{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T19:30:40Z","timestamp":1775935840408,"version":"3.50.1"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Heilongjiang Province Natural Science Foundation","doi-asserted-by":"crossref","award":["LH2022F005"],"award-info":[{"award-number":["LH2022F005"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hainan Province Science and Technology Special Fund","award":["ZDYF2022SHFZ047"],"award-info":[{"award-number":["ZDYF2022SHFZ047"]}]},{"name":"Infrared and Low Temperature Plasma Key Laboratory of Anhui Province","award":["IRKL2022KF07"],"award-info":[{"award-number":["IRKL2022KF07"]}]},{"name":"Foundation of State Key Laboratory of Public Big Data","award":["PBD2022-15"],"award-info":[{"award-number":["PBD2022-15"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s10489-023-04784-1","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T15:06:25Z","timestamp":1691593585000},"page":"25543-25561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["RGB-T salient object detection via excavating and enhancing CNN features"],"prefix":"10.1007","volume":"53","author":[{"given":"Hongbo","family":"Bi","sequence":"first","affiliation":[]},{"given":"Jiayuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ranwan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yuyu","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Keyong","family":"Shao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"4784_CR1","doi-asserted-by":"crossref","unstructured":"Cheng Z, Sun H, Takeuchi M, Katto J (2020) Learned image compression with discretized gaussian mixture likelihoods and attention modules. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7939\u20137948","DOI":"10.1109\/CVPR42600.2020.00796"},{"key":"4784_CR2","doi-asserted-by":"crossref","unstructured":"Oh SW, Lee J-Y, Xu N, Kim SJ (2019) Video object segmentation using space-time memory networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 9226\u20139235","DOI":"10.1109\/ICCV.2019.00932"},{"key":"4784_CR3","doi-asserted-by":"crossref","unstructured":"Zhu J, Shen Y, Zhao D, Zhou B (2020) In-domain gan inversion for real image editing. In: European Conference on Computer Vision, pp 592\u2013608 . Springer","DOI":"10.1007\/978-3-030-58520-4_35"},{"key":"4784_CR4","doi-asserted-by":"crossref","unstructured":"Danelljan M, Gool LV, Timofte R (2020) Probabilistic regression for visual tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7183\u20137192","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"4784_CR5","doi-asserted-by":"crossref","unstructured":"Zhao J-X, Liu J-J, Fan D-P, Cao Y, Yang J, Cheng M-M (2019) Egnet: Edge guidance network for salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 8779\u20138788","DOI":"10.1109\/ICCV.2019.00887"},{"key":"4784_CR6","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":"4784_CR7","doi-asserted-by":"publisher","first-page":"12321","DOI":"10.1609\/aaai.v34i07.6916","volume":"34","author":"J Wei","year":"2020","unstructured":"Wei J, Wang S, Huang Q (2020) F3net: fusion, feedback and focus for salient object detection. Proceedings of the AAAI Conference on Artificial Intelligence 34:12321\u201312328","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4784_CR8","doi-asserted-by":"crossref","unstructured":"Ji W, Li J, Yu S, Zhang M, Piao Y, Yao S, Bi Q, Ma K, Zheng Y, Lu H, et\u00a0al. (2021) Calibrated rgb-d salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9471\u20139481","DOI":"10.1109\/CVPR46437.2021.00935"},{"issue":"5","key":"4784_CR9","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 Trans Neural Netw Learning Syst 32(5):2075\u20132089","journal-title":"IEEE Trans Neural Netw Learning Syst"},{"key":"4784_CR10","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1609\/aaai.v35i2.16191","volume":"35","author":"Q Chen","year":"2021","unstructured":"Chen Q, Liu Z, Zhang Y, Fu K, Zhao Q, Du H (2021) Rgb-d salient object detection via 3d convolutional neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35:1063\u20131071","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"3","key":"4784_CR11","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1109\/TCSVT.2021.3077058","volume":"32","author":"W Zhou","year":"2021","unstructured":"Zhou W, Guo Q, Lei J, Yu L, Hwang J-N (2021) Ecffnet: Effective and consistent feature fusion network for rgb-t salient object