Papers › PSANet: Point-wise Spatial Attention Network for Scene Parsing

PSANet: Point-wise Spatial Attention Network for Scene Parsing

1 Sep 2018ECCV 2018 9archive 2025-07-28

Hengshuang Zhao, Yi Zhang, Shu Liu, Jianping Shi, Chen Change Loy, Dahua Lin, Jiaya Jia

We notice information flow in convolutional neural networks is restricted inside local neighborhood regions due to the physical design of convolutional filters, which limits the overall understanding of complex scenes. In this paper, we propose the point-wise spatial attention network (PSANet) to relax the local neighborhood constraint. Each position on the feature map is connected to all the other ones through a self-adaptively learned attention mask. Moreover, information propagation in bi-direction for scene parsing is enabled. Information at other positions can be collected to help the prediction of the current position and vice versa, information at the current position can be distributed to assist the prediction of other ones. Our proposed approach achieves top performance on various competitive scene parsing datasets, including ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating its effectiveness and generality.

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Code

hszhao/PSANet officialmentioned in paperpytorch report
open-mmlab/mmsegmentation pytorchApache-2.0 report

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Tasks

Scene ParsingSemantic Segmentation

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Segmentation ADE20K PSANet (ResNet-101) Validation mIoU 43.77 #206 of 235 Archive leaderboard report
Semantic Segmentation ADE20K val PSANet (ResNet-101) mIoU 43.77 #88 of 95 Archive leaderboard report
Semantic Segmentation Cityscapes test PSANet (ResNet-101) Mean IoU (class) 80.1% #52 of 105 Archive leaderboard report

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Methods

Introduced by this paper: PSANet

1x1 ConvolutionAuxiliary ClassifierAverage PoolingBatch NormalizationBottleneck Residual BlockConcatenated Skip ConnectionConvolutionGlobal Average PoolingKaiming InitializationMax PoolingPSANetPoint-wise Spatial AttentionPolynomial Rate DecayRandom Gaussian BlurRandom Horizontal FlipReLUResidual BlockResidual ConnectionSGD with MomentumSyncBNWeight Decay

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