2019

Imbalance Problems in Object Detection: A Review

Oksuz, Kemal, Cam, Baris Can, Kalkan, Sinan et al.

Understand

In this paper, we present a comprehensive review of the imbalance problems in object detection.

To analyze the problems in a systematic manner, we introduce a problem-based taxonomy.
Following this taxonomy, we discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature.
In addition, we identify major open issues regarding the existing imbalance problems as well as imbalance problems that have not been discussed before.

Built on

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Y. Liu, T. Gao, and H. Yang, “Selectnet: Learning to sample from the wild for imbalanced data training,” arXiv , vol. 1905.09872, 2019
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S. S. Mullick, S. Datta, and S. Das, “Generative Adversarial Minority Oversampling,” arXiv , vol. 1903.09730, 2019
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C. Huang, Y. Li, C. L. Chen, and X. Tang, “Deep imbalanced learning for face recognition and attribute prediction,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2019
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W. Zheng, Z. Chen, J. Lu, and J. Zhou, “Hardness-aware deep metric learning,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
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S. Liu, E. Johns, and A. J. Davison, “End-to-end multi-task learning with attention,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
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K. Oksuz, B. C. Cam, S. Kalkan, and E. Akbas, “Generating positive bounding boxes for balanced training of object detectors,” in IEEE Winter Applications on Computer Vision (WACV) , 2020
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