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

TL;DR

Fluid Annotation is a collaborative human-machine interface that significantly speeds up full image annotation by leveraging strong neural network models and empowering annotators to focus on errors, achieving three times faster results than traditional methods.

Contribution

The paper introduces Fluid Annotation, a novel interface that combines machine assistance with human oversight for efficient, full-image annotation in a single pass.

Findings

Achieves three times faster annotation than LabelMe.

Effectively combines machine predictions with human corrections.

Enables focused annotation effort on machine errors.

Abstract

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.