Papers › ParseNet: Looking Wider to See Better

ParseNet: Looking Wider to See Better

15 Jun 2015arXiv:1506.04579archive 2025-07-28

Wei Liu, Andrew Rabinovich, Alexander C. Berg

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 .

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SegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Segmentation PASCAL Context ParseNet mIoU 40.4 #61 of 66 Archive leaderboard report
Semantic Segmentation PASCAL VOC 2012 test ParseNet Mean IoU 69.8% #39 of 51 Archive leaderboard report

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