  Region sampling or weighting is significantly important to the success of
modern region-based object detectors. Unlike some previous works, which only
focus on "hard" samples when optimizing the objective function, we argue that
sample weighting should be data-dependent and task-dependent. The importance of
a sample for the objective function optimization is determined by its
uncertainties to both object classification and bounding box regression tasks.
To this end, we devise a general loss function to cover most region-based
object detectors with various sampling strategies, and then based on it we
propose a unified sample weighting network to predict a sample's task weights.
Our framework is simple yet effective. It leverages the samples' uncertainty
distributions on classification loss, regression loss, IoU, and probability
score, to predict sample weights. Our approach has several advantages: (i). It
jointly learns sample weights for both classification and regression tasks,
which differentiates it from most previous work. (ii). It is a data-driven
process, so it avoids some manual parameter tuning. (iii). It can be
effortlessly plugged into most object detectors and achieves noticeable
performance improvements without affecting their inference time. Our approach
has been thoroughly evaluated with recent object detection frameworks and it
can consistently boost the detection accuracy. Code has been made available at
\url{https://github.com/caiqi/sample-weighting-network}.

Computer Vision and Pattern Recognition