  We propose an end-to-end learning framework for segmenting generic objects in
both images and videos. Given a novel image or video, our approach produces a
pixel-level mask for all "object-like" regions---even for object categories
never seen during training. We formulate the task as a structured prediction
problem of assigning an object/background label to each pixel, implemented
using a deep fully convolutional network. When applied to a video, our model
further incorporates a motion stream, and the network learns to combine both
appearance and motion and attempts to extract all prominent objects whether
they are moving or not. Beyond the core model, a second contribution of our
approach is how it leverages varying strengths of training annotations.
Pixel-level annotations are quite difficult to obtain, yet crucial for training
a deep network approach for segmentation. Thus we propose ways to exploit
weakly labeled data for learning dense foreground segmentation. For images, we
show the value in mixing object category examples with image-level labels
together with relatively few images with boundary-level annotations. For video,
we show how to bootstrap weakly annotated videos together with the network
trained for image segmentation. Through experiments on multiple challenging
image and video segmentation benchmarks, our method offers consistently strong
results and improves the state-of-the-art for fully automatic segmentation of
generic (unseen) objects. In addition, we demonstrate how our approach benefits
image retrieval and image retargeting, both of which flourish when given our
high-quality foreground maps. Code, models, and videos are
at:http://vision.cs.utexas.edu/projects/pixelobjectness/
