Details

Autor(en) / Beteiligte

• Russakovsky, Olga
• Li-Jia Li
• Li Fei-Fei

Titel

Best of both worlds: Human-machine collaboration for object annotation

Ist Teil von

• 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, p.2121-2131

Ort / Verlag

IEEE

Erscheinungsjahr

2015

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.