Pixel Consensus Voting for Panoptic Segmentation
| Autor: | Ruotian Luo, Michael Maire, Greg Shakhnarovich, Haochen Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science media_common.quotation_subject Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network Hough transform law.invention law Voting 0202 electrical engineering electronic engineering information engineering Segmentation 0105 earth and related environmental sciences media_common Pixel business.industry Probabilistic logic Pattern recognition Image segmentation 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.2004.01849 |
| Popis: | 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. Comment: CVPR 2020 |
| Databáze: | OpenAIRE |
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