  We introduce Fluid Annotation, an intuitive human-machine collaboration
interface for annotating the class label and outline of every object and
background region in an image. Fluid annotation is based on three principles:
(I) Strong Machine-Learning aid. We start from the output of a strong neural
network model, which the annotator can edit by correcting the labels of
existing regions, adding new regions to cover missing objects, and removing
incorrect regions. The edit operations are also assisted by the model. (II)
Full image annotation in a single pass. 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. (III) Empower the annotator. We empower the
annotator to choose what to annotate and in which order. This enables
concentrating on what the machine does not already know, i.e. putting human
effort only on the errors it made. This helps using the annotation budget
effectively. Through extensive experiments on the COCO+Stuff dataset, we
demonstrate that Fluid Annotation leads to accurate annotations very
efficiently, taking three times less annotation time than the popular LabelMe
interface.
