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Learn to Propagate Reliably on Noisy Affinity Graphs

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12360))

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Abstract

Recent works have shown that exploiting unlabeled data through label propagation can substantially reduce the labeling cost, which has been a critical issue in developing visual recognition models. Yet, how to propagate labels reliably, especially on a dataset with unknown outliers, remains an open question. Conventional methods such as linear diffusion lack the capability of handling complex graph structures and may perform poorly when the seeds are sparse. Latest methods based on graph neural networks would face difficulties on performance drop as they scale out to noisy graphs. To overcome these difficulties, we propose a new framework that allows labels to be propagated reliably on large-scale real-world data. This framework incorporates (1) a local graph neural network to predict accurately on varying local structures while maintaining high scalability, and (2) a confidence-based path scheduler that identifies outliers and moves forward the propagation frontier in a prudent way. Both components are learnable and closely coupled. Experiments on both ImageNet and Ms-Celeb-1M show that our confidence guided framework can significantly improve the overall accuracies of the propagated labels, especially when the graph is very noisy.

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Acknowledgment

This work is partially supported by the SenseTime Collaborative Grant on Large-scale Multi-modality Analysis (CUHK Agreement No. TS1610626 & No. TS1712093), the General Research Fund (GRF) of Hong Kong (No. 14203518 & No. 14205719).

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Authors and Affiliations

  1. The Chinese University of Hong Kong, Shatin, Hong Kong

    Lei Yang, Qingqiu Huang, Huaiyi Huang & Dahua Lin

  2. The Chinese University of Hong Kong, Shenzhen, China

    Linning Xu

Authors
  1. Lei Yang
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  2. Qingqiu Huang
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  3. Huaiyi Huang
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  4. Linning Xu
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  5. Dahua Lin
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Corresponding author

Correspondence to Lei Yang .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Yang, L., Huang, Q., Huang, H., Xu, L., Lin, D. (2020). Learn to Propagate Reliably on Noisy Affinity Graphs. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12360. Springer, Cham. https://doi.org/10.1007/978-3-030-58555-6_27

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  • DOI: https://doi.org/10.1007/978-3-030-58555-6_27

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