no code implementations • 17 Jun 2019 • Jasper R. R. Uijlings, Mykhaylo Andriluka, Vittorio Ferrari
This paper aims to reduce the time to annotate images for panoptic segmentation, which requires annotating segmentation masks and class labels for all object instances and stuff regions.
no code implementations • CVPR 2019 • Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari
We propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions.
no code implementations • 20 Jun 2018 • Mykhaylo Andriluka, Jasper R. R. Uijlings, Vittorio Ferrari
As opposed to performing a series of small annotation tasks in isolation, we propose a unified interface for full image annotation in a single pass.
no code implementations • ICCV 2017 • Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
We crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show that (1) annotation time is only 7s per box, 5x 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.
no code implementations • CVPR 2017 • Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
Training object class detectors typically requires a large set of images with objects annotated by bounding boxes.
1 code implementation • CVPR 2016 • Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
Training object class detectors typically requires a large set of images in which objects are annotated by bounding-boxes.