{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T12:12:08Z","timestamp":1774872728673,"version":"3.50.1"},"reference-count":85,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472149, 62302155"],"award-info":[{"award-number":["62472149, 62302155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012554","name":"Hubei Provincial Department of Education","doi-asserted-by":"publisher","award":["T2023006"],"award-info":[{"award-number":["T2023006"]}],"id":[{"id":"10.13039\/100012554","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018806","name":"Department of Science and Technology of Hubei Province","doi-asserted-by":"publisher","award":["2023BEB024"],"award-info":[{"award-number":["2023BEB024"]}],"id":[{"id":"10.13039\/501100018806","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10489-025-07060-6","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T10:27:24Z","timestamp":1767781644000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SDLK-Net: Enhanced squeezed directional large kernel multi-scale multi-modal fusion network for salient object detection"],"prefix":"10.1007","volume":"56","author":[{"given":"Lingyu","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengmao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2532-0231","authenticated-orcid":false,"given":"Zhiwei","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"issue":"10","key":"7060_CR1","doi-asserted-by":"publisher","first-page":"4555","DOI":"10.1109\/TIP.2016.2592701","volume":"25","author":"X Jia","year":"2016","unstructured":"Jia X, Lu H, Yang M-H (2016) Visual tracking via coarse and fine structural local sparse appearance models. IEEE Trans Image Process 25(10):4555\u20134564","journal-title":"IEEE Trans Image Process"},{"key":"7060_CR2","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.patrec.2018.08.010","volume":"130","author":"H Wang","year":"2020","unstructured":"Wang H, Li Z, Li Y, Gupta BB, Choi C (2020) Visual saliency guided complex image retrieval. Pattern Recogn Lett 130:64\u201372","journal-title":"Pattern Recogn Lett"},{"key":"7060_CR3","first-page":"8594","volume":"33","author":"G Li","year":"2019","unstructured":"Li G, Zhu X, Zeng Y, Wang Q, Lin L (2019) Semantic relationships guided representation learning for facial action unit recognition. Proc AAAI Conf Artif Intell 33:8594\u20138601","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7060_CR4","doi-asserted-by":"crossref","unstructured":"Qiao L, Shi Y, Li J, Wang Y, Huang T, Tian Y (2019) Transductive episodic-wise adaptive metric for few-shot learning. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 3603\u20133612","DOI":"10.1109\/ICCV.2019.00370"},{"key":"7060_CR5","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TIP.2019.2928144","volume":"29","author":"L Zhou","year":"2019","unstructured":"Zhou L, Zhang Y, Jiang Y-G, Zhang T, Fan W (2019) Re-caption: saliency-enhanced image captioning through two-phase learning. IEEE Trans Image Process 29:694\u2013709","journal-title":"IEEE Trans Image Process"},{"key":"7060_CR6","doi-asserted-by":"crossref","unstructured":"Chang K-Y, Liu T-L, Lai S-H (2011) From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model. In: CVPR 2011. IEEE pp 2129\u20132136","DOI":"10.1109\/CVPR.2011.5995415"},{"key":"7060_CR7","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neucom.2013.09.021","volume":"129","author":"C Qin","year":"2014","unstructured":"Qin C, Zhang G, Zhou Y, Tao W, Cao Z (2014) Integration of the saliency-based seed extraction and random walks for image segmentation. Neurocomputing 129:378\u2013391","journal-title":"Neurocomputing"},{"issue":"3","key":"7060_CR8","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.image.2012.11.008","volume":"28","author":"Q-G Ji","year":"2013","unstructured":"Ji Q-G, Fang Z-D, Xie Z-H, Lu Z-M (2013) Video abstraction based on the visual attention model and online clustering. Sig Process Image Commun 28(3):241\u2013253","journal-title":"Sig Process Image Commun"},{"issue":"1","key":"7060_CR9","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1186\/s13104-024-07062-6","volume":"17","author":"M Forootan","year":"2024","unstructured":"Forootan