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arXiv · 1909.02466

FreeAnchor: Learning to Match Anchors for Visual Object Detection

Xiaosong Zhang·Fang Wan·Chang Liu·Rongrong Ji·Qixiang Ye
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Abstract

Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins.

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Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye. 2019-09-05. FreeAnchor: Learning to Match Anchors for Visual Object Detection. https://arxiv.org/abs/1909.02466

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