Although deep CNNs have brought significant improvement to image saliency
detection, most CNN based models are sensitive to distortion such as
compression and noise. In this paper, we propose an end-to-end generic salient
object segmentation model called Metric Expression Network (MEnet) to deal with
saliency detection with the tolerance of distortion. Within MEnet, a new
topological metric space is constructed, whose implicit metric is determined by
the deep network. As a result, we manage to group all the pixels in the
observed image semantically within this latent space into two regions: a
salient region and a non-salient region. With this architecture, all feature
extractions are carried out at the pixel level, enabling fine granularity of
output boundaries of the salient objects. What's more, we try to give a general
analysis for the noise robustness of the network in the sense of Lipschitz and
Jacobian literature. Experiments demonstrate that robust salient maps
facilitating object segmentation can be generated by the proposed metric. Tests
on several public benchmarks show that MEnet has achieved desirable
performance. Furthermore, by direct computation and measuring the robustness,
the proposed method outperforms previous CNN-based methods on distorted inputs.

该论文提出了一种新的端到端泛化显著性目标分割模型 MEnet，使用一种新的拓扑空间来确定深度网络所确定的隐式度量，可忍受压缩和噪声等扰动，实现在像素级别上对显著区域和非显著区域的聚类，进而能够对出现在图像中的显著对象进行分割，同时该方法能够更好地处理扭曲的输入图像。