  This work proposed a novel learning objective to train a deep neural network
to perform end-to-end image pixel clustering. We applied the approach to
instance segmentation, which is at the intersection of image semantic
segmentation and object detection. We utilize the most fundamental property of
instance labeling -- the pairwise relationship between pixels -- as the
supervision to formulate the learning objective, then apply it to train a fully
convolutional network (FCN) for learning to perform pixel-wise clustering. The
resulting clusters can be used as the instance labeling directly. To support
labeling of an unlimited number of instance, we further formulate ideas from
graph coloring theory into the proposed learning objective. The evaluation on
the Cityscapes dataset demonstrates strong performance and therefore proof of
the concept. Moreover, our approach won the second place in the lane detection
competition of 2017 CVPR Autonomous Driving Challenge, and was the top
performer without using external data.
