  We develop a deep learning algorithm for contour detection with a fully
convolutional encoder-decoder network. Different from previous low-level edge
detection, our algorithm focuses on detecting higher-level object contours. Our
network is trained end-to-end on PASCAL VOC with refined ground truth from
inaccurate polygon annotations, yielding much higher precision in object
contour detection than previous methods. We find that the learned model
generalizes well to unseen object classes from the same super-categories on MS
COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning.
By combining with the multiscale combinatorial grouping algorithm, our method
can generate high-quality segmented object proposals, which significantly
advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62
to 0.67) with a relatively small amount of candidates ($\sim$1660 per image).
