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Papers/Panoptic Segmentation with a Joint Semantic and Instance S...

Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network

Daan de Geus, Panagiotis Meletis, Gijs Dubbelman

2018-09-06CoRR 2019 2Panoptic SegmentationSegmentationSemantic SegmentationInstance Segmentation
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Abstract

We present a single network method for panoptic segmentation. This method combines the predictions from a jointly trained semantic and instance segmentation network using heuristics. Joint training is the first step towards an end-to-end panoptic segmentation network and is faster and more memory efficient than training and predicting with two networks, as done in previous work. The architecture consists of a ResNet-50 feature extractor shared by the semantic segmentation and instance segmentation branch. For instance segmentation, a Mask R-CNN type of architecture is used, while the semantic segmentation branch is augmented with a Pyramid Pooling Module. Results for this method are submitted to the COCO and Mapillary Joint Recognition Challenge 2018. Our approach achieves a PQ score of 17.6 on the Mapillary Vistas validation set and 27.2 on the COCO test-dev set.

Results

TaskDatasetMetricValueModel
Semantic SegmentationMapillary valPQ17.6JSIS-Net (ResNet-50)
Semantic SegmentationCOCO test-devPQ27.2JSIS-Net
Semantic SegmentationCOCO test-devPQst23.4JSIS-Net
Semantic SegmentationCOCO test-devPQth29.6JSIS-Net
10-shot image generationMapillary valPQ17.6JSIS-Net (ResNet-50)
10-shot image generationCOCO test-devPQ27.2JSIS-Net
10-shot image generationCOCO test-devPQst23.4JSIS-Net
10-shot image generationCOCO test-devPQth29.6JSIS-Net
Panoptic SegmentationMapillary valPQ17.6JSIS-Net (ResNet-50)
Panoptic SegmentationCOCO test-devPQ27.2JSIS-Net
Panoptic SegmentationCOCO test-devPQst23.4JSIS-Net
Panoptic SegmentationCOCO test-devPQth29.6JSIS-Net

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