{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:14:01Z","timestamp":1778285641524,"version":"3.51.4"},"reference-count":92,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T00:00:00Z","timestamp":1746144000000},"content-version":"vor","delay-in-days":121,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZ22F020003"],"award-info":[{"award-number":["LZ22F020003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271180"],"award-info":[{"award-number":["62271180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>With the advancement of modern camera technology, the resolution and quality of images have been significantly improved. High\u2010resolution images can provide more detailed and clearer information, but they may also introduce more noise, which interferes with the detection and localization of salient objects. To address this issue, existing high\u2010resolution salient object detection methods either design complex network structures or adopt multi\u2010modal fusion. However, these approaches often consume significant computing and storage resources. This leads to redundancy of irrelevant features and loss of critical details. In this paper, we propose a network called bidirectionally guided multi\u2010scale feature decoding network for high\u2010resolution salient object detection. The model incorporates a bidirectional guidance method to explore the complementarity between encoding and decoding features, thereby achieving a comprehensive combination and enhancement of features. Additionally, in the decoder, multi\u2010scale encoding features are obtained and utilized sequentially to enhance feature learning and improve the accuracy of salient object detection. Specifically, our model consists of an encoder, a guided multi\u2010scale feature enhancement (GMFE) module, a guided feature fusion (GFF) module, and a multi\u2010scale feature decoder (MFD) module. First, multi\u2010scale encoding features are extracted through the encoder. These features are then fed into the GMFE module to enhance the multi\u2010scale encoding features under the guidance of saliency map derived from the decoding features of the previous layer. Subsequently, in the GFF module, the enhanced encoding features are fused with the decoding features from the previous layer. Finally, in the MFD module, the bidirectionally guided multi\u2010scale encoding features is integrated to generate an accurate saliency map. Experiments on two high\u2010resolution and two low\u2010resolution datasets demonstrate that our model outperforms on high\u2010resolution datasets while maintaining competitive performance on low\u2010resolution datasets, underscoring its effectiveness across varying image\u00a0qualities.<\/jats:p>","DOI":"10.1049\/ipr2.70094","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:47:06Z","timestamp":1746233226000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bidirectionally Guided Multi\u2010Scale Feature Decoding Network for High\u2010Resolution Salient Object Detection"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1811-2387","authenticated-orcid":false,"given":"Jiangping","family":"Tang","sequence":"first","affiliation":[{"name":"School of Automation Hangzhou Dianzi University Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyao","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science Hangzhou Dianzi University Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation Hangzhou Dianzi University Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liuxin","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Automation Hangzhou Dianzi University Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation Hangzhou Dianzi University Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Cyberspace Hangzhou Dianzi University Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,5,2]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","unstructured":"W.F\u00f6rstner \u201cA Framework for Low Level Feature Extraction \u201d inComputer Vision\u2014ECCV'94: Third European Conference on Computer Vision Stockholm(Springer 1994) 383\u2013394.","DOI":"10.1007\/BFb0028370"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"X.HouandL.Zhang \u201cSaliency Detection: A Spectral Residual Approach \u201d in2007 IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2007) 1\u20138.