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Appendix:
CRF-RNN feature in Caffe is implemented for the paper: Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.

Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, and Philip H. S. Torr are with University of Oxford. Vibhav Vineet did this work when he was with the University of Oxford, he is now with the Stanford University. Zhizhong Su, Dalong Du, Chang Huang are with the Baidu Institute of Deep Learning (IDL).

CRF-RNN uses the Permutohedral lattice library, the DenseCRF library and the Caffe future version.

Permutohedral lattice library (BSD license) is from Andrew Adams, Jongmin Baek, Abe Davis. Fast High-Dimensional Filtering Using the Permutohedral Lattice. Eurographics 2010. DenseCRF library from Philipp Krahenbuhl and Vladlen Koltun. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. NIPS 2011.

For more information about CRF-RNN please vist the project website http://crfasrnn.torr.vision. Contact: crfasrnn@gmail.com