Residual Learning for Salient Object Detection


Most recently developed salient object detection Deep Learning algorithms use multi-scale methodologies and fully convolutional neural networks to enhance performance (FCNs). Predictions at different scales are combined to get a final conclusion. However, there are significant issues with the current multi-scale methods: 1) it is difficult to learn discriminative features and filters directly to regress high-resolution saliency masks for each scale; 2) rescaling the multi-scale features could bring in many redundant and erroneous values, weakening the network's representational capabilities. To gradually improve the coarse prediction, we present a residual learning technique in this study. On a concrete level, we learn to forecast residuals rather than the fine-resolution result directly at each scale in order to correct for differences between the coarse saliency map and the scale-matching ground truth masks. The coarse prediction is generated using a Dilated Convolutional Pyramid Pooling (DCPP) module, and the residual learning is guided by several new Attentional Residual Modules (ARMs). "R 2.Net" stands for Residual Refinement Network. On five publicly available benchmark datasets, we compare the proposed method's performance to that of other recent state-of-the-art methods. No additional post-processing is required for our R 2.Net, which runs at a real-time pace of 33 frames per second on a single GPU.

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