  Bounding-box annotation form has been the most frequently used method for
visual object localization tasks. However, bounding-box annotation relies on a
large amount of precisely annotating bounding boxes, and it is expensive and
laborious. It is impossible to be employed in practical scenarios and even
redundant for some applications (such as tiny person localization) that the
size would not matter. Therefore, we propose a novel point-based framework for
the person localization task by annotating each person as a coarse point
(CoarsePoint) instead of an accurate bounding box that can be any point within
the object extent. Then, the network predicts the person's location as a 2D
coordinate in the image. Although this greatly simplifies the data annotation
pipeline, the CoarsePoint annotation inevitably decreases label reliability
(label uncertainty) and causes network confusion during training. As a result,
we propose a point self-refinement approach that iteratively updates point
annotations in a self-paced way. The proposed refinement system alleviates the
label uncertainty and progressively improves localization performance.
Experimental results show that our approach has achieved comparable object
localization performance while saving up to 80$\%$ of annotation cost.
