Paper:
MetaAnchor: Learning to Detect Objects with Customized Anchors
We propose a novel and flexible anchor mechanism named MetaAnchor for object detection frameworks. Unlike many previous detectors model anchors via a predefined manner, in MetaAnchor anchor functions could be dynamically generated from the arbitrary customized prior boxes. Taking advantage of weight prediction, MetaAnchor is able to work with most of the anchor-based object detection systems such as RetinaNet. Compared with the predefined anchor scheme, we empirically find that MetaAnchor is more robust to anchor settings and bounding box distributions; in addition, it also shows the potential on transfer tasks. Our experiment on COCO detection task shows that MetaAnchor consistently outperforms the counterparts in various scenarios.

Review:
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. Experiments to apply MetaAnchor to state-of-the-art single-stage detector RetinaNet on COCO dataset shows that MetaAnchor outperforms consistently outperforms the counterparts in various scenarios.  Strengths:  - The technical content of the paper appears to be correct with support of some experiment results. - This paper is clearly written with many details description and discussion on how optimization works and how experiment is performed. - There is some novelty of using neural network to learn anchor function.  Weaknesses:  - As mentioned in section 3.2, MetaAnchor does not show significant improvement for two-stage anchor-based object detection. - The experiment evaluation is only done for one method and on one dataset. It is not very convincing that MetaAnchor is able to work with most of the anchor-based object detection system.  Rebuttal Response:  Overall, I think this approach has value on one-stage frameworks. The experiment is only on COCO, but performed in an extensive way. It would be great to have more consistently good results on another benchmark dataset such as OpenImage. But for this submission now, I am willing the upscale my score from 4 to 6.