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• W611457968 abstract "The semantic image segmentation task presents a trade-off between test time accuracy and training time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time-consuming to obtain; image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of $$12.9,%$$ mIOU over image-level supervision. Further, we demonstrate that models trained with point-level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget." @default.
• W611457968 created "2016-06-24" @default.
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• W611457968 date "2016-01-01" @default.
• W611457968 modified "2023-10-15" @default.
• W611457968 title "What’s the Point: Semantic Segmentation with Point Supervision" @default.
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• W611457968 doi "https://doi.org/10.1007/978-3-319-46478-7_34" @default.
• W611457968 hasPublicationYear "2016" @default.
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