Learning from Noisy Anchors for One-stage Object Detection

CVPR 2020 Hengduo LiZuxuan WuChen ZhuCaiming XiongRichard SocherLarry S. Davis

State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their intersection-over-union (IoU) with corresponding groundtruth objects. Such a harsh split conditioned on IoU results in binary labels that are potentially noisy and challenging for training... (read more)

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