Search Results for author: Jasper R. R. Uijlings

Found 6 papers, 1 papers with code

Panoptic Image Annotation with a Collaborative Assistant

no code implementations17 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.

Panoptic Segmentation Segmentation

Interactive Full Image Segmentation by Considering All Regions Jointly

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.

Image Segmentation Interactive Segmentation +2

Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation

no code implementations20 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.

Extreme clicking for efficient object annotation

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.

Object

We don't need no bounding-boxes: Training object class detectors using only human verification

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.

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