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"S. A.ChatzichristofisandY. S.Boutalis \u201cFCTH: Fuzzy Color and Texture Histogram\u2010A Low Level Feature for Accurate Image Retrieval \u201d in2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services(IEEE 2008) 191\u2013196.","DOI":"10.1109\/WIAMIS.2008.24"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"N.Riche M.Duvinage M.Mancas B.Gosselin andT.Dutoit \u201cSaliency and Human Fixations: State\u2010of\u2010the\u2010Art and Study of Comparison Metrics \u201d inProceedings of the IEEE International Conference on Computer Vision(IEEE 2013) 1153\u20131160.","DOI":"10.1109\/ICCV.2013.147"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1167\/8.7.32"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"C.Guo Q.Ma andL.Zhang \u201cSpatio\u2010Temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform \u201d in2008 IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2008) 1\u20138.","DOI":"10.1109\/CVPR.2008.4587715"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"R.Achanta S.Hemami F.Estrada andS.Susstrunk \u201cFrequency\u2010Tuned Salient Region Detection \u201d in2009 IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2009) 1597\u20131604.","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.147"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2424870"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.730558"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.89"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/35058500"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.visres.2008.09.007"},{"issue":"1","key":"e_1_2_10_15_1","first-page":"194","article-title":"Image Signature: Highlighting Sparse Salient Regions","volume":"34","author":"Hou X.","year":"2011","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_10_16_1","doi-asserted-by":"crossref","unstructured":"C.Yang L.Zhang H.Lu X.Ruan andM.\u2010H.Yang \u201cSaliency Detection Via Graph\u2010Based Manifold Ranking \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2013) 3166\u20133173.","DOI":"10.1109\/CVPR.2013.407"},{"key":"e_1_2_10_17_1","doi-asserted-by":"crossref","unstructured":"J.Harel C.Koch andP.Perona \u201cGraph\u2010Based Visual Saliency \u201dAdvances in Neural Information Processing Systems19(ACM 2006) 545\u2013552.","DOI":"10.7551\/mitpress\/7503.003.0073"},{"key":"e_1_2_10_18_1","doi-asserted-by":"crossref","unstructured":"Y.Wei F.Wen W.Zhu andJ.Sun \u201cGeodesic Saliency Using Background Priors \u201d inComputer Vision\u2013ECCV 2012: 12th European Conference on Computer Vision(Springer 2012) 29\u201342.","DOI":"10.1007\/978-3-642-33712-3_3"},{"issue":"2","key":"e_1_2_10_19_1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/TPAMI.2010.70","article-title":"Learning to Detect a Salient Object","volume":"33","author":"Liu T.","year":"2010","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_10_20_1","doi-asserted-by":"crossref","unstructured":"J.Liu B.Kuipers andS.Savarese \u201cRecognizing Human Actions by Attributes \u201d inCVPR 2011(IEEE 2011) 3337\u20133344.","DOI":"10.1109\/CVPR.2011.5995353"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107130"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2018.2875586"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2846598"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"P.Zhang D.Wang H.Lu H.Wang andB.Yin \u201cLearning Uncertain Convolutional Features for Accurate Saliency Detection \u201d inProceedings of the IEEE International Conference on Computer Vision(IEEE 2017) 212\u2013221.","DOI":"10.1109\/ICCV.2017.32"},{"key":"e_1_2_10_25_1","doi-asserted-by":"crossref","unstructured":"P.Zhang D.Wang H.Lu H.Wang andX.Ruan \u201cAmulet: Aggregating Multi\u2010Level Convolutional Features for Salient Object Detection \u201d inProceedings of the IEEE International Conference on Computer Vision(IEEE 2017) 202\u2013211.","DOI":"10.1109\/ICCV.2017.31"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3207605"},{"key":"e_1_2_10_27_1","doi-asserted-by":"crossref","unstructured":"T.Wang A.Borji L.Zhang