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Learning Specific-Class Segmentation from Diverse Data

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

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  1. 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

  2. Semantic image segmentation

  3. 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

  4. 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.

  5. Energy function without latent variables Notation: Joint feature vector (essentially the terms of a CRF) Image Parameters to be trained

  6. Structured output training Ground truth labels Loss function

  7. Introducing latent variables

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

  9. Introducing latent variables

  10. Self-paced optimisation

  11. Self-paced optimisation Indicator variable to switch off the harder cases.

  12. Second idea: Latent variable estimation The algorithm involves estimating annotation consistent latent variables in the following equation: More precisely

  13. Move to white-board Me Beware of Equations You

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