A central challenge for the task of semantic segmentation is the prohibitive
cost of obtaining dense pixel-level annotations to supervise model training. In
this work, we show that in order to achieve a good level of segmentation
performance, all you need are a few well-chosen pixel labels. We make the
following contributions: (i) We investigate the novel semantic segmentation
setting in which labels are supplied only at sparse pixel locations, and show
that deep neural networks can use a handful of such labels to good effect; (ii)
We demonstrate how to exploit this phenomena within an active learning
framework, termed PixelPick, to radically reduce labelling cost, and propose an
efficient "mouse-free" annotation strategy to implement our approach; (iii) We
conduct extensive experiments to study the influence of annotation diversity
under a fixed budget, model pretraining, model capacity and the sampling
mechanism for picking pixels in this low annotation regime; (iv) We provide
comparisons to the existing state of the art in semantic segmentation with
active learning, and demonstrate comparable performance with up to two orders
of magnitude fewer pixel annotations on the CamVid, Cityscapes and PASCAL VOC
2012 benchmarks; (v) Finally, we evaluate the efficiency of our annotation
pipeline and its sensitivity to annotator error to demonstrate its
practicality.

通过仅在稀疏像素位置提供标签，深度神经网络可以实现良好的语义分割性能，结合主动学习机制和高效注释策略可以大大减少标注成本，并在 CamVid，Cityscapes 和 PASCAL VOC 2012 数据集上进行了实验证明了相当可比的性能。