P.Zhang andH.Lu \u201cA Stagewise Refinement Model for Detecting Salient Objects in Images \u201d inProceedings of the IEEE International Conference on Computer Vision(IEEE 2017) 4019\u20134028.","DOI":"10.1109\/ICCV.2017.433"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2893535"},{"key":"e_1_2_10_29_1","doi-asserted-by":"crossref","unstructured":"P.Zhang W.Liu H.Lu andC.Shen \u201cSalient Object Detection by Lossless Feature Reflection \u201darXiv:1802.06527(2018).","DOI":"10.24963\/ijcai.2018\/160"},{"key":"e_1_2_10_30_1","doi-asserted-by":"crossref","unstructured":"H.Zhang M.Cisse Y. N.Dauphin andD.Lopez\u2010Paz \u201cMixup: Beyond Empirical Risk Minimization \u201darXiv:1710.09412(2017).","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"e_1_2_10_31_1","doi-asserted-by":"crossref","unstructured":"E. D.Cubuk B.Zoph D.Mane V.Vasudevan andQ. V.Le \u201cAutoaugment: Learning Augmentation Policies From Data \u201darXiv:1805.09501(2018).","DOI":"10.1109\/CVPR.2019.00020"},{"key":"e_1_2_10_32_1","doi-asserted-by":"crossref","unstructured":"E. D.Cubuk B.Zoph J.Shlens andQ. V.Le \u201cRandaugment: Practical Automated Data Augmentation With a Reduced Search Space \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops(IEEE 2020) 702\u2013703.","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"e_1_2_10_33_1","doi-asserted-by":"crossref","unstructured":"J.\u2010J.Liu Q.Hou M.\u2010M.Cheng J.Feng andJ.Jiang \u201cA Simple Pooling\u2010Based Design for Real\u2010Time Salient Object Detection \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2019) 3917\u20133926.","DOI":"10.1109\/CVPR.2019.00404"},{"key":"e_1_2_10_34_1","doi-asserted-by":"crossref","unstructured":"G.LiandY.Yu \u201cDeep Contrast Learning for Salient Object Detection \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2016) 478\u2013487.","DOI":"10.1109\/CVPR.2016.58"},{"key":"e_1_2_10_35_1","doi-asserted-by":"crossref","unstructured":"S.Gao P.Zhang T.Yan andH.Lu \u201cMulti\u2010Scale and Detail\u2010Enhanced Segment Anything Model for Salient Object Detection \u201d inProceedings of the 32nd ACM International Conference on Multimedia(ACM 2024) 9894\u20139903.","DOI":"10.1145\/3664647.3680650"},{"key":"e_1_2_10_36_1","doi-asserted-by":"crossref","unstructured":"P.Zhang T.Yan Y.Liu andH.Lu \u201cFantastic Animals and Where to Find Them: Segment Any Marine Animal With Dual Sam \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2024) 2578\u20132587.","DOI":"10.1109\/CVPR52733.2024.00249"},{"key":"e_1_2_10_37_1","doi-asserted-by":"crossref","unstructured":"J. M. J.Valanarasu P.Oza I.Hacihaliloglu andV. M.Patel \u201cMedical Transformer: Gated Axial\u2010Attention for Medical Image Segmentation \u201d inMedical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference(Springer 2021) 36\u201346.","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"e_1_2_10_38_1","doi-asserted-by":"crossref","unstructured":"Z.Xu D.Lu Y.Wang et\u00a0al. \u201cNoisy Labels are Treasure: Mean\u2010Teacher\u2010Assisted Confident Learning for Hepatic Vessel Segmentation \u201d inMedical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference(Springer 2021) 3\u201313.","DOI":"10.1007\/978-3-030-87193-2_1"},{"key":"e_1_2_10_39_1","doi-asserted-by":"crossref","unstructured":"Y.Tang R.Gao H.Lee et\u00a0al. \u201cPancreas CT Segmentation by Predictive Phenotyping \u201d inMedical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference(Springer 2021) 25\u201335.","DOI":"10.1007\/978-3-030-87193-2_3"},{"key":"e_1_2_10_40_1","doi-asserted-by":"crossref","unstructured":"T.Nguyen B.\u2010S.Hua andN.Le \u201c3D\u2010Ucaps: 3D Capsules Unet for Volumetric Image Segmentation \u201d inMedical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference(Springer 2021) 548\u2013558.","DOI":"10.1007\/978-3-030-87193-2_52"},{"key":"e_1_2_10_41_1","doi-asserted-by":"crossref","unstructured":"D.Karimi S. D.Vasylechko andA.Gholipour \u201cConvolution\u2010Free Medical Image Segmentation Using Transformers \u201d inMedical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference(Springer 2021) 78\u201388.","DOI":"10.1007\/978-3-030-87193-2_8"},{"key":"e_1_2_10_42_1","unstructured":"M.Bojarski P.Yeres A.Choromanska et\u00a0al. \u201cExplaining How a Deep Neural Network Trained With End\u2010to\u2010End Learning Steers A Car \u201darXiv:1704.07911(2017)."