where and represent the weighing factor and focusing parameter, respectively. The other notations refer to Equation . In practice, and are separately set to 0.25 and 2 , according to the parameter setting in [48]. The final loss function for ROI Head is obtained by adding the above loss and :
The losses of ROI Head are accumulated and back-propagated to train the proposed framework.

3.4 Pi-Index: the strategy of estimating class prior

In the Faster RCNN framework, the PU loss is usually applied to the anchor-based RPN [34], [49]. In other words, classification and regression are performed separately for each anchor in RPN. Incomplete annotations should thus be converted from annotated boxes to anchors. There are lots of positive anchors assigned negative labels as background because a part of the targets lacks their annotations. For the PU classification loss of RPN, the class prior is defined as the percentage of both correctly and incorrectly labeled positive anchors.

A. Background: the existing methods

Zhao et al. considered the class prior as a hyper-parameter and determined it by grid search based on a validation set [49]. The estimation granularity of the class prior relies on the interval of the grid search.
Table 1: Performance (AP) of the method in [34] with different confidence thresholds (0.1-0.5) and annotation percents (0.3, 0.5, 0.7 and 1).