  Semantic segmentation research has recently witnessed rapid progress, but
many leading methods are unable to identify object instances. In this paper, we
present Multi-task Network Cascades for instance-aware semantic segmentation.
Our model consists of three networks, respectively differentiating instances,
estimating masks, and categorizing objects. These networks form a cascaded
structure, and are designed to share their convolutional features. We develop
an algorithm for the nontrivial end-to-end training of this causal, cascaded
structure. Our solution is a clean, single-step training framework and can be
generalized to cascades that have more stages. We demonstrate state-of-the-art
instance-aware semantic segmentation accuracy on PASCAL VOC. Meanwhile, our
method takes only 360ms testing an image using VGG-16, which is two orders of
magnitude faster than previous systems for this challenging problem. As a by
product, our method also achieves compelling object detection results which
surpass the competitive Fast/Faster R-CNN systems.
  The method described in this paper is the foundation of our submissions to
the MS COCO 2015 segmentation competition, where we won the 1st place.