detection. IEEE Trans Circ Syst Video Technol 32(3):1224\u20131235","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"4784_CR12","doi-asserted-by":"crossref","unstructured":"Zhou W, Zhu Y, Lei J, Wan J, Yu L (2021) Apnet: Adversarial learning assistance and perceived importance fusion network for all-day rgbt salient object detection. IEEE Transactions on Emerging Topics in Computational Intelligence","DOI":"10.1109\/TETCI.2021.3118043"},{"issue":"4","key":"4784_CR13","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":"4","key":"4784_CR14","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1109\/TCSVT.2018.2823769","volume":"29","author":"Y Liu","year":"2018","unstructured":"Liu Y, Han J, Zhang Q, Wang L (2018) Salient object detection via twostage graphs. IEEE Trans Circ Syst Video Technol 29(4):1023\u20131037","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"4784_CR15","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.patcog.2016.10.025","volume":"64","author":"J Zhang","year":"2017","unstructured":"Zhang J, Ehinger KA, Wei H, Zhang K, Yang J (2017) A novel graph-based optimization framework for salient object detection. Pattern Recogn 64:39\u201350","journal-title":"Pattern Recogn"},{"key":"4784_CR16","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neucom.2019.02.041","volume":"340","author":"L Zhang","year":"2019","unstructured":"Zhang L, Zhang D, Sun J, Wei G, Bo H (2019) Salient object detection by local and global manifold regularized svm model. Neurocomputing 340:42\u201354","journal-title":"Neurocomputing"},{"key":"4784_CR17","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), IEEE vol 1, pp 389\u2013392","DOI":"10.1109\/ISCID.2017.92"},{"issue":"5","key":"4784_CR18","doi-asserted-by":"publisher","first-page":"2050","DOI":"10.1109\/TCYB.2018.2879859","volume":"50","author":"S Chen","year":"2018","unstructured":"Chen S, Wang B, Tan X, Hu X (2018) Embedding attention and residual network for accurate salient object detection. IEEE Trans Cybern 50(5):2050\u20132062","journal-title":"IEEE Trans Cybern"},{"key":"4784_CR19","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"},{"key":"4784_CR20","doi-asserted-by":"publisher","first-page":"3321","DOI":"10.1109\/TIP.2019.2959253","volume":"29","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Huang N, Yao L, Zhang D, Shan C, Han J (2019) Rgb-t salient object detection via fusing multi-level cnn features. IEEE Trans Image Process 29:3321\u20133335","journal-title":"IEEE Trans Image Process"},{"key":"4784_CR21","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/LSP.2021.3102524","volume":"28","author":"Q Guo","year":"2021","unstructured":"Guo Q, Zhou W, Lei J, Yu L (2021) Tsfnet: Two-stage fusion network for rgb-t salient object detection. IEEE Signal Process Lett 28:1655\u20131659","journal-title":"IEEE Signal Process Lett"},{"issue":"5","key":"4784_CR22","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":"4784_CR23","doi-asserted-by":"crossref","unstructured":"Chen S, Tan X, Wang B, Hu X (2018) Reverse attention for salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 234\u2013250","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"4784_CR24","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":"4784_CR25","doi-asserted-by":"crossref","unstructured":"Song H, Wang W, Zhao S, Shen J, Lam K-M (2018) Pyramid dilated deeper convlstm for video salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 715\u2013731","DOI":"10.1007\/978-3-030-01252-6_44"},{"issue":"3","key":"4784_CR26","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","volume":"37","author":"M-M Cheng","year":"2014","unstructured":"Cheng M-M, Mitra NJ, Huang X, Torr PH, Hu S-M (2014) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Int 37(3):569\u2013582","journal-title":"IEEE Trans Pattern Anal Mach Int"},{"key":"4784_CR27","doi-asserted-by":"crossref","unstructured":"Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2814\u20132821","DOI":"10.1109\/CVPR.2014.360"},{"key":"4784_CR28","doi-asserted-by":"crossref","unstructured":"Bi S, Li G, Yu Y (2014) Person re-identification using multiple experts with random subspaces. J Image Graph 2(2):151\u2013157","DOI":"10.12720\/joig.2.2.151-157"},{"issue":"4","key":"4784_CR29","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1109\/TPAMI.2016.2562626","volume":"39","author":"H Peng","year":"2016","unstructured":"Peng