  Semantic image segmentation is a fundamental task in image understanding.
Per-pixel semantic labelling of an image benefits greatly from the ability to
consider region consistency both locally and globally. However, many Fully
Convolutional Network based methods do not impose such consistency, which may
give rise to noisy and implausible predictions. We address this issue by
proposing a dense multi-label network module that is able to encourage the
region consistency at different levels. This simple but effective module can be
easily integrated into any semantic segmentation systems. With comprehensive
experiments, we show that the dense multi-label can successfully remove the
implausible labels and clear the confusion so as to boost the performance of
semantic segmentation systems.