},{"key":"e_1_2_10_43_1","doi-asserted-by":"crossref","unstructured":"R.Kulkarni S.Dhavalikar andS.Bangar \u201cTraffic Light Detection and Recognition for Self Driving Cars Using Deep Learning \u201d in2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)(IEEE 2018) 1\u20134.","DOI":"10.1109\/ICCUBEA.2018.8697819"},{"key":"e_1_2_10_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2962338"},{"key":"e_1_2_10_45_1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"e_1_2_10_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3164407"},{"key":"e_1_2_10_47_1","doi-asserted-by":"crossref","unstructured":"X.Deng P.Zhang W.Liu andH.Lu \u201cRecurrent Multi\u2010Scale Transformer for High\u2010Resolution Salient Object Detection \u201d inProceedings of the 31st ACM International Conference on Multimedia(ACM 2023) 7413\u20137423.","DOI":"10.1145\/3581783.3611983"},{"key":"e_1_2_10_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3045624"},{"key":"e_1_2_10_49_1","doi-asserted-by":"crossref","unstructured":"Y.Zeng P.Zhang J.Zhang Z.Lin andH.Lu \u201cTowards High\u2010Resolution Salient Object Detection \u201d inProceedings of the IEEE\/CVF International Conference on Computer Vision(IEEE 2019) 7234\u20137243.","DOI":"10.1109\/ICCV.2019.00733"},{"key":"e_1_2_10_50_1","doi-asserted-by":"crossref","unstructured":"X.Zhao Y.Pang L.Zhang H.Lu andL.Zhang \u201cSuppress and Balance: A Simple Gated Network for Salient Object Detection \u201d inComputer Vision\u2013ECCV 2020: 16th European Conference(Springer 2020) 35\u201351.","DOI":"10.1007\/978-3-030-58536-5_3"},{"key":"e_1_2_10_51_1","doi-asserted-by":"crossref","unstructured":"N.LiuandJ.Han \u201cDhsnet: Deep Hierarchical Saliency Network for Salient Object Detection \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2016) 678\u2013686.","DOI":"10.1109\/CVPR.2016.80"},{"key":"e_1_2_10_52_1","doi-asserted-by":"crossref","unstructured":"B.Wei K.Hao X.\u2010s.Tang andL.Ren \u201cFabric Defect Detection Based on Faster RCNN \u201d inArtificial Intelligence on Fashion and Textiles: Proceedings of the Artificial Intelligence on Fashion and Textiles (AIFT) Conference 2018(Springer 2019) 45\u201351.","DOI":"10.1007\/978-3-319-99695-0_6"},{"key":"e_1_2_10_53_1","doi-asserted-by":"crossref","unstructured":"B.Xu H.Liang R.Liang andP.Chen \u201cLocate Globally Segment Locally: A Progressive Architecture With Knowledge Review Network for Salient Object Detection \u201d inProceedings of the AAAI Conference on Artificial Intelligence vol.35(AAAI 2021) 3004\u20133012.","DOI":"10.1609\/aaai.v35i4.16408"},{"key":"e_1_2_10_54_1","doi-asserted-by":"crossref","unstructured":"X.Zhang T.Wang J.Qi H.Lu andG.Wang \u201cProgressive Attention Guided Recurrent Network for Salient Object Detection \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2018) 714\u2013722.","DOI":"10.1109\/CVPR.2018.00081"},{"key":"e_1_2_10_55_1","doi-asserted-by":"crossref","unstructured":"Y.Pang X.Zhao L.Zhang andH.Lu \u201cMulti\u2010Scale Interactive Network for Salient Object Detection \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2020) 9413\u20139422.","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"e_1_2_10_56_1","doi-asserted-by":"crossref","unstructured":"C.Xie C.Xia M.Ma Z.Zhao X.Chen andJ.Li \u201cPyramid Grafting Network for One\u2010Stage High Resolution Saliency Detection \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2022) 11717\u201311726.","DOI":"10.1109\/CVPR52688.2022.01142"},{"key":"e_1_2_10_57_1","unstructured":"L.Tang B.Li Y.Zhong S.Ding andM.Song \u201cDisentangled High Quality Salient Object Detection \u201d inProceedings of the IEEE\/CVF International Conference on Computer Vision(IEEE 2021) 3580\u20133590."},{"key":"e_1_2_10_58_1","doi-asserted-by":"crossref","unstructured":"K.Sun B.Xiao D.Liu andJ.Wang \u201cDeep High\u2010Resolution Representation Learning for Human Pose Estimation \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2019) 5693\u20135703.