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InstanceCut: from Edges to Instances with MultiCut

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44919

Levinkov,  Evgeny
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons98382

Andres,  Bjoern
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

arXiv:1611.08272.pdf
(Preprint), 7MB

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Zitation

Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., & Rother, C. (2016). InstanceCut: from Edges to Instances with MultiCut. Retrieved from http://arxiv.org/abs/1611.08272.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-795A-2
Zusammenfassung
This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches. Our approach, we term InstanceCut, represents the problem by two output modalities: (i) an instance-agnostic semantic segmentation and (ii) all instance-boundaries. The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instance-aware edge detection model. To reason globally about the optimal partitioning of an image into instances, we combine these two modalities into a novel MultiCut formulation. We evaluate our approach on the challenging CityScapes dataset. Despite the conceptual simplicity of our approach, we achieve the best result among all published methods, and perform particularly well for rare object classes.