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Papers/Pixel Consensus Voting for Panoptic Segmentation

Pixel Consensus Voting for Panoptic Segmentation

Haochen Wang, Ruotian Luo, Michael Maire, Greg Shakhnarovich

2020-04-04CVPR 2020 6Panoptic SegmentationSegmentationSemantic SegmentationInstance Segmentation
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Abstract

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.

Results

TaskDatasetMetricValueModel
Semantic SegmentationCOCO test-devPQ37.7PCV (ResNet-50)
Semantic SegmentationCOCO test-devPQst33.1PCV (ResNet-50)
Semantic SegmentationCOCO test-devPQth40.7PCV (ResNet-50)
10-shot image generationCOCO test-devPQ37.7PCV (ResNet-50)
10-shot image generationCOCO test-devPQst33.1PCV (ResNet-50)
10-shot image generationCOCO test-devPQth40.7PCV (ResNet-50)
Panoptic SegmentationCOCO test-devPQ37.7PCV (ResNet-50)
Panoptic SegmentationCOCO test-devPQst33.1PCV (ResNet-50)
Panoptic SegmentationCOCO test-devPQth40.7PCV (ResNet-50)

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