Reviews: MetaAnchor: Learning to Detect Objects with Customized Anchors

Summary: This paper proposes MetaAnchor, which is a anchor mechanism for object detection. In MetaAnchor, anchor functions are dynamically generated from anchor box bi, which describes the common properties of object boxes associated with i_th bin. It introduces a anchor function generator which maps any bounding box prior bi to the corresponding anchor function. In this paper, the anchor function generator is modeled as two-layer network for residual term R, added to the shared and learnable parameters for the anchor function theta *. The residual term R can also depends on input feature x, which introduces the data-dependent variant of anchor function generator. Using weight prediction mechanism, anchor function generator could be implemented and embedded into existing object detection frameworks for joint optimization.