2019

Classification Calibration for Long-tail Instance Segmentation

Wang, Tao, Li, Yu, Kang, Bingyi et al.

Understand

Remarkable progress has been made in object instance detection and segmentation in recent years.

  • However, existing state-of-the-art methods are mostly evaluated with fairly balanced and class-limited benchmarks, such as Microsoft COCO dataset [8].
  • In this report, we investigate the performance drop phenomenon of state-of-the-art two-stage instance segmentation models when processing extreme long-tail training data based on the LVIS [5] dataset, and find a major cause is the inaccurate classification of object proposals.
  • Based on this observation, we propose to calibrate the prediction of classification head to improve recognition performance for the tail classes.

Built on

  • Microsoft COCO: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014

    Earlier work this paper cites.

  • Mask r-cnn

    Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017

    Earlier work this paper cites.

  • COCO-Stuff: Thing and stuff classes in context

    Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018

    Earlier work this paper cites.

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  • MMDetection: Open mmlab detection toolbox and benchmark

    Original

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    Cited alongside, same era.

Then

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