M, Rajabnia M, Mafi AR, Tehrani HA, Ghadirzadeh E, Setayeshfar M, Ghaffari Z, Tashakoripour M, Zali MR, Bolhasani H (2024) ERCPMP: an endoscopic image and video dataset for colorectal polyps morphology and pathology. BMC Res Notes 17(1):393","journal-title":"BMC Res Notes"},{"key":"7060_CR10","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s41095-019-0149-9","volume":"5","author":"A Borji","year":"2019","unstructured":"Borji A, Cheng M-M, Hou Q, Jiang H, Li J (2019) Salient object detection: a survey. Computat Visual Media 5:117\u2013150","journal-title":"Computat Visual Media"},{"issue":"12","key":"7060_CR11","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji A, Cheng M-M, Jiang H, Li J (2015) Salient object detection: a benchmark. IEEE Trans Image Process 24(12):5706\u20135722","journal-title":"IEEE Trans Image Process"},{"key":"7060_CR12","doi-asserted-by":"crossref","unstructured":"Pang Y, Zhao X, Zhang L, Lu H (2020) Multi-scale interactive network for salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 9413\u20139422","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"7060_CR13","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1109\/TMM.2023.3275308","volume":"26","author":"J Wu","year":"2023","unstructured":"Wu J, Hao F, Liang W, Xu J (2023) Transformer fusion and pixel-level contrastive learning for RGB-D salient object detection. IEEE Trans Multimed 26:1011\u20131026","journal-title":"IEEE Trans Multimed"},{"issue":"7","key":"7060_CR14","doi-asserted-by":"publisher","first-page":"4486","DOI":"10.1109\/TCSVT.2021.3127149","volume":"32","author":"Z Liu","year":"2021","unstructured":"Liu Z, Tan Y, He Q, Xiao Y (2021) SwinNet: swin transformer drives edge-aware RGB-D and RGB-T salient object detection. IEEE Trans Circuits Syst Video Technol 32(7):4486\u20134497","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"2","key":"7060_CR15","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TCSVT.2022.3202563","volume":"33","author":"B Tang","year":"2022","unstructured":"Tang B, Liu Z, Tan Y, He Q (2022) HRTransNet: HRFormer-driven two-modality salient object detection. IEEE Trans Circ Syst Video Technol 33(2):728\u2013742","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"7060_CR16","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1109\/TIP.2023.3234702","volume":"32","author":"Y Pang","year":"2023","unstructured":"Pang Y, Zhao X, Zhang L, Lu H (2023) CAVER: cross-modal view-mixed transformer for bi-modal salient object detection. IEEE Trans Image Process 32:892\u2013904","journal-title":"IEEE Trans Image Process"},{"key":"7060_CR17","doi-asserted-by":"crossref","unstructured":"Liu N, Zhang N, Wan K, Shao L, Han J (2021) Visual saliency transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4722\u20134732","DOI":"10.1109\/ICCV48922.2021.00468"},{"key":"7060_CR18","doi-asserted-by":"crossref","unstructured":"Sun F, Ren P, Yin B, Wang F, Li H (2023) CATNet: a cascaded and aggregated transformer network for RGB-D salient object detection. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2023.3294003"},{"key":"7060_CR19","doi-asserted-by":"crossref","unstructured":"Wei J, Wang S, Huang Q (2020) F$$^3$$net: fusion, feedback and focus for salient object detection. 34(07):12321\u201312328","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"7060_CR20","doi-asserted-by":"crossref","unstructured":"Chen Q, Zhang Z, Lu Y, Fu K, Zhao Q (2022) 3-D convolutional neural networks for RGB-D salient object detection and beyond, vol 35. IEEE, pp 4309\u20134323","DOI":"10.1109\/TNNLS.2022.3202241"},{"key":"7060_CR21","doi-asserted-by":"publisher","first-page":"3528","DOI":"10.1109\/TIP.2021.3062689","volume":"30","author":"G Li","year":"2021","unstructured":"Li G, Liu Z, Chen M, Bai Z, Lin W, Ling H (2021) Hierarchical alternate interaction network for RGB-D salient object detection. IEEE Trans Image Process 30:3528\u20133542","journal-title":"IEEE Trans Image Process"},{"key":"7060_CR22","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TIP.2022.3140606","volume":"31","author":"F Wang","year":"2022","unstructured":"Wang F, Pan J, Xu S, Tang J (2022) Learning