  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 for
the inconsistency of annotated points. Existing POL methods heavily reply on
accurate key-point annotations which are difficult to define. In this study, we
propose a POL method using coarse point annotations, relaxing the supervision
signals from accurate key points to freely spotted points. To this end, we
propose a coarse point refinement (CPR) approach, which to our best knowledge
is the first attempt to alleviate semantic variance from the perspective of
algorithm. CPR constructs point bags, selects semantic-correlated points, and
produces semantic center points through multiple instance learning (MIL). In
this way, CPR defines a weakly supervised evolution procedure, which ensures
training high-performance object localizer under coarse point supervision.
Experimental results on COCO, DOTA and our proposed SeaPerson dataset validate
the effectiveness of the CPR approach. The dataset and code will be available
at https://github.com/ucas-vg/PointTinyBenchmark/.
