Imbalance Problems in Object Detection: A Review

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 solu...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2021-10, Vol.43 (10), p.3388-3415
Main Authors: Oksuz, Kemal, Cam, Baris Can, Kalkan, Sinan, Akbas, Emre
Format: Article
Language:English
Subjects:
class imbalance
Deep learning
Feature extraction
imbalance
Neural networks
Object detection
Object recognition
objective imbalance
Pattern analysis
Pipelines
scale imbalance
spatial imbalance
Taxonomy
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Summary: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. Moreover, in order to keep our review up to date, we provide an accompanying webpage which catalogs papers addressing imbalance problems, according to our problem-based taxonomy. Researchers can track newer studies on this webpage available at: https://github.com/kemaloksuz/ObjectDetectionImbalance .
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2020.2981890