Physics

Image segmentation

Frame (networking)

Segmentation-based object categorization

Scale-space segmentation

Operating system

Epistemology

Philosophy

Artificial intelligence

Exploit

Polygon (computer graphics)

Annotation

Scale (ratio)

Quality (philosophy)

Computer science

Quantum mechanics

Object (grammar)

Process (computing)

Computer vision

Computer security

Telecommunications

Segmentation

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.

Large-Scale Interactive Object Segmentation With Human Annotators