  Existing deep neural network based salient object detection (SOD) methods
mainly focus on pursuing high network accuracy. However, those methods overlook
the gap between network accuracy and prediction confidence, known as the
confidence uncalibration problem. Thus, state-of-the-art SOD networks are prone
to be overconfident. In other words, the predicted confidence of the networks
does not reflect the real probability of correctness of salient object
detection, which significantly hinder their real-world applicability. In this
paper, we introduce an uncertaintyaware deep SOD network, and propose two
strategies from different perspectives to prevent deep SOD networks from being
overconfident. The first strategy, namely Boundary Distribution Smoothing
(BDS), generates continuous labels by smoothing the original binary
ground-truth with respect to pixel-wise uncertainty. The second strategy,
namely Uncertainty-Aware Temperature Scaling (UATS), exploits a relaxed Sigmoid
function during both training and testing with spatially-variant temperature
scaling to produce softened output. Both strategies can be incorporated into
existing deep SOD networks with minimal efforts. Moreover, we propose a new
saliency evaluation metric, namely dense calibration measure C, to measure how
the model is calibrated on a given dataset. Extensive experimental results on
seven benchmark datasets demonstrate that our solutions can not only better
calibrate SOD models, but also improve the network accuracy.
