  Salient object detection has seen remarkable progress driven by deep learning
techniques. However, most of deep learning based salient object detection
methods are black-box in nature and lacking in interpretability. This paper
proposes the first self-explanatory saliency detection network that explicitly
exploits low- and high-level features for salient object detection. We
demonstrate that such supportive clues not only significantly enhances
performance of salient object detection but also gives better justified
detection results. More specifically, we develop a multi-stage saliency encoder
to extract multi-scale features which contain both low- and high-level saliency
context. Dense short- and long-range connections are introduced to reuse these
features iteratively. Benefiting from the direct access to low- and high-level
features, the proposed saliency encoder can not only model the object context
but also preserve the boundary. Furthermore, a self-explanatory generator is
proposed to interpret how the proposed saliency encoder or other deep saliency
models making decisions. The generator simulates the absence of interesting
features by preventing these features from contributing to the saliency
classifier and estimates the corresponding saliency prediction without these
features. A comparison function, saliency explanation, is defined to measure
the prediction changes between deep saliency models and corresponding
generator. Through visualizing the differences, we can interpret the capability
of different deep neural networks based saliency detection models and
demonstrate that our proposed model indeed uses more reasonable structure for
salient object detection. Extensive experiments on five popular benchmark
datasets and the visualized saliency explanation demonstrate that the proposed
method provides new state-of-the-art.
