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Semantic Instance Segmentation via Deep Metric Learning
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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.
Forward citations
Cited by 2 Pith papers
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A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning
SAST detects arbitrarily-shaped scene text in a single forward pass by combining four geometric map predictions with point-to-quad pixel clustering, reaching 80.97 Hmean at 27.63 FPS on SCUT-CTW1500.
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