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Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

Authors:

Cong ZhaoAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 237 - 246

https://doi.org/10.1145/3269206.3271672

Published: 17 October 2018 Publication History

Abstract

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.

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Cited By

Liu XHe YLi JYan RLi XHuang H(2024)A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic SegmentationSensors10.3390/s2411338824:11(3388)Online publication date: 24-May-2024
https://doi.org/10.3390/s24113388
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https://dl.acm.org/doi/10.1145/3664647.3680953
Prunella MScardigno RBuongiorno DBrunetti ALongo NCarli RDotoli MBevilacqua V(2023)Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A SurveyIEEE Access10.1109/ACCESS.2023.327174811(43370-43423)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3271748
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Index Terms

Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
      2. Computer vision representations
        Appearance and texture representations

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Published: 17 October 2018

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October 22 - 26, 2018

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

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Cited By

Liu XHe YLi JYan RLi XHuang H(2024)A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic SegmentationSensors10.3390/s2411338824:11(3388)Online publication date: 24-May-2024
https://doi.org/10.3390/s24113388
Cui FYang XWu CXiao LTian XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Advancing Prompt Learning through an External LayerProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680953(807-816)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680953
Prunella MScardigno RBuongiorno DBrunetti ALongo NCarli RDotoli MBevilacqua V(2023)Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A SurveyIEEE Access10.1109/ACCESS.2023.327174811(43370-43423)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3271748
Sayez NVleeschouwer C(2022)Accelerating the creation of instance segmentation training sets through bounding box annotation2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956321(252-258)Online publication date: 21-Aug-2022
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