A Stagewise Refinement Model for Detecting Salient Objects in Images
Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of problems in computer vision, including salient object detection. To detect and segment salient objects accurately, it is necessary to extract and combine high-level semantic features with low-level fine details simultaneously. This happens to be a challenge for CNNs as repeated subsampling operations such as pooling and convolution lead to a significant decrease in the initial image resolution, which results in loss of spatial details and finer structures. To remedy this problem, here we propose to augment feedforward neural networks with a novel pyramid pooling module and a multi-stage refinement mechanism for saliency detection. First, our deep feedward net is used to generate a coarse prediction map with much detailed structures lost. Then, refinement nets are integrated with local context information to refine the preceding saliency maps generated in the master branch in a stagewise manner. Further, a pyramid pooling module is applied for different region-based global context aggregation. Empirical evaluations over five benchmark datasets show that our proposed method compares favorably against the state-of-the-art approaches.
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DUTS
HKU-IS
DUT-OMRON
ECSSD
Results from the Paper
Ranked #14 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
