ShapeOdds: Variational Bayesian Learning of Generative Shape Models

This repo contains the open-source implementation of our paper: ShapeOdds: Variational Bayesian Learning of Generative Shape Models. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2231-2242, 2017.

Content

• example.m: run a demo examples using the Weizmann horses dataset. In this example you can train a shapeodds model, perform inference and robust inference, quantify model generalization, and draw samples from the model.
• EstimateShapeOdds.m: the main function that trains a shapeodds model given a set of silhouttes
• bin: a directory that includes all auxilary functions used by EstimateShapeOdds.m and example.m
• data: a directory where data used in example.m is stored
• models: a directory where you can store learned models and other outputs

For any questions, please contact Shireen Elhabian (shireen-at-sci-dot-utah-dot-edu)

Citation

If you use this code in any publication, please cite the following:

ShapeOdds

Shireen Y. Elhabian and Ross T. Whitaker. ShapeOdds: Variational Bayesian Learning of Generative Shape Models. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2231-2242, 2017.

Piecewise Bounds

Marlin BM, Khan ME, Murphy KP. Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. InICML 2011 Jun 28 (pp. 633-640).

RBM code

Tsogkas S, Kokkinos I, Papandreou G, Vedaldi A. Deep learning for semantic part segmentation with high-level guidance. arXiv preprint arXiv:1505.02438. 2015 May 10.