Most of existing salient object detection models have achieved great progress
by aggregating multi-level features extracted from convolutional neural
networks. However, because of the different receptive fields of different
convolutional layers, there exists big differences between features generated
by these layers. Common feature fusion strategies (addition or concatenation)
ignore these differences and may cause suboptimal solutions. In this paper, we
propose the F3Net to solve above problem, which mainly consists of cross
feature module (CFM) and cascaded feedback decoder (CFD) trained by minimizing
a new pixel position aware loss (PPA). Specifically, CFM aims to selectively
aggregate multi-level features. Different from addition and concatenation, CFM
adaptively selects complementary components from input features before fusion,
which can effectively avoid introducing too much redundant information that may
destroy the original features. Besides, CFD adopts a multi-stage feedback
mechanism, where features closed to supervision will be introduced to the
output of previous layers to supplement them and eliminate the differences
between features. These refined features will go through multiple similar
iterations before generating the final saliency maps. Furthermore, different
from binary cross entropy, the proposed PPA loss doesn't treat pixels equally,
which can synthesize the local structure information of a pixel to guide the
network to focus more on local details. Hard pixels from boundaries or
error-prone parts will be given more attention to emphasize their importance.
F3Net is able to segment salient object regions accurately and provide clear
local details. Comprehensive experiments on five benchmark datasets demonstrate
that F3Net outperforms state-of-the-art approaches on six evaluation metrics.

本文提出 F3Net 算法，通过交叉特征模块和级联反馈解码器，结合像素位置感知损失函数，实现高效的显著目标检测，能够在各项指标上优于现有算法。