discriminative cross-modality features for RGB-D saliency detection. IEEE Trans Image Process 31:1285\u20131297","journal-title":"IEEE Trans Image Process"},{"key":"7060_CR23","doi-asserted-by":"publisher","first-page":"109139","DOI":"10.1016\/j.patcog.2022.109139","volume":"135","author":"X Fang","year":"2023","unstructured":"Fang X, Jiang M, Zhu J, Shao X, Wang H (2023) M2RNet: multi-modal and multi-scale refined network for RGB-D salient object detection. Pattern Recogn 135:109139","journal-title":"Pattern Recogn"},{"key":"7060_CR24","doi-asserted-by":"crossref","unstructured":"Wang K, Tu Z, Li C, Zhang C, Luo B (2024) Learning adaptive fusion bank for multi-modal salient object detection. IEEE Transactions on Circuits and Systems for Video Technology","DOI":"10.1109\/TCSVT.2024.3375505"},{"key":"7060_CR25","doi-asserted-by":"crossref","unstructured":"Zhong M, Sun J, Ren P, Wang F, Sun F (2024) MAGNet: multi-scale awareness and global fusion network for RGB-D salient object detection. Knowledge-Based Systems, 112126","DOI":"10.1016\/j.knosys.2024.112126"},{"key":"7060_CR26","doi-asserted-by":"crossref","unstructured":"Zhang M, Ren W, Piao Y, Rong Z, Lu H (2020) Select, supplement and focus for RGB-D saliency detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3472\u20133481","DOI":"10.1109\/CVPR42600.2020.00353"},{"key":"7060_CR27","doi-asserted-by":"publisher","first-page":"126831","DOI":"10.1109\/ACCESS.2019.2936915","volume":"7","author":"B Wang","year":"2019","unstructured":"Wang B, Zhang T, Wang X, Hu H (2019) Salient object detection integrating both background and foreground information based on manifold preserving. IEEE Access 7:126831\u2013126841","journal-title":"IEEE Access"},{"key":"7060_CR28","doi-asserted-by":"crossref","unstructured":"Li Y, Hou Q, Zheng Z, Cheng M-M, Yang J, Li X (2023) Large selective kernel network for remote sensing object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 16794\u201316805","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"7060_CR29","doi-asserted-by":"crossref","unstructured":"Cai X, Lai Q, Wang Y, Wang W, Sun Z, Yao Y (2024) Poly kernel inception network for remote sensing detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 27706\u201327716","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"7060_CR30","doi-asserted-by":"crossref","unstructured":"Zhao S, Wen Z, Qi Q, Lam K-M, Shen J (2023) Learning fine-grained information with capsule-wise attention for salient object detection. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2023.3234436"},{"issue":"5","key":"7060_CR31","doi-asserted-by":"publisher","first-page":"2949","DOI":"10.1109\/TCSVT.2021.3099120","volume":"32","author":"J Wang","year":"2021","unstructured":"Wang J, Song K, Bao Y, Huang L, Yan Y (2021) CGFNet: Cross-guided fusion network for RGB-T salient object detection. IEEE Trans Circ Syst Video Technol 32(5):2949\u20132961","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"7060_CR32","doi-asserted-by":"crossref","unstructured":"Zhou T, Fu H, Chen G, Zhou Y, Fan D-P, Shao L (2021) Specificity-preserving RGB-D saliency detection. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 4681\u20134691","DOI":"10.1109\/ICCV48922.2021.00464"},{"key":"7060_CR33","doi-asserted-by":"crossref","unstructured":"Zhang W, Ji G-P, Wang Z, Fu K, Zhao Q (2021) Depth quality-inspired feature manipulation for efficient RGB-D salient object detection. In: Proceedings of the 29th ACM international conference on multimedia. pp 731\u2013740","DOI":"10.1145\/3474085.3475240"},{"issue":"15","key":"7060_CR34","doi-asserted-by":"publisher","first-page":"7825","DOI":"10.3390\/app12157825","volume":"12","author":"L Yan","year":"2022","unstructured":"Yan L, Li K, Gao R, Wang C, Xiong N (2022) An intelligent weighted object detector for feature extraction to enrich global image information. Appl Sci 12(15):7825","journal-title":"Appl Sci"},{"key":"7060_CR35","unstructured":"Vaswani A (2017) Attention is all you need. Advances in Neural Information Processing Systems"},{"issue":"11","key":"7060_CR36","doi-asserted-by":"publisher","first-page":"12760","DOI":"10.1109\/TPAMI.2022.3202765","volume":"45","author":"Y-H