  Recent object detection systems rely on two critical steps: (1) a set of
object proposals is predicted as efficiently as possible, and (2) this set of
candidate proposals is then passed to an object classifier. Such approaches
have been shown they can be fast, while achieving the state of the art in
detection performance. In this paper, we propose a new way to generate object
proposals, introducing an approach based on a discriminative convolutional
network. Our model is trained jointly with two objectives: given an image
patch, the first part of the system outputs a class-agnostic segmentation mask,
while the second part of the system outputs the likelihood of the patch being
centered on a full object. At test time, the model is efficiently applied on
the whole test image and generates a set of segmentation masks, each of them
being assigned with a corresponding object likelihood score. We show that our
model yields significant improvements over state-of-the-art object proposal
algorithms. In particular, compared to previous approaches, our model obtains
substantially higher object recall using fewer proposals. We also show that our
model is able to generalize to unseen categories it has not seen during
training. Unlike all previous approaches for generating object masks, we do not
rely on edges, superpixels, or any other form of low-level segmentation.
