Saliency Detection with Recurrent Fully Convolutional Networks

By Linzhao Wang, Lijun Wang, Huchuan Lu, Pingping Zhang, Xiang Ruan. European Conference on Computer Vision, 2016

532 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7a7a18e1f3dddcc351403237ea5255099441d5d5

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In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Level Sets for Salient Object Detection (2017, methodology): “However, when applying CNNs to pixel-wise saliency labeling, it may suffer from some limitations.”
Progressive Attention Guided Recurrent Network for Salient Object Detection (2018, methodology): “BL [23], KSR [29], LEGS [25], ELD [12], MDF [13], DS [16], MCDL [37], DCL [14], RFCN [27], DHS [18], UCF [36] and Amulet [35].”
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection (2017, methodology): “As a result, the independent prediction is not satisfactory enough, both quantitatively and visually, and further post-processing is needed [24, 44].”
Instance-Level Salient Object Segmentation (2017, methodology): “Fully convolutional networks have been widely used in image labeling problems and have achieved great successes in salient region detection [30, 33, 45] and object contour detection [47, 50].”
Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking (2020, background): “Wang et al. [32] presented a saliency model using recurrent fully convolutional networks, which can incorporate saliency prior knowledge for more accurate inference and learn to reﬁne the saliency map by correcting its previous errors.”
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection (2018, methodology): “The increased performance benefits from the role that CWM plays in filtering out the noise and cluttered 3/27/2018 main _newlly.html file:///media/wtt/Experiment/desktop/vis/camera/main%20_newlly.html 1/1 ILSVRC2012_test_00000026 ILSVRC2012_test_00047403 ILSVRC2012_test_00057576 ILSVRC2012_test_0005”
Pyramid Feature Attention Network for Saliency Detection (2019, methodology): “To be speciﬁc, (1) the proposed method not only highlights the correct salient object regions clearly, but also well suppresses the saliencies [42] 0.8508 0.0484 0.9249 0.0478 0.9200 0.0392 0.8806 0.0788 0.7740 0.0635 GRL[33] 0.8341 0.0509 0.9230 0.0446 0.9130 0.0377 0.8811 0.0799 0.7788 0.0632 PAGR”
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground (2018, methodology): “Furthermore, RFCN [17] combines two related cues including edges and superpixels into FCN to obtain the good performance (MF = 0.”
Few-Shot Learning via Saliency-Guided Hallucination of Samples (2019, methodology): “As our dataset contains both Image RFCN [37] MNL [46] RBD [47] Figure 3: Saliency maps generated by different methods.”
Reverse Attention for Salient Object Detection (2018, methodology): “We compare the proposed method with 10 state-of-the-art ones, including 9 recent CNN-based approaches, DCL+ [22], DHS [26], SSD [16], RFCN [39], DLS [10], NLDF [30], DSS and DSS+ [8], Amulet [45], UCF [46], and one conventional top approach, DRFI [13], where symbol “+” indicates that the network inc”
Learning Uncertain Convolutional Features for Accurate Saliency Detection (2017, methodology): “Second, instead of simply using FCNs as predictors in [26, 45], our model can learn uncertain convolutional features by using multiple reformulated dropouts, which improve the robustness and accuracy of saliency detection.”
Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection (2020, methodology): “RFCN [32] 9 .”
Stacked Cross Refinement Network for Edge-Aware Salient Object Detection (2019, background): “Then fully convolutional networks (FCNs) [23] based approaches [4, 15, 22, 24, 29, 30, 31, 35, 36, 37] further boost the development of salient object detection.”
Deeply Supervised Salient Object Detection with Short Connections (2016, methodology): “We compare the proposed approach with 7 recent CNNbased methods, including MDF [47], DS [60], DCL [35], ELD [50], MC [49], RFCN [51], and DHS [36].”
PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection (2017, methodology): “We compare our saliency model against other 9 state-of-the-art models, namely, SRM [38], DSS [9], NLDF [26], Amulet [45], UCF [46], DHS [23], RFCN [37], DCL [19], and MDF [18].”
A Bi-Directional Message Passing Model for Salient Object Detection (2018, methodology): “We compare the proposed saliency detection model with 13 state-of-the-art methods, including 11 deep learning based methods (LEGS [27], MDF [15], RFCN [29], ELD [12], DCL [16], DHS [18], NLDF [20], DSS [8], Amulet [37], UCF [38], SRM [30]) and 2 conventional ones (DRFI [9] and BSCA [24]).”