  Edge detection has made significant progress with the help of deep
Convolutional Networks (ConvNet). These ConvNet based edge detectors have
approached human level performance on standard benchmarks. We provide a
systematical study of these detectors' outputs. We show that the detection
results did not accurately localize edge pixels, which can be adversarial for
tasks that require crisp edge inputs. As a remedy, we propose a novel
refinement architecture to address the challenging problem of learning a crisp
edge detector using ConvNet. Our method leverages a top-down backward
refinement pathway, and progressively increases the resolution of feature maps
to generate crisp edges. Our results achieve superior performance, surpassing
human accuracy when using standard criteria on BSDS500, and largely
outperforming state-of-the-art methods when using more strict criteria. More
importantly, we demonstrate the benefit of crisp edge maps for several
important applications in computer vision, including optical flow estimation,
object proposal generation and semantic segmentation.
