  We present a technique for adding global context to deep convolutional
networks for semantic segmentation. The approach is simple, using the average
feature for a layer to augment the features at each location. In addition, we
study several idiosyncrasies of training, significantly increasing the
performance of baseline networks (e.g. from FCN). When we add our proposed
global feature, and a technique for learning normalization parameters, accuracy
increases consistently even over our improved versions of the baselines. Our
proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow
and PASCAL-Context with small additional computational cost over baselines, and
near current state-of-the-art performance on PASCAL VOC 2012 semantic
segmentation with a simple approach. Code is available at
https://github.com/weiliu89/caffe/tree/fcn .
