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About this paper
Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas
A comprehensive review introduces a novel problem-based taxonomy for imbalance issues in deep learning-based object detection, systematically analyzing existing solutions and identifying open research questions. The work establishes a foundational resource for the field, accompanied by a dynamic online repository.
Key insights
- Imbalance problems are inherent and widespread across various stages of the object detection pipeline, affecting class distribution, object scales, spatial locations, and loss function balancing.
- While solutions for foreground-background class imbalance and regression loss have advanced significantly (e.g., from hard to soft sampling, Lp-norm to IoU-based losses), areas like foreground-foreground class imbalance and objective imbalance remain less explored.
- Imbalance problems are interconnected, with changes in one type affecting others, highlighting the need for unified and holistic solutions rather than isolated approaches.
Summary by alphaXiv.
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