  We propose an end-to-end learning framework for generating foreground object
segmentations. Given a single novel image, our approach produces pixel-level
masks for all "object-like" regions---even for object categories never seen
during training. We formulate the task as a structured prediction problem of
assigning foreground/background labels to all pixels, implemented using a deep
fully convolutional network. Key to our idea is training with a mix of
image-level object category examples together with relatively few images with
boundary-level annotations. Our method substantially improves the
state-of-the-art on foreground segmentation for ImageNet and MIT Object
Discovery datasets. Furthermore, on over 1 million images, we show that it
generalizes well to segment object categories unseen in the foreground maps
used for training. Finally, we demonstrate how our approach benefits image
retrieval and image retargeting, both of which flourish when given our
high-quality foreground maps.
