Learning to Segment Object Candidates

Understand

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.