H, Li B, Ling H, Hu W, Xiong W, Maybank SJ (2016) Salient object detection via structured matrix decomposition. IEEE Trans Pattern Anal Mach Int 39(4):818\u2013832","journal-title":"IEEE Trans Pattern Anal Mach Int"},{"issue":"1","key":"4784_CR30","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 Multimedia 22(1):160\u2013173","journal-title":"IEEE Trans Multimedia"},{"issue":"12","key":"4784_CR31","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 Sys Video Technol 30(12):4421\u20134433","journal-title":"IEEE Trans Circ Sys Video Technol"},{"issue":"5","key":"4784_CR32","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1109\/TCSVT.2020.3014663","volume":"31","author":"Q Zhang","year":"2020","unstructured":"Zhang Q, Xiao T, Huang N, Zhang D, Han J (2020) Revisiting feature fusion for rgb-t salient object detection. IEEE Trans Circ Syst Video Technol 31(5):1804\u20131818","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"4784_CR33","doi-asserted-by":"crossref","unstructured":"Jiang Z, Davis LS (2013) Submodular salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2043\u20132050","DOI":"10.1109\/CVPR.2013.266"},{"key":"4784_CR34","doi-asserted-by":"crossref","unstructured":"Qin X, Zhang Z, Huang C, Gao C, Dehghan M, Jagersand M (2019) Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7479\u20137489","DOI":"10.1109\/CVPR.2019.00766"},{"key":"4784_CR35","doi-asserted-by":"crossref","unstructured":"Hou Q, Cheng M-M, Hu X, Borji A, Tu Z, Torr PH (2017) Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3203\u20133212","DOI":"10.1109\/CVPR.2017.563"},{"issue":"8","key":"4784_CR36","doi-asserted-by":"publisher","first-page":"3919","DOI":"10.1109\/TIP.2016.2579306","volume":"25","author":"X Li","year":"2016","unstructured":"Li X, Zhao L, Wei L, Yang M-H, Wu F, Zhuang Y, Ling H, Wang J (2016) Deepsaliency: Multi-task deep neural network model for salient object detection. IEEE Trans Image Process 25(8):3919\u20133930","journal-title":"IEEE Trans Image Process"},{"key":"4784_CR37","doi-asserted-by":"crossref","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 Multimedia","DOI":"10.1109\/TMM.2022.3171688"},{"key":"4784_CR38","doi-asserted-by":"crossref","unstructured":"Liu J-J, Hou Q, Cheng M-M, Feng J, Jiang J (2019) A simple poolingbased design for real-time salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3917\u20133926","DOI":"10.1109\/CVPR.2019.00404"},{"key":"4784_CR39","doi-asserted-by":"crossref","unstructured":"Zhao T, Wu X (2019) Pyramid feature attention network for saliency detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3085\u20133094","DOI":"10.1109\/CVPR.2019.00320"},{"key":"4784_CR40","doi-asserted-by":"crossref","unstructured":"Feng D, Barnes N, You S, McCarthy C (2016) Local background enclosure for rgb-d salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2343\u20132350","DOI":"10.1109\/CVPR.2016.257"},{"issue":"6","key":"4784_CR41","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/TIP.2019.2891104","volume":"28","author":"H Chen","year":"2019","unstructured":"Chen H, Li Y (2019) Three-stream attention-aware network for rgb-d salient object detection. IEEE Trans Image Process 28(6):2825\u20132835","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"4784_CR42","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1109\/LSP.2016.2557347","volume":"23","author":"R Cong","year":"2016","unstructured":"Cong R, Lei J, Zhang C, Huang Q, Cao X, Hou C (2016) Saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion. IEEE Signal Process Lett 23(6):819\u2013823","journal-title":"IEEE Signal Process Lett"},{"issue":"1","key":"4784_CR43","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/TCYB.2020.2969255","volume":"51","author":"C Li","year":"2020","unstructured":"Li C, Cong R, Kwong S, Hou J, Fu H, Zhu G, Zhang D, Huang Q (2020) Asif-net: Attention steered interweave fusion network for rgb-d salient object detection. IEEE Trans Cybern 51(1):88\u2013100","journal-title":"IEEE Trans Cybern"},{"key":"4784_CR44","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":"4784_CR45","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.neucom.2022.03.029","volume":"490","author":"Y Liang","year":"2022","unstructured":"Liang Y, Qin G, Sun M, Qin J, Yan J, Zhang Z (2022) Multi-modal interactive attention and dual progressive decoding network for rgb-d\/t salient object