Object Localization under Single Coarse Point Supervision
This work addresses the challenge of low-cost data annotation for object localization, offering a practical solution for applications requiring efficient annotation, though it is incremental in refining existing weakly supervised methods.
The paper tackles the problem of semantic variance in point-based object localization by proposing a method that uses coarse point annotations instead of accurate key points, achieving high-performance object localization validated on COCO, DOTA, and a new SeaPerson dataset.
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/.