Wu","year":"2022","unstructured":"Wu Y-H, Liu Y, Zhan X, Cheng M-M (2022) P2T: pyramid pooling transformer for scene understanding. IEEE Trans Pattern Anal Mach Intell 45(11):12760\u201312771","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7060_CR37","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, Fan D-P, Song K, Liang D, Lu T, Luo P, Shao L (2021) Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 568\u2013578","DOI":"10.1109\/ICCV48922.2021.00061"},{"issue":"3","key":"7060_CR38","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","volume":"8","author":"W Wang","year":"2022","unstructured":"Wang W, Xie E, Li X, Fan D-P, Song K, Liang D, Lu T, Luo P, Shao L (2022) PVT v2: improved baselines with pyramid vision transformer. Comput Vis Med 8(3):415\u2013424","journal-title":"Comput Vis Med"},{"issue":"2","key":"7060_CR39","doi-asserted-by":"publisher","first-page":"023029","DOI":"10.1117\/1.JEI.33.2.023029","volume":"33","author":"L Yan","year":"2024","unstructured":"Yan L, Chen J, Tang Y (2024) TSD-CAM: transformer-based self distillation with CAM similarity for weakly supervised semantic segmentation. J Electron Imaging 33(2):023029\u2013023029","journal-title":"J Electron Imaging"},{"key":"7060_CR40","doi-asserted-by":"publisher","first-page":"103915","DOI":"10.1016\/j.cviu.2023.103915","volume":"240","author":"Q Wu","year":"2024","unstructured":"Wu Q, Zhu P, Chai Z, Guo G (2024) Joint learning of foreground, background and edge for salient object detection. Comput Vis Image Underst 240:103915","journal-title":"Comput Vis Image Underst"},{"key":"7060_CR41","first-page":"1","volume":"60","author":"C Wang","year":"2022","unstructured":"Wang C, Ning X, Sun L, Zhang L, Li W, Bai X (2022) Learning discriminative features by covering local geometric space for point cloud analysis. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"7060_CR42","first-page":"1962","volume":"34","author":"C Wang","year":"2022","unstructured":"Wang C, Wang H, Ning X, Tian S, Li W (2022) 3D point cloud classification method based on dynamic coverage of local area. J Softw 34(4):1962\u20131976","journal-title":"J Softw"},{"key":"7060_CR43","doi-asserted-by":"crossref","unstructured":"Wang C, Wu M, Lam S-K, Ning X, Yu S, Wang R, Li W, Srikanthan T (2024) GPSFormer: a global perception and local structure fitting-based transformer for point cloud understanding. In: European conference on computer vision. Springer, pp 75\u201392","DOI":"10.1007\/978-3-031-73242-3_5"},{"key":"7060_CR44","doi-asserted-by":"crossref","unstructured":"Jiang L, Wang C, Ning X, Yu Z (2023) LTTPoint: a MLP-based point cloud classification method with local topology transformation module. In: 2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, pp 783\u2013789","DOI":"10.1109\/ACAIT60137.2023.10528609"},{"key":"7060_CR45","doi-asserted-by":"crossref","unstructured":"Wang C, Cao R, Wang R (2025) Learning discriminative topological structure information representation for 2D shape and social network classification via persistent homology. Knowledge-Based Systems, 113125","DOI":"10.1016\/j.knosys.2025.113125"},{"key":"7060_CR46","doi-asserted-by":"crossref","unstructured":"Ma Y, Sun D, Meng Q, Ding Z, Li C (2017) Learning multiscale deep features and SVM regressors for adaptive RGB-T saliency detection. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), vol 1. IEEE, pp 389\u2013392","DOI":"10.1109\/ISCID.2017.92"},{"key":"7060_CR47","doi-asserted-by":"crossref","unstructured":"Tu Z, Xia T, Li C, Lu Y, Tang J (2019) M3S-NIR: multi-modal multi-scale noise-insensitive ranking for RGB-T saliency detection. In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, pp 141\u2013146","DOI":"10.1109\/MIPR.2019.00032"},{"key":"7060_CR48","doi-asserted-by":"crossref","unstructured":"Wang G, Li C, Ma Y, Zheng A, Tang J, Luo B (2018) RGB-T saliency detection benchmark: dataset, baselines, analysis and a novel approach. In: Image and graphics technologies and applications: 13th conference on image and graphics technologies and applications, IGTA 2018, Beijing, China, April 8\u201310, 2018, Revised Selected Papers 13. Springer, pp 