentation from Diverse Data

M. Pawan Kumar, HaithermTurki, Dan Preston and Daphne Koller at ICCV 2011

VGG reading group, 29 Nov 2011, presented by VarunGulshan



Main idea
Main idea

  • High level: Getting fully labelled data for training is expensive, use other easily available ‘diverse’ data for learning (bounding boxes, classification labels for image).

Tags: Car, people

Person bounding box


Implementing the idea
Implementing the idea

  • The bounding box/image classification data is incomplete for segmentation, fill in the missing information using latent variables.

  • Setup the training cost function using latent variables. Use their self-paced learning algorithm for Latent-SVM’s [NIPS2010] to optimise the training cost function.

  • While inferring latent variables, make sure latent variable estimation is consistent with the weak annotation. Setting up the inference problems to ensure this condition.


Energy function without latent variables
Energy function without latent variables

Notation:

Joint feature vector (essentially the terms of a CRF)

Image

Parameters to be trained


Structured output training
Structured output training

Ground truth labels

Loss function



Introducing latent variables1
Introducing latent variables

But we don’t know what hk is (its latent), so maximise it out.




Self paced optimisation1
Self-paced optimisation

Indicator variable to switch off the harder cases.


Second idea latent variable estimation
Second idea: Latent variable estimation

The algorithm involves estimating annotation consistent latent variables in the following equation:

More precisely


Move to white board
Move to white-board

Me

Beware of Equations

You


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