Higher Order Conditional Random Fields in Deep Neural Networks

Understand

We address the problem of semantic segmentation using deep learning.

• Most segmentation systems include a Conditional Random Field (CRF) to produce a structured output that is consistent with the image's visual features.
• Recent deep learning approaches have incorporated CRFs into Convolutional Neural Networks (CNNs), with some even training the CRF end-to-end with the rest of the network.
• However, these approaches have not employed higher order potentials, which have previously been shown to significantly improve segmentation performance.