359\u2013369","DOI":"10.1007\/978-981-13-1702-6_36"},{"issue":"12","key":"7060_CR49","doi-asserted-by":"publisher","first-page":"4421","DOI":"10.1109\/TCSVT.2019.2951621","volume":"30","author":"J Tang","year":"2019","unstructured":"Tang J, Fan D, Wang X, Tu Z, Li C (2019) RGBT salient object detection: benchmark and a novel cooperative ranking approach. IEEE Trans Circ Syst Video Technol 30(12):4421\u20134433","journal-title":"IEEE Trans Circ Syst Video Technol"},{"issue":"1","key":"7060_CR50","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/TMM.2019.2924578","volume":"22","author":"Z Tu","year":"2019","unstructured":"Tu Z, Xia T, Li C, Wang X, Ma Y, Tang J (2019) RGB-T image saliency detection via collaborative graph learning. IEEE Trans Multimed 22(1):160\u2013173","journal-title":"IEEE Trans Multimed"},{"key":"7060_CR51","doi-asserted-by":"publisher","first-page":"5678","DOI":"10.1109\/TIP.2021.3087412","volume":"30","author":"Z Tu","year":"2021","unstructured":"Tu Z, Li Z, Li C, Lang Y, Tang J (2021) Multi-interactive dual-decoder for RGB-thermal salient object detection. IEEE Trans Image Process 30:5678\u20135691","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"7060_CR52","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/TCSVT.2021.3082939","volume":"32","author":"W Gao","year":"2021","unstructured":"Gao W, Liao G, Ma S, Li G, Liang Y, Lin W (2021) Unified information fusion network for multi-modal RGB-D and RGB-T salient object detection. IEEE Trans Circ Syst Video Technol 32(4):2091\u20132106","journal-title":"IEEE Trans Circ Syst Video Technol"},{"issue":"5","key":"7060_CR53","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.1109\/TCSVT.2021.3102268","volume":"32","author":"F Huo","year":"2021","unstructured":"Huo F, Zhu X, Zhang L, Liu Q, Shu Y (2021) Efficient context-guided stacked refinement network for RGB-T salient object detection. IEEE Trans Circ Syst Video Technol 32(5):3111\u20133124","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"7060_CR54","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang J, Han Y (2023) Saliency prototype for RGB-D and RGB-T salient object detection. In: Proceedings of the 31st ACM international conference on multimedia. pp 3696\u20133705","DOI":"10.1145\/3581783.3612466"},{"key":"7060_CR55","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"issue":"3","key":"7060_CR56","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1007\/s00371-023-02887-x","volume":"40","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Wang H, Yang G, Zhang J, Gong C, Wang Y (2024) CSNet: a ConvNeXt-based Siamese network for RGB-D salient object detection. Vis Comput 40(3):1805\u20131823","journal-title":"Vis Comput"},{"key":"7060_CR57","doi-asserted-by":"crossref","unstructured":"Wu Z, Su L, Huang Q (2019) Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 3907\u20133916","DOI":"10.1109\/CVPR.2019.00403"},{"key":"7060_CR58","doi-asserted-by":"crossref","unstructured":"Ju R, Ge L, Geng W, Ren T, Wu G (2014) Depth saliency based on anisotropic center-surround difference. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE, pp 1115\u20131119","DOI":"10.1109\/ICIP.2014.7025222"},{"key":"7060_CR59","doi-asserted-by":"crossref","unstructured":"Peng H, Li B, Xiong W, Hu W, Ji R (2014) RGBD salient object detection: a benchmark and algorithms. In: Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III 13. Springer, pp 92\u2013109","DOI":"10.1007\/978-3-319-10578-9_7"},{"issue":"5","key":"7060_CR60","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1109\/TNNLS.2020.2996406","volume":"32","author":"D-P Fan","year":"2020","unstructured":"Fan D-P, Lin Z, Zhang Z, Zhu M, Cheng M-M (2020) Rethinking RGB-D salient object detection: models, data sets, and large-scale benchmarks. IEEE Tran Neural Netw Learn Syst 32(5):2075\u20132089","journal-title":"IEEE Tran Neural Netw Learn Syst"},{"key":"7060_CR61","doi-asserted-by":"crossref","unstructured":"Niu Y, Geng Y, Li X, Liu F (2012) Leveraging stereopsis for saliency analysis. