CPR++: Object Localization via Single Coarse Point Supervision
Xuehui Yu Pengfei Chen Kuiran Wang Xumeng Han Guorong Li Zhenjun Han Qixiang Ye Jianbin Jiao
Point-based object localization (POL), which pursues high-performance object
sensing under low-cost data annotation, has attracted increased attention.
However, the point annotation mode inevitably introduces semantic variance due
to the inconsistency of annotated points. Existing POL heavily rely on strict
annotation rules, which are difficult to define and apply, to handle the
problem. In this study, we propose coarse point refinement (CPR), which to our
best knowledge is the first attempt to alleviate semantic variance from an
algorithmic perspective. CPR reduces the semantic variance by selecting a
semantic centre point in a neighbourhood region to replace the initial
annotated point. Furthermore, We design a sampling region estimation module to
dynamically compute a sampling region for each object and use a cascaded
structure to achieve end-to-end optimization. We further integrate a variance
regularization into the structure to concentrate the predicted scores, yielding
CPR++. We observe that CPR++ can obtain scale information and further reduce
the semantic variance in a global region, thus guaranteeing high-performance
object localization. Extensive experiments on four challenging datasets
validate the effectiveness of both CPR and CPR++. We hope our work can inspire
more research on designing algorithms rather than annotation rules to address
the semantic variance problem in POL. The dataset and code will be public at
github.com/ucas-vg/PointTinyBenchmark.