  Current CNN-based solutions to salient object detection (SOD) mainly rely on
the optimization of cross-entropy loss (CELoss). Then the quality of detected
saliency maps is often evaluated in terms of F-measure. In this paper, we
investigate an interesting issue: can we consistently use the F-measure
formulation in both training and evaluation for SOD? By reformulating the
standard F-measure we propose the relaxed F-measure which is differentiable
w.r.t the posterior and can be easily appended to the back of CNNs as the loss
function. Compared to the conventional cross-entropy loss of which the
gradients decrease dramatically in the saturated area, our loss function, named
FLoss, holds considerable gradients even when the activation approaches the
target. Consequently, the FLoss can continuously force the network to produce
polarized activations. Comprehensive benchmarks on several popular datasets
show that FLoss outperforms the state-of-the-art with a considerable margin.
More specifically, due to the polarized predictions, our method is able to
obtain high-quality saliency maps without carefully tuning the optimal
threshold, showing significant advantages in real-world applications.
