Learning Specific-Class Segmentation from Diverse Data

M. Pawan Kumar, 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

• 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, peoplePerson bounding box

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

Notation:

Image

Parameters to be trained

Joint feature vector (essentially the terms of a CRF)

Structured output training

Ground truth labels

Loss function

Introducing latent variables

Introducing latent variables

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

Introducing latent variables

Self-paced optimisation

Self-paced optimisation

Indicator variable to switch off the harder cases.

Second idea: Latent variable estimation

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

More precisely

Move to white-board

Me

You

Beware of Equations