A Stagewise Refinement Model for Detecting Salient Objects in Images

By Tiantian Wang, Ali Borji, Lihe Zhang, Pingping Zhang, Huchuan Lu. IEEE International Conference on Computer Vision, 2017

416 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/898d3f2560d3568eea53b5781af58811145f4cf6

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In Computer Vision and Pattern Recognition

Citations in the corpus

Salient Object Detection With Pyramid Attention and Salient Edges (2019, methodology): “…MDF [21], LEGS [34], DS [24], DCL [22], ELD [20], MC [57], RFCN [36], DHS [26], HEDS [14], KSR [38], NLDF [29], DLS [15], AMU [54], UCF [55], SRM [37], FSN [8], PAGR [56], RAS [7] and C2S [23]. we use either the implementations with the recommended parameter settings or the saliency maps shared…”
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection (2018, methodology): “Recently, significant progress has been made by deep learning based methods [26, 36, 17, 28, 15, 16, 18, 22, 10, 29, 33, 3], which can be broadly categorized into region-based and Fully Convolutional Network (FCN)-based methods.”
A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection (2020, methodology): “95 SRM [36] 2017 6.”
Suppress and Balance: A Simple Gated Network for Salient Object Detection (2020, methodology): “We compare the proposed algorithm with seventeen state-of-the-art saliency detection methods, including the DCL [26], DSS [20], Amulet [69], SRM [51], DGRL [53], RAS [7], PAGRN [70], BMPM [67], R3Net [12], HRS [66], MLMS [58], PAGE [57], ICNet [55], CPD [59], BANet [45], BASNet [37] and Capsal [68].”
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”
Multi-Scale Interactive Network for Salient Object Detection (2020, methodology): “Following most existing state-of-the-art methods [30, 27, 41, 34, 37, 46, 48, 20, 53, 32, 42, 56], we use the DUTS-”
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (2019, methodology): “The other is average F-measure (denoted as avgF), which is adopted in [35, 36, 42, 43].”
RGB-D salient object detection: A survey (2020, background): “Although significant progress has made in the SOD field over the past several years [50, 73, 73, 77, 119, 136, 142, 150, 151, 157, 168, 169, 174, 176], there is still room for improvement under challenging factors such as complicated background or different lighting conditions”
Progressive Attention Guided Recurrent Network for Salient Object Detection (2018, methodology): “Salient Object Detection methods can be categorized as conventional low-level hand-crafted features based [20, 4, 33, 15, 9, 38, 23, 21] and Convolutional Neural Networks driven [25, 13, 37, 14, 12, 16, 27, 18, 7, 35, 36, 28] approaches.”
Anabranch network for camouflaged object segmentation (2019, methodology): “DHS and WSS reduce accuracy while DSS and SRM slightly increase accuracy (see Table 6).”
Visual Saliency Transformer (2021, methodology): “Most methods [24, 65, 49, 84, 16] leveraged a multi-level feature fusion strategy by using UNet [57] or HED-style [71] network structures.”
Label Decoupling Framework for Salient Object Detection (2020, methodology): “Many CNNsbased methods [15, 39, 26, 27, 28, 40, 4, 21, 7, 42, 16, 32] have achieved remarkable performance by designing different decoders to aggregate multi-level CNN features.”
PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection (2017, methodology): “[38] also use several stages to progressively refine saliency maps by combining local and global context information.”
F3Net: Fusion, Feedback and Focus for Salient Object Detection (2019, methodology): “(Deng et al. 2018) and (Wang et al. 2017b) adopted an iterative strategy to reﬁne the saliency maps step-by-step, using features both from deep layers and shallow layers.”
BASNet: Boundary-Aware Salient Object Detection (2019, methodology): “[64] adopted the pyramid pooling module from [15], in which three-scale pyramid pooling features are concatenated.”
Stacked Cross Refinement Network for Edge-Aware Salient Object Detection (2019, methodology): “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.”
Surface defect saliency of magnetic tile (2018, background): “In fact, most architectures of cutting-edge saliency detection models [38–41] are generated from fully convolutional network [44] (FCN).”