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Extreme clicking for efficient object annotation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationInternational Conference on Computer Vision (ICCV 2017)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4940-4949
Number of pages10
ISBN (Electronic)978-1-5386-1032-9
ISBN (Print)978-1-5386-1033-6
DOIs
Publication statusPublished - 25 Dec 2017
Event2017 IEEE International Conference on Computer Vision - Venice, Italy
Duration: 22 Oct 201729 Oct 2017
http://iccv2017.thecvf.com/

Conference

Conference2017 IEEE International Conference on Computer Vision
Abbreviated titleICCV 2017
CountryItaly
CityVenice
Period22/10/1729/10/17
Internet address

Abstract

Manually annotating object bounding boxes is central to building computer vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s for one high quality box [62]). It involves clicking on imaginary corners of a tight box around the object. This is difficult as these corners are often outside the actual object and several adjustments are required to obtain a tight box. We propose extreme clicking instead: we ask the annotator to click on four physical points on the object: the top, bottom,
left- and right-most points. This task is more natural and these points are easy to find. We crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show that (1) annotation time is only 7s per box, 5× faster than the traditional way of drawing boxes [62]; (2) the quality of the boxes is as good as the original ground-truth drawn the traditional way; (3) detectors trained on our annotations are as accurate as those trained on the original ground-truth. Moreover, our extreme clicking strategy not only yields box coordinates, but also four accurate boundary points. We show (4) how to incorporate them into GrabCut to obtain more accurate segmentations than those delivered when initializing it from bounding boxes; (5) semantic segmentations models trained on these segmentations outperform those trained on segmentations derived from bounding boxes.

Event

2017 IEEE International Conference on Computer Vision

22/10/1729/10/17

Venice, Italy

Event: Conference

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