  In this work we show that adapting Deep Convolutional Neural Network training
to the task of boundary detection can result in substantial improvements over
the current state-of-the-art in boundary detection.
  Our contributions consist firstly in combining a careful design of the loss
for boundary detection training, a multi-resolution architecture and training
with external data to improve the detection accuracy of the current state of
the art. When measured on the standard Berkeley Segmentation Dataset, we
improve theoptimal dataset scale F-measure from 0.780 to 0.808 - while human
performance is at 0.803. We further improve performance to 0.813 by combining
deep learning with grouping, integrating the Normalized Cuts technique within a
deep network.
  We also examine the potential of our boundary detector in conjunction with
the task of semantic segmentation and demonstrate clear improvements over
state-of-the-art systems. Our detector is fully integrated in the popular Caffe
framework and processes a 320x420 image in less than a second.
