Deep convolutional neural networks have become a key element in the recent
breakthrough of salient object detection. However, existing CNN-based methods
are based on either patch-wise (region-wise) training and inference or fully
convolutional networks. Methods in the former category are generally
time-consuming due to severe storage and computational redundancies among
overlapping patches. To overcome this deficiency, methods in the second
category attempt to directly map a raw input image to a predicted dense
saliency map in a single network forward pass. Though being very efficient, it
is arduous for these methods to detect salient objects of different scales or
salient regions with weak semantic information. In this paper, we develop
hybrid contrast-oriented deep neural networks to overcome the aforementioned
limitations. Each of our deep networks is composed of two complementary
components, including a fully convolutional stream for dense prediction and a
segment-level spatial pooling stream for sparse saliency inference. We further
propose an attentional module that learns weight maps for fusing the two
saliency predictions from these two streams. A tailored alternate scheme is
designed to train these deep networks by fine-tuning pre-trained baseline
models. Finally, a customized fully connected CRF model incorporating a salient
contour feature embedding can be optionally applied as a post-processing step
to improve spatial coherence and contour positioning in the fused result from
these two streams. Extensive experiments on six benchmark datasets demonstrate
that our proposed model can significantly outperform the state of the art in
terms of all popular evaluation metrics.

本文提出了一种基于深度对比型深度神经网络的反卷积模型进行显著性目标检测的方法，能够解决现有方法中时间和精度的局限性，并且在多个基准数据集上都表现出良好的性能。