  We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for multi-scale
contour detection and it uses a novel sparse boundary representation for
hierarchical segmentation; it gives a significant leap in performance over the
state-of-the-art, and it generalizes very well to unseen categories and
datasets. Particularly, we show that learning to estimate not only contour
strength but also orientation provides more accurate results. We perform
extensive experiments for low-level applications on BSDS, PASCAL Context,
PASCAL Segmentation, and NYUD to evaluate boundary detection performance,
showing that COB provides state-of-the-art contours and region hierarchies in
all datasets. We also evaluate COB on high-level tasks when coupled with
multiple pipelines for object proposals, semantic contours, semantic
segmentation, and object detection on MS-COCO, SBD, and PASCAL; showing that
COB also improves the results for all tasks.
