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Creators/Authors contains: "Cho, Jang Hyun"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, ICLR)
    Free, publicly-accessible full text available April 24, 2026
  2. ; ; (, IEEE)
    Full Text Available 
  3. ; (, European Conference on Computer Vision)
    Large-scale object detection and instance segmentation face a severe data imbalance. The finer-grained object classes become, the less frequent they appear in our datasets. However, at test-time, we expect a detector that performs well for all classes and not just the most frequent ones. In this paper, we provide a theoretical understanding of the long-trail detection problem. We show how the commonly used mean average precision evaluation metric on an unknown test set is bound by a margin-based binary classification error on a long-tailed object detection training set. We optimize margin-based binary classification error with a novel surrogate objective called \textbf{Effective Class-Margin Loss} (ECM). The ECM loss is simple, theoretically well-motivated, and outperforms other heuristic counterparts on LVIS v1 benchmark over a wide range of architecture and detectors. 
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    Accepted Manuscript Full Text Available