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Exploiting Saliency for Object Segmentation from Image Level Labels

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons134225

Oh,  Seong Joon
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/persons79309

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons127761

Akata,  Zeynep
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
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)

arXiv:1701.08261.pdf
(Preprint), 21MB

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Zitation

Oh, S. J., Benenson, R., Khoreva, A., Akata, Z., Fritz, M., & Schiele, B. (2017). Exploiting Saliency for Object Segmentation from Image Level Labels. Retrieved from http://arxiv.org/abs/1701.08261.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-5610-4
Zusammenfassung
There have been remarkable improvements in the semantic labelling task in the recent years. However, the state of the art methods rely on large-scale pixel-level annotations. This paper studies the problem of training a pixel-wise semantic labeller network from image-level annotations of the present object classes. Recently, it has been shown that high quality seeds indicating discriminative object regions can be obtained from image-level labels. Without additional information, obtaining the full extent of the object is an inherently ill-posed problem due to co-occurrences. We propose using a saliency model as additional information and hereby exploit prior knowledge on the object extent and image statistics. We show how to combine both information sources in order to recover 80% of the fully supervised performance - which is the new state of the art in weakly supervised training for pixel-wise semantic labelling.