Rodrigo Benenson
I am research scientist working on how to scale-up machine scene understanding.
Studied electronics engineering at UTFSM. Did my PhD on driverless cars in a team affiliated to Mines Paristech and INRIA. Did a first post-doc in computer vision at KU Leuven, and a second at Max-Planck-Institut für Informatik, in Saarbrücken, Germany. I joined Google in 2016.
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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 segmentation models and report new insights and caveats; (2) we execute a large-scale annotation campaign with real human annotators, producing masks for 2.5M instances on the OpenImages dataset. We have released this data publicly, forming (at the time of release) 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 while also providing better 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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