Large-scale interactive object segmentation with human annotators
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Manually annotating object segmentation masks is very time consuming. Interactive object segmentation methods offer a more efficient alternative where a human annotator and a machine segmentation model collaborate. In this paper we make several contributions to interactive segmentation: (1) we systematically explore in simulation the design space of deep interactive segmentations models and report new insights and caveats; (2) we execute a large-scale annotation campaign with real human annotators, producing masks for 2.5M new instances on the OpenImages dataset. We plan to release this data, which forms the largest existing dataset for instance segmentation. Moreover, by re-annotating part of the COCO dataset, we show that we can produce instance masks 3 times faster than traditional polygon drawing tools at comparable quality. (3) We present a technique for automatically estimating the quality of the produced masks which exploits indirect signals from the annotation process.
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