Papers › Pixel Consensus Voting for Panoptic Segmentation

Pixel Consensus Voting for Panoptic Segmentation

4 Apr 2020CVPR 2020 6arXiv:2004.01849archive 2025-07-28

Haochen Wang, Ruotian Luo, Michael Maire, Greg Shakhnarovich

The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the Generalized Hough transform. Pixels cast discretized, probabilistic votes for the likely regions that contain instance centroids. At the detected peaks that emerge in the voting heatmap, backprojection is applied to collect pixels and produce instance masks. Unlike a sliding window detector that densely enumerates object proposals, our method detects instances as a result of the consensus among pixel-wise votes. We implement vote aggregation and backprojection using native operators of a convolutional neural network. The discretization of centroid voting reduces the training of instance segmentation to pixel labeling, analogous and complementary to FCN-style semantic segmentation, leading to an efficient and unified architecture that jointly models things and stuff. We demonstrate the effectiveness of our pipeline on COCO and Cityscapes Panoptic Segmentation and obtain competitive results. Code will be open-sourced.

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Tasks

Instance SegmentationPanoptic SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Panoptic Segmentation COCO test-dev PCV (ResNet-50) PQ 37.7 #36 of 38 Archive leaderboard report
Panoptic Segmentation COCO test-dev PCV (ResNet-50) PQst 33.1 #36 of 38 Archive leaderboard report
Panoptic Segmentation COCO test-dev PCV (ResNet-50) PQth 40.7 #36 of 38 Archive leaderboard report

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Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingKaiming InitializationMax PoolingReLUResidual BlockResidual Connection

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