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"e_1_2_10_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.08.038"},{"key":"e_1_2_10_60_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07915-w"},{"key":"e_1_2_10_61_1","unstructured":"C.Xia C.Xie Z.He T.Yu andJ.Li \u201cPGNeXt: High\u2010Resolution Salient Object Detection Via Pyramid Grafting Network \u201darXiv:2408.01137(2024)."},{"key":"e_1_2_10_62_1","unstructured":"K.SimonyanandA.Zisserman \u201cVery Deep Convolutional Networks for Large\u2010Scale Image Recognition \u201darXiv:1409.1556(2014)."},{"key":"e_1_2_10_63_1","doi-asserted-by":"crossref","unstructured":"S.Liu D.Huang andY.Wang \u201cReceptive Field Block Net for Accurate and Fast Object Detection \u201d inProceedings of the European Conference on Computer Vision (ECCV)(Springer 2018) 385\u2013400.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"e_1_2_10_64_1","doi-asserted-by":"crossref","unstructured":"S.Woo J.Park J.\u2010Y.Lee andI. S.Kweon \u201cCBAM: Convolutional Block Attention Module \u201d inProceedings of the European Conference on Computer Vision (ECCV)(2018) 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_2_10_65_1","doi-asserted-by":"crossref","unstructured":"X.Qin Z.Zhang C.Huang C.Gao M.Dehghan andM.Jagersand \u201cBASNet: Boundary\u2010Aware Salient Object Detection \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2019) 7479\u20137489.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"e_1_2_10_66_1","doi-asserted-by":"crossref","unstructured":"Q.Hou M.\u2010M.Cheng X.Hu A.Borji Z.Tu andP. H.Torr \u201cDeeply Supervised Salient Object Detection With Short Connections \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2017) 3203\u20133212.","DOI":"10.1109\/CVPR.2017.563"},{"key":"e_1_2_10_67_1","doi-asserted-by":"crossref","unstructured":"J.\u2010X.Zhao J.\u2010J.Liu D.\u2010P.Fan Y.Cao J.Yang andM.\u2010M.Cheng \u201cEGNet: Edge Guidance Network for Salient Object Detection \u201d inProceedings of the IEEE\/CVF International Conference on Computer Vision(IEEE 2019) 8779\u20138788.","DOI":"10.1109\/ICCV.2019.00887"},{"key":"e_1_2_10_68_1","doi-asserted-by":"crossref","unstructured":"G.M\u00e1ttyus W.Luo andR.Urtasun \u201cDeeproadmapper: Extracting Road Topology From Aerial Images \u201d inProceedings of the IEEE International Conference on Computer Vision(IEEE 2017) 3438\u20133446.","DOI":"10.1109\/ICCV.2017.372"},{"key":"e_1_2_10_69_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-005-5724-z"},{"key":"e_1_2_10_70_1","unstructured":"D. P.Kingma \u201cAdam: A Method for Stochastic Optimization \u201darXiv:1412.6980(2014)."},{"key":"e_1_2_10_71_1","doi-asserted-by":"crossref","unstructured":"L.Wang H.Lu Y.Wang et\u00a0al. \u201cLearning to Detect Salient Objects With Image\u2010Level Supervision \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2017) 136\u2013145.","DOI":"10.1109\/CVPR.2017.404"},{"key":"e_1_2_10_72_1","doi-asserted-by":"crossref","unstructured":"G.LiandY.Yu \u201cVisual Saliency Based on Multiscale Deep Features \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2015) 5455\u20135463.","DOI":"10.1109\/CVPR.2015.7299184"},{"key":"e_1_2_10_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2487833"},{"key":"e_1_2_10_74_1","doi-asserted-by":"crossref","unstructured":"D.\u2010P.Fan M.\u2010M.Cheng Y.Liu T.Li andA.Borji \u201cStructure\u2010Measure: A New Way to Evaluate Foreground Maps \u201d inProceedings of the IEEE International Conference on Computer Vision(IEEE 2017) 4548\u20134557.","DOI":"10.1109\/ICCV.2017.487"},{"key":"e_1_2_10_75_1","doi-asserted-by":"crossref","unstructured":"D.\u2010P.Fan C.Gong Y.Cao B.Ren M.\u2010M.Cheng andA.Borji \u201cEnhanced\u2010Alignment Measure for Binary Foreground Map Evaluation \u201d inProceedings of the 27th International Joint Conference on Artificial Intelligence IJCAI'18(ACM 2018) 698\u2013704.","DOI":"10.24963\/ijcai.2018\/97"},{"key":"e_1_2_10_76_1","doi-asserted-by":"crossref","unstructured":"L.Wang L.Wang H.Lu P.Zhang andX.Ruan \u201cSaliency Detection With Recurrent Fully Convolutional Networks \u201d inComputer Vision\u2013ECCV 2016: 14th European Conference(Springer 2016) 825\u2013841.","DOI":"10.1007\/978-3-319-46493-0_50"},{"key":"e_1_2_10_77_1","doi-asserted-by":"crossref","unstructured":"Z.Luo A.Mishra A.Achkar J.Eichel S.Li andP.