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PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection
Fully convolutional neural networks (FCNs) have shown outstanding perfor...
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A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection
Existing RGB-D salient object detection (SOD) approaches concentrate on ...
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Reverse Attention for Salient Object Detection
Benefit from the quick development of deep learning techniques, salient ...
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RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
We present an effective method to progressively integrate and refine the...
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Boundary-Aware Salient Object Detection via Recurrent Two-Stream Guided Refinement Network
Recent deep learning based salient object detection methods which utiliz...
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Depth Quality Aware Salient Object Detection
The existing fusion based RGB-D salient object detection methods usually...
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U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection
In this paper, we design a simple yet powerful deep network architecture...
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Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection
In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).
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