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

arXiv: https://arxiv.org/abs/1806.07527 · HTML: https://mlsanity.com/paper/1806.07527/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 16 of 16)

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- [STEP: Segmenting and Tracking Every Pixel](https://mlsanity.com/paper/2102.11859/) (2021)
- [ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO](https://mlsanity.com/paper/2204.03359/) (2022)
- [PanopticNeRF-360: Panoramic 3D-to-2D Label Transfer in Urban Scenes](https://mlsanity.com/paper/2309.10815/) (2023)
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- [OneLabeler: A Flexible System for Building Data Labeling Tools](https://mlsanity.com/paper/2203.14227/) (2022)
- [Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns](https://mlsanity.com/paper/2303.11678/) (2023)
- [Compensation Learning in Semantic Segmentation](https://mlsanity.com/paper/2304.13428/) (2023)
- [Rethinking Interactive Image Segmentation: Feature Space Annotation](https://mlsanity.com/paper/2101.04378/) (2021)
- [Simulation-Based Optimization of User Interfaces for Quality-Assuring Machine Learning Model Predictions](https://mlsanity.com/paper/2104.01129/) (2021)
- [Clicking Matters:Towards Interactive Human Parsing](https://mlsanity.com/paper/2111.06162/) (2021)
- [Pseudo Pixel-level Labeling for Images with Evolving Content](https://mlsanity.com/paper/2105.09975/) (2021)
- [Benign Adversarial Attack: Tricking Models for Goodness](https://mlsanity.com/paper/2107.11986/) (2021)
- [HAISTA-NET: Human Assisted Instance Segmentation Through Attention](https://mlsanity.com/paper/2305.03105/) (2023)
- [Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation](https://mlsanity.com/paper/2503.06954/) (2025)

## Abstract (arXiv)

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.