ParseNet：放眼更广，看得更清楚
ParseNet: Looking Wider to See Better
摘要：
我们提出了一种将全局上下文添加到深度卷积网络以进行语义分割的技术。该方法很简单，使用层的平均特征来增强每个位置的特征。此外，我们还研究了训练的几个特性，显著提高了基线网络（例如来自FCN）的性能。当我们添加我们提出的全局特征和学习归一化参数的技术时，准确性甚至比我们改进的基线版本都要一致地提高。我们提出的方法ParseNet在SiftFlow和PASCAL-Context上实现了最先进的性能，并且在基线上具有较小的额外计算成本，并且在PASCAL VOC 2012语义分割上具有接近当前最先进的性能。代码可在https://github.com/weiliu89/caffe/tree/fcn上获得。
英文摘要：
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 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