\u2010M.Jodoin \u201cNon\u2010Local Deep Features for Salient Object Detection \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2017) 6609\u20136617.","DOI":"10.1109\/CVPR.2017.698"},{"key":"e_1_2_10_78_1","doi-asserted-by":"crossref","unstructured":"S.Chen X.Tan B.Wang andX.Hu \u201cReverse Attention for Salient Object Detection \u201d inProceedings of the European Conference on Computer Vision (ECCV)(Springer 2018) 234\u2013250.","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"e_1_2_10_79_1","doi-asserted-by":"crossref","unstructured":"T.Wang L.Zhang S.Wang et\u00a0al. \u201cDetect Globally Refine Locally: A Novel Approach to Saliency Detection \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2018) 3127\u20133135.","DOI":"10.1109\/CVPR.2018.00330"},{"key":"e_1_2_10_80_1","doi-asserted-by":"crossref","unstructured":"H.Wu S.Zheng J.Zhang andK.Huang \u201cFast End\u2010to\u2010End Trainable Guided Filter \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2018) 1838\u20131847.","DOI":"10.1109\/CVPR.2018.00197"},{"key":"e_1_2_10_81_1","doi-asserted-by":"crossref","unstructured":"Z.Wu L.Su andQ.Huang \u201cStacked Cross Refinement Network for Edge\u2010Aware Salient Object Detection \u201d inProceedings of the IEEE\/CVF International Conference on Computer Vision(IEEE 2019) 7264\u20137273.","DOI":"10.1109\/ICCV.2019.00736"},{"key":"e_1_2_10_82_1","doi-asserted-by":"crossref","unstructured":"Z.Wu L.Su andQ.Huang \u201cCascaded Partial Decoder for Fast and Accurate Salient Object Detection \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2019) 3907\u20133916.","DOI":"10.1109\/CVPR.2019.00403"},{"key":"e_1_2_10_83_1","doi-asserted-by":"crossref","unstructured":"J.Wei S.Wang andQ.Huang \u201cF3Net${\\rm F}^3{\\rm Net}$: Fusion Feedback and Focus for Salient Object Detection \u201d inProceedings of the AAAI Conference on Artificial Intelligence vol.34(AAAI Publication 2020) 12321\u201312328.","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"e_1_2_10_84_1","doi-asserted-by":"crossref","unstructured":"Z.Chen Q.Xu R.Cong andQ.Huang \u201cGlobal Context\u2010Aware Progressive Aggregation Network for Salient Object Detection \u201d inProceedings of the AAAI Conference on Artificial Intelligence vol.34(AAAI Publication 2020) 10599\u201310606.","DOI":"10.1609\/aaai.v34i07.6633"},{"key":"e_1_2_10_85_1","doi-asserted-by":"crossref","unstructured":"J.Wei S.Wang Z.Wu C.Su Q.Huang andQ.Tian \u201cLabel Decoupling Framework for Salient Object Detection \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2020) 13025\u201313034.","DOI":"10.1109\/CVPR42600.2020.01304"},{"key":"e_1_2_10_86_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1435"},{"key":"e_1_2_10_87_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3250302","article-title":"LFRNet: Localizing, Focus, and Refinement Network for Salient Object Detection of Surface Defects","volume":"72","author":"Wan B.","year":"2023","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"e_1_2_10_88_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106474"},{"key":"e_1_2_10_89_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126560"},{"key":"e_1_2_10_90_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103820"},{"key":"e_1_2_10_91_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110074"},{"key":"e_1_2_10_92_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110903"},{"issue":"3","key":"e_1_2_10_93_1","first-page":"3738","article-title":"Salient Object Detection Via Integrity Learning","volume":"45","author":"Zhuge M.","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70094","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ipr2.70094","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:36:32Z","timestamp":1778283392000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.70094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":92,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/ipr2.70094"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.70094","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"value":"1751-9659","type":"print"},{"value":"1751-9667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-11-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70094"}}