Semantic Scholar Open Access 2019 251 sitasi

Large-Scale Interactive Object Segmentation With Human Annotators

Rodrigo Benenson Stefan Popov V. Ferrari

Lihat Sumber DOI

Abstrak

Manually annotating object segmentation masks is very time consuming. Interactive object segmentation methods offer a more efficient alternative where a human annotator and a machine segmentation model collaborate. In this paper we make several contributions to interactive segmentation: (1) we systematically explore in simulation the design space of deep interactive segmentation models and report new insights and caveats; (2) we execute a large-scale annotation campaign with real human annotators, producing masks for 2.5M instances on the OpenImages dataset. We released this data publicly, forming the largest existing dataset for instance segmentation. Moreover, by re-annotating part of the COCO dataset, we show that we can produce instance masks 3x faster than traditional polygon drawing tools while also providing better quality. (3) We present a technique for automatically estimating the quality of the produced masks which exploits indirect signals from the annotation process.

Topik & Kata Kunci

Computer Science

Penulis (3)

Rodrigo Benenson

Stefan Popov

V. Ferrari

Format Sitasi

APA MLA BibTeX

Benenson, R., Popov, S., Ferrari, V. (2019). Large-Scale Interactive Object Segmentation With Human Annotators. https://doi.org/10.1109/CVPR.2019.01197

Akses Cepat

Lihat di Sumber doi.org/10.1109/CVPR.2019.01197

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 251×
Sumber Database: Semantic Scholar
DOI: 10.1109/CVPR.2019.01197
Akses: Open Access ✓