Recently, the dominant DETR-based approaches apply central-concept spatial
prior to accelerate Transformer detector convergency. These methods gradually
refine the reference points to the center of target objects and imbue object
queries with the updated central reference information for spatially
conditional attention. However, centralizing reference points may severely
deteriorate queries' saliency and confuse detectors due to the indiscriminative
spatial prior. To bridge the gap between the reference points of salient
queries and Transformer detectors, we propose SAlient Point-based DETR
(SAP-DETR) by treating object detection as a transformation from salient points
to instance objects. In SAP-DETR, we explicitly initialize a query-specific
reference point for each object query, gradually aggregate them into an
instance object, and then predict the distance from each side of the bounding
box to these points. By rapidly attending to query-specific reference region
and other conditional extreme regions from the image features, SAP-DETR can
effectively bridge the gap between the salient point and the query-based
Transformer detector with a significant convergency speed. Our extensive
experiments have demonstrated that SAP-DETR achieves 1.4 times convergency
speed with competitive performance. Under the standard training scheme,
SAP-DETR stably promotes the SOTA approaches by 1.0 AP. Based on ResNet-DC-101,
SAP-DETR achieves 46.9 AP.

提出基于显著点的 DETR 模型以解决传统模型中参考点集中问题，显著点作为检测器，具有更强的区分能力和更快的收敛速度，实验证明其性能优于 SOTA 方法。