detection. Neurocomputing 490:132\u2013145","journal-title":"Neurocomputing"},{"key":"4784_CR46","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 8-10, 2018, Revised Selected Papers 13, pp 359\u2013369. Springer","DOI":"10.1007\/978-981-13-1702-6_36"},{"key":"4784_CR47","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"10","key":"4784_CR48","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.3390\/math10101750","volume":"10","author":"T Liu","year":"2022","unstructured":"Liu T, Luo R, Xu L, Feng D, Cao L, Liu S, Guo J (2022) Spatial channel attention for deep convolutional neural networks. Mathematics 10(10):1750","journal-title":"Mathematics"},{"key":"4784_CR49","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4784_CR50","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"4784_CR51","doi-asserted-by":"crossref","unstructured":"Liu N, Zhang N, Han J (2020) Learning selective self-mutual attention for rgb-d saliency detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 13756\u201313765","DOI":"10.1109\/CVPR42600.2020.01377"},{"key":"4784_CR52","doi-asserted-by":"crossref","unstructured":"Lan G, Xiao S, Wen J, Chen D, Zhu Y (2022) Data-driven deepfake forensics model based on large-scale frequency and noise features. IEEE Int Syst","DOI":"10.1109\/MIS.2022.3217391"},{"issue":"4","key":"4784_CR53","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Int 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Int"},{"key":"4784_CR54","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"4784_CR55","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"4784_CR56","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D, et\u00a0al (2018) Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 385\u2013400","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"4784_CR57","doi-asserted-by":"crossref","unstructured":"Xiao S, Lan G, Yang J, Li Y, Wen J (2022) Securing the socio-cyber world: Multiorder attribute node association classification for manipulated media. IEEE Trans Comput Soc Syst 1\u201310","DOI":"10.1109\/TCSS.2022.3213832"},{"issue":"13","key":"4784_CR58","doi-asserted-by":"publisher","first-page":"4697","DOI":"10.3390\/s22134697","volume":"22","author":"J Yang","year":"2022","unstructured":"Yang J, Lan G, Xiao S, Li Y, Wen J, Zhu Y (2022) Enriching facial anti-spoofing datasets via an effective face swapping framework. Sensors 22(13):4697","journal-title":"Sensors"},{"issue":"2","key":"4784_CR59","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2021","unstructured":"Gao S-H, Cheng M-M, Zhao K, Zhang X-Y, Yang M-H, Torr P (2021) Res2net: A new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Int 43(2):652\u2013662","journal-title":"IEEE Trans Pattern Anal Mach Int"},{"key":"4784_CR60","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"4784_CR61","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":"4784_CR62","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 preprint arXiv:1805.10421","DOI":"10.24963\/ijcai.2018\/97"},{"key":"4784_CR63","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":"4784_CR64","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":"4784_CR65","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang Z, Zheng Y, Chuang Y-Y Satoh S (2019) Learning to reduce dual-level discrepancy for infrared-visible person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 618\u2013626","DOI":"10.1109\/CVPR.2019.00071"},{"key":"4784_CR66","doi-asserted-by":"crossref","unstructured":"Deng Z, Hu X, Zhu L, Xu X, Qin J, Han G, Heng P-A (2018) R3net: Recurrent residual refinement network for saliency detection. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp 684\u2013690 . AAAI Press Menlo Park, CA, USA","DOI":"10.24963\/ijcai.2018\/95"},{"key":"4784_CR67","doi-asserted-by":"crossref","unstructured":"Wang G, Zhang T, Cheng J, Liu S, Yang Y, Hou Z (2019) Rgb-infrared cross-modality person re-identification via joint pixel and feature alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 3623\u20133632","DOI":"10.1109\/ICCV.2019.00372"},{"key":"4784_CR68","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.patcog.2018.08.007","volume":"86","author":"H Chen","year":"2019","unstructured":"Chen H, Li Y, Su D (2019) Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for rgb-d salient object detection. Pattern Recogn 86:376\u2013385","journal-title":"Pattern