A single-shot affinity-pyramid network with cascaded graph partition achieves state-of-the-art Cityscapes instance segmentation (37.3 AP, 61.1 PQ with ResNet-101) and outperforms DeeperLab on COCO panoptic segmentation.
Semantic Instance Segmentation via Deep Metric Learning
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abstract
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of "seed points", chosen from a deep, fully convolutional scoring model. We show competitive results on the Pascal VOC instance segmentation benchmark.
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SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
A single-shot affinity-pyramid network with cascaded graph partition achieves state-of-the-art Cityscapes instance segmentation (37.3 AP, 61.1 PQ with ResNet-101) and outperforms DeeperLab on COCO panoptic segmentation.