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 454\u2013461","DOI":"10.1109\/CVPR.2012.6247708"},{"key":"7060_CR62","doi-asserted-by":"crossref","unstructured":"Desingh K, Krishna KM, Rajan D, Jawahar C (2013) Depth really matters: improving visual salient region detection with depth. In: BMVC. pp 1\u201311","DOI":"10.5244\/C.27.98"},{"key":"7060_CR63","doi-asserted-by":"crossref","unstructured":"Piao Y, Ji, W, Li J, Zhang M, Lu H (2019) Depth-induced multi-scale recurrent attention network for saliency detection. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 7254\u20137263","DOI":"10.1109\/ICCV.2019.00735"},{"issue":"1","key":"7060_CR64","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/TMM.2019.2924578","volume":"22","author":"Z Tu","year":"2019","unstructured":"Tu Z, Xia T, Li C, Wang X, Ma Y, Tang J (2019) RGB-T image saliency detection via collaborative graph learning. IEEE Trans Multimed 22(1):160\u2013173","journal-title":"IEEE Trans Multimed"},{"key":"7060_CR65","doi-asserted-by":"publisher","first-page":"4163","DOI":"10.1109\/TMM.2022.3171688","volume":"25","author":"Z Tu","year":"2022","unstructured":"Tu Z, Ma Y, Li Z, Li C, Xu J, Liu Y (2022) RGBT salient object detection: a large-scale dataset and benchmark. IEEE Trans Multimed 25:4163\u20134176","journal-title":"IEEE Trans Multimed"},{"key":"7060_CR66","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp 2623\u20132631","DOI":"10.1145\/3292500.3330701"},{"key":"7060_CR67","doi-asserted-by":"crossref","unstructured":"Fan D-P, Cheng M-M, Liu Y, Li T, Borji A (2017) Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE international conference on computer vision. pp 4548\u20134557","DOI":"10.1109\/ICCV.2017.487"},{"key":"7060_CR68","doi-asserted-by":"crossref","unstructured":"Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 1597\u20131604","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"7060_CR69","doi-asserted-by":"crossref","unstructured":"Fan D-P, Gong C, Cao Y, Ren B, Cheng M-M, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. arXiv:1805.10421","DOI":"10.24963\/ijcai.2018\/97"},{"key":"7060_CR70","doi-asserted-by":"crossref","unstructured":"Perazzi F, Kr\u00e4henb\u00fchl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 733\u2013740","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"7060_CR71","doi-asserted-by":"publisher","first-page":"105048","DOI":"10.1016\/j.imavis.2024.105048","volume":"147","author":"C Sun","year":"2024","unstructured":"Sun C, Zhang Q, Zhuang C, Zhang M (2024) BMFNet: bifurcated multi-modal fusion network for RGB-D salient object detection. Image Vis Comput 147:105048","journal-title":"Image Vis Comput"},{"key":"7060_CR72","doi-asserted-by":"publisher","first-page":"109516","DOI":"10.1016\/j.patcog.2023.109516","volume":"140","author":"Y Wang","year":"2023","unstructured":"Wang Y, Jia X, Zhang L, Li Y, Elder JH, Lu H (2023) A uniform transformer-based structure for feature fusion and enhancement for RGB-D saliency detection. Pattern Recogn 140:109516","journal-title":"Pattern Recogn"},{"key":"7060_CR73","doi-asserted-by":"publisher","first-page":"109194","DOI":"10.1016\/j.patcog.2022.109194","volume":"136","author":"H Bi","year":"2023","unstructured":"Bi H, Wu R, Liu Z, Zhu H, Zhang C, Xiang T-Z (2023) Cross-modal hierarchical interaction network for RGB-D salient object detection. Pattern Recogn 136:109194","journal-title":"Pattern Recogn"},{"key":"7060_CR74","doi-asserted-by":"publisher","first-page":"2160","DOI":"10.1109\/TIP.2023.3263111","volume":"32","author":"Z Wu","year":"2023","unstructured":"Wu Z, Allibert G, Meriaudeau F, Ma C, Demonceaux C (2023) HiDAnet: RGB-D salient object detection via hierarchical depth awareness. IEEE Trans Image Process 32:2160\u20132173","journal-title":"IEEE Trans Image Process"},{"key":"7060_CR75","doi-asserted-by":"crossref","unstructured":"Wu Z, Gobichettipalayam S, Tamadazte B, Allibert G, Paudel DP, Demonceaux C (2022) Robust RGB-D fusion for saliency detection. In: 2022 international conference on 3D vision (3DV). IEEE, pp 403\u2013413","DOI":"10.1109\/3DV57658.2022.00052"},{"key":"7060_CR76","doi-asserted-by":"publisher","first-page":"5142","DOI":"10.1109\/TMM.2022.3187856","volume":"25","author":"M Zhang","year":"2022","unstructured":"Zhang M, Yao S, Hu B, Piao Y, Ji