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FreeAnchor: Learning to Match Anchors for Visual Object Detection

2019-09-05NeurIPS 2019Code Available0· sign in to hype

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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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
COCO test-devFreeAnchor (ResNeXt-101)box mAP44.8Unverified

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