  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. In this paper, we demonstrate that two types of
higher order potential, based on object detections and superpixels, can be
included in a CRF embedded within a deep network. We design these higher order
potentials to allow inference with the differentiable mean field algorithm. As
a result, all the parameters of our richer CRF model can be learned end-to-end
with our pixelwise CNN classifier. We achieve state-of-the-art segmentation
performance on the PASCAL VOC benchmark with these trainable higher order
potentials.
