Semantic Instance Segmentation with a Discriminative Loss Function

8 Aug 2017Bert De BrabandereDavy NevenLuc Van Gool

Semantic instance segmentation remains a challenging task. In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Instance Segmentation Cityscapes test Semantic Instance Segmentation with a Discriminative Loss Function Average Precision 17.5 # 9
Multi-Human Parsing MHP v1.0 DL AP 0.5 47.76% # 4
Lane Detection TuSimple Discriminative loss function Accuracy 96.40% # 5

Methods used in the Paper


METHOD TYPE
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