Training Object Class Detectors with Click Supervision // Creative AI Podcast Episode #12





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Published on Jun 13, 2017

Having a machine identify an object within an image has made significant progress over the years, however researchers are continuing to work on how to make this happen more accurately and faster, ultimately cheaper. One way of doing that is improving the training data. In their research, we see how the baseline object classification is applied to the image. In most cases, it covers a part of the intended target — but often missing key details. By adding a modest annotation input from humans, in this case asking someone to click on the object, they’ve been able to reduce total annotation time by approximately 10x.


Full Paper: https://arxiv.org/pdf/1704.06189.pdf

Abstract: "Training object class detectors typically requires a large
set of images with objects annotated by bounding boxes.
However, manually drawing bounding boxes is very time
consuming. In this paper we greatly reduce annotation
time by proposing center-click annotations: we ask annotators
to click on the center of an imaginary bounding box
which tightly encloses the object instance. We then incorporate
these clicks into existing Multiple Instance Learning
techniques for weakly supervised object localization, to
jointly localize object bounding boxes over all training images.
Extensive experiments on PASCAL VOC 2007 and
MS COCO show that: (1) our scheme delivers high-quality
detectors, performing substantially better than those produced
by weakly supervised techniques, with a modest extra
annotation effort; (2) these detectors in fact perform in a
range close to those trained from manually drawn bounding
boxes; (3) as the center-click task is very fast, our scheme
reduces total annotation time by 9× to 18×."

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