W (2022) DFNet: criss-cross dynamic filter network for RGB-D salient object detection. IEEE Trans Multimed 25:5142\u20135154","journal-title":"IEEE Trans Multimed"},{"key":"7060_CR77","doi-asserted-by":"crossref","unstructured":"Zhao X, Pang Y, Zhang L, Lu H, Ruan X (2022) Self-supervised pretraining for RGB-D salient object detection. In: Proceedings of the AAAI conference on artificial intelligence, vol 36. pp 3463\u20133471","DOI":"10.1609\/aaai.v36i3.20257"},{"key":"7060_CR78","doi-asserted-by":"crossref","unstructured":"Lee M, Park C, Cho S, Lee S (2022) SPSN: superpixel prototype sampling network for RGB-D salient object detection. In: European conference on computer vision. Springer, pp 630\u2013647","DOI":"10.1007\/978-3-031-19818-2_36"},{"key":"7060_CR79","doi-asserted-by":"crossref","unstructured":"Sun P, Zhang W, Wang H, Li S, Li X (2021) Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 1407\u20131417","DOI":"10.1109\/CVPR46437.2021.00146"},{"key":"7060_CR80","doi-asserted-by":"crossref","unstructured":"Wang K, Lin D, Li C, Tu Z, Luo B (2024) Alignment-free RGBT salient object detection: semantics-guided asymmetric correlation network and a unified benchmark. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2024.3410542"},{"issue":"7","key":"7060_CR81","doi-asserted-by":"publisher","first-page":"3104","DOI":"10.1109\/TCSVT.2022.3233131","volume":"33","author":"K Song","year":"2022","unstructured":"Song K, Huang L, Gong A, Yan Y (2022) Multiple graph affinity interactive network and a variable illumination dataset for RGBT image salient object detection. IEEE Trans Circ Syst Video Technol 33(7):3104\u20133118","journal-title":"IEEE Trans Circ Syst Video Technol"},{"issue":"8","key":"7060_CR82","doi-asserted-by":"publisher","first-page":"4149","DOI":"10.1109\/TCSVT.2023.3241196","volume":"33","author":"Z Xie","year":"2023","unstructured":"Xie Z, Shao F, Chen G, Chen H, Jiang Q, Meng X, Ho Y-S (2023) Cross-modality double bidirectional interaction and fusion network for RGB-T salient object detection. IEEE Trans Circ Syst Video Technol 33(8):4149\u20134163","journal-title":"IEEE Trans Circ Syst Video Technol"},{"issue":"11","key":"7060_CR83","doi-asserted-by":"publisher","first-page":"7646","DOI":"10.1109\/TCSVT.2022.3184840","volume":"32","author":"G Liao","year":"2022","unstructured":"Liao G, Gao W, Li G, Wang J, Kwong S (2022) Cross-collaborative fusion-encoder network for robust RGB-thermal salient object detection. IEEE Trans Circ Syst Video Technol 32(11):7646\u20137661","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"7060_CR84","doi-asserted-by":"publisher","first-page":"6971","DOI":"10.1109\/TMM.2022.3216476","volume":"25","author":"R Cong","year":"2022","unstructured":"Cong R, Zhang K, Zhang C, Zheng F, Zhao Y, Huang Q, Kwong S (2022) Does thermal really always matter for RGB-T salient object detection? IEEE Trans Multimed 25:6971\u20136982","journal-title":"IEEE Trans Multimed"},{"issue":"21","key":"7060_CR85","doi-asserted-by":"publisher","first-page":"25543","DOI":"10.1007\/s10489-023-04784-1","volume":"53","author":"H Bi","year":"2023","unstructured":"Bi H, Zhang J, Wu R, Tong Y, Fu X, Shao K (2023) RGB-T salient object detection via excavating and enhancing CNN features. Appl Intell 53(21):25543\u201325561","journal-title":"Appl Intell"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07060-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-07060-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07060-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:03:37Z","timestamp":1774868617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-07060-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":85,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["7060"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-07060-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"1 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study adheres to ethical guidelines and standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors confirm that there are no conflicts of interest regarding the publication of this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"31"}}