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We propose a novel and flexible anchor mechanism named MetaAnchor for objectdetection frameworks. Unlike many previous detectors model anchors via apredefined manner, in MetaAnchor anchor functions could be dynamicallygenerated from the arbitrary customized prior boxes. Taking advantage of weightprediction, MetaAnchor is able to work with most of the anchor-based objectdetection systems such as RetinaNet. Compared with the predefined anchorscheme, we empirically find that MetaAnchor is more robust to anchor settingsand bounding box distributions; in addition, it also shows the potential ontransfer tasks. Our experiment on COCO detection task shows that MetaAnchorconsistently outperforms the counterparts in various scenarios.

MetaAnchor: Learning to Detect Objects with Customized Anchors