research-article

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

Authors:

Mykhaylo Andriluka,

Jasper R. R. Uijlings,

Vittorio FerrariAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 1957 - 1966

https://doi.org/10.1145/3240508.3241916

Published: 15 October 2018 Publication History

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 [51,68], 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 ma-chine 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 [11,51], we demonstrate that Fluid Annotation leads to accurate an-notations very efficiently, taking 3x less annotation time than the popular LabelMe interface [70].

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Amin RChen FHirsch LOu CLa TButz A(2024)Integrating Crowd and Machine Learning in an Intelligent Interface: A Case Study of Oil Spill Detection in Satellite ImagesProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656680(1-9)Online publication date: 3-Jun-2024
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Index Terms

Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
      2. Computer vision tasks
        Scene understanding
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces

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cover image ACM Conferences

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

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Published: 15 October 2018

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October 22 - 26, 2018

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Amin RChen FHirsch LOu CLa TButz A(2024)Integrating Crowd and Machine Learning in an Intelligent Interface: A Case Study of Oil Spill Detection in Satellite ImagesProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656680(1-9)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656680
Kim YLee ELee YOh U(2024)Understanding Novice's Annotation Process For 3D Semantic Segmentation Task With Human-In-The-LoopProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645150(444-454)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645150
Wang XKim HRahman SMitra KMiao Z(2024)Human-LLM Collaborative Annotation Through Effective Verification of LLM LabelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641960(1-21)Online publication date: 11-May-2024
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