  Instance-level object segmentation is an important yet under-explored task.
The few existing studies are almost all based on region proposal methods to
extract candidate segments and then utilize object classification to produce
final results. Nonetheless, generating accurate region proposals itself is
quite challenging. In this work, we propose a Proposal-Free Network (PFN ) to
address the instance-level object segmentation problem, which outputs the
instance numbers of different categories and the pixel-level information on 1)
the coordinates of the instance bounding box each pixel belongs to, and 2) the
confidences of different categories for each pixel, based on pixel-to-pixel
deep convolutional neural network. All the outputs together, by using any
off-the-shelf clustering method for simple post-processing, can naturally
generate the ultimate instance-level object segmentation results. The whole PFN
can be easily trained in an end-to-end way without the requirement of a
proposal generation stage. Extensive evaluations on the challenging PASCAL VOC
2012 semantic segmentation benchmark demonstrate that the proposed PFN solution
well beats the state-of-the-arts for instance-level object segmentation. In
particular, the $AP^r$ over 20 classes at 0.5 IoU reaches 58.7% by PFN,
significantly higher than 43.8% and 46.3% by the state-of-the-art algorithms,
SDS [9] and [16], respectively.
