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  One-Shot Segmentation in Clutter

Michaelis, C., Bethge, M., & Ecker, A. (2018). One-Shot Segmentation in Clutter. In J. Dy, & A. Krause (Eds.), International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden (pp. 3549-3558). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0001-EEF0-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0001-EEF1-3
Genre: Conference Paper

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http://proceedings.mlr.press/v80/michaelis18a/michaelis18a.pdf (Publisher version)
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 Creators:
Michaelis, C, Author
Bethge, M1, 2, Author           
Ecker, A, Author           
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example. We propose a novel dataset, which we call cluttered Omniglot. Using a baseline architecture combining a Siamese embedding for detection with a U-net for segmentation we show that increasing levels of clutter make the task progressively harder. Using oracle models with access to various amounts of ground-truth information, we evaluate different aspects of the problem and show that in this kind of visual search task, detection and segmentation are two intertwined problems, the solution to each of which helps solving the other. We therefore introduce MaskNet, an improved model that attends to multiple candidate locations, generates segmentation proposals to mask out background clutter and selects among the segmented objects. Our findings suggest that such image recognition models based on an iterative refinement of object detection and foreground segmentation may provide a way to deal with highly cluttered scenes.

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 Dates: 2018-07
 Publication Status: Issued
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Title: 35th International Conference on Machine Learning (ICML 2018)
Place of Event: Stockholm, Sweden
Start-/End Date: 2018-07-10 - 2018-07-15

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Title: International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden
Source Genre: Proceedings
 Creator(s):
Dy, J, Editor
Krause, A, Editor
Affiliations:
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Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: 6046 Volume / Issue: - Sequence Number: - Start / End Page: 3549 - 3558 Identifier: -

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Title: PMLR Proceedings of Machine Learning Research
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Pages: - Volume / Issue: 80 Sequence Number: - Start / End Page: - Identifier: -