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Papers/Semantic Instance Segmentation via Deep Metric Learning

Semantic Instance Segmentation via Deep Metric Learning

Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, Kevin P. Murphy

2017-03-30Metric LearningSegmentationSemantic SegmentationInstance SegmentationObject Proposal Generation
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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.

Results

TaskDatasetMetricValueModel
Object DetectionPASCAL VOC 2012, 60 proposals per imageAverage Recall0.667inst-DML
3DPASCAL VOC 2012, 60 proposals per imageAverage Recall0.667inst-DML
2D ClassificationPASCAL VOC 2012, 60 proposals per imageAverage Recall0.667inst-DML
2D Object DetectionPASCAL VOC 2012, 60 proposals per imageAverage Recall0.667inst-DML
16kPASCAL VOC 2012, 60 proposals per imageAverage Recall0.667inst-DML

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