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Simple Does It: Weakly Supervised Instance and Semantic Segmentation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons79309

Khoreva,  Anna
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons79212

Benenson,  Rodrigo
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86681

Hosang,  Jan
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

1603.07485v2
(Preprint), 7MB

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Zitation

Khoreva, A., Benenson, R., Hosang, J., Hein, M., & Schiele, B. (2016). Simple Does It: Weakly Supervised Instance and Semantic Segmentation. Retrieved from http://arxiv.org/abs/1603.07485.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002A-1A7D-5
Zusammenfassung
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose to recursively train a convnet such that outputs are improved after each iteration. We explore which aspects affect the recursive training, and which is the most suitable box-guided segmentation to use as initialisation. Our results improve significantly over previously reported ones, even when using rectangles as rough initialisation. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.