Recogn"},{"key":"4784_CR69","doi-asserted-by":"publisher","first-page":"55277","DOI":"10.1109\/ACCESS.2019.2913107","volume":"7","author":"N Wang","year":"2019","unstructured":"Wang N, Gong X (2019) Adaptive fusion for rgb-d salient object detection. IEEE Access 7:55277\u201355284","journal-title":"IEEE Access"},{"issue":"5","key":"4784_CR70","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":"4784_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3185323","volume":"71","author":"F Huo","year":"2022","unstructured":"Huo F, Zhu X, Zhang Q, Liu Z, Yu W (2022) Real-time one-stream semantic-guided refinement network for rgb-thermal salient object detection. IEEE Trans Instrum Meas 71:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"4784_CR72","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.neucom.2022.09.052","volume":"511","author":"H Bi","year":"2022","unstructured":"Bi H, Wu R, Liu Z, Zhang J, Zhang C, Xiang T-Z, Wang X (2022) Psnet: Parallel symmetric network for rgb-t salient object detection. Neurocomput 511:410\u2013425","journal-title":"Neurocomput"},{"key":"4784_CR73","doi-asserted-by":"crossref","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","DOI":"10.1109\/TCSVT.2023.3241196"},{"key":"4784_CR74","doi-asserted-by":"crossref","unstructured":"Zhou W, Zhu Y, Lei J, Yang R, Yu L (2023) Lsnet: Lightweight spatial boosting network for detecting salient objects in rgb-thermal images. IEEE Trans Image Process","DOI":"10.1109\/TIP.2023.3242775"},{"issue":"7","key":"4784_CR75","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 edgeaware rgb-d and rgb-t salient object detection. IEEE Trans Circ Syst Video Technol 32(7):4486\u20134497","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"4784_CR76","doi-asserted-by":"crossref","unstructured":"Tang B, Liu Z, Tan Y, He Q (2022) Hrtransnet: Hrformer-driven twomodality salient object detection. IEEE Trans Circ Syst Video Technol","DOI":"10.1109\/TCSVT.2022.3202563"},{"key":"4784_CR77","doi-asserted-by":"crossref","unstructured":"Ding X, Guo Y, Ding G, Han J (2019) Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 1911\u20131920","DOI":"10.1109\/ICCV.2019.00200"},{"key":"4784_CR78","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-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III 13, pp 92\u2013109 . Springer","DOI":"10.1007\/978-3-319-10578-9_7"},{"key":"4784_CR79","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":"4784_CR80","doi-asserted-by":"publisher","first-page":"3376","DOI":"10.1109\/TIP.2021.3060167","volume":"30","author":"W-D Jin","year":"2021","unstructured":"Jin W-D, Xu J, Han Q, Zhang Y, Cheng M-M (2021) Cdnet: Complementary depth network for rgb-d salient object detection. IEEE Trans Image Process 30:3376\u20133390","journal-title":"IEEE Trans Image Process"},{"key":"4784_CR81","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.neucom.2021.04.053","volume":"452","author":"Z Huang","year":"2021","unstructured":"Huang Z, Chen H-X, Zhou T, Yang Y-Z, Liu B-Y (2021) Multilevel cross-modal interaction network for rgb-d salient object detection. Neurocomput 452:200\u2013211","journal-title":"Neurocomput"},{"key":"4784_CR82","doi-asserted-by":"crossref","unstructured":"Jin X, Guo C, He Z, Xu J, Wang Y, Su Y (2022) Fcmnet: Frequencyaware cross-modality attention networks for rgb-d salient object detection. Neurocomput 491:414-425","DOI":"10.1016\/j.neucom.2022.04.015"},{"key":"4784_CR83","unstructured":"Hu M, Zhang X, Zhao L (2022) Multi-scale residual interaction for rgbd salient object detection. In: Proceedings of the Asian Conference on Computer Vision, pp 2494\u20132509"},{"key":"4784_CR84","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":"4784_CR85","doi-asserted-by":"publisher","first-page":"116591","DOI":"10.1016\/j.image.2021.116591","volume":"102","author":"X Zhou","year":"2022","unstructured":"Zhou X, Wen H, Shi R, Yin H, Zhang J, Yan C (2022) Fanet: Feature aggregation network for rgbd saliency detection. Signal Process Image Commun 102:116591","journal-title":"Signal Process Image Commun"},{"key":"4784_CR86","doi-asserted-by":"crossref","unstructured":"Bi H, Zhang J, Wu R, Tong Y, Jin W (2023) Cross-modal\u00a0refined adjacentguided network for rgb-d salient object detection. Multimedia Tools Object Detection Appl 1\u201326","DOI":"10.1016\/j.patcog.2022.109194"},{"key":"4784_CR87","doi-asserted-by":"crossref","unstructured":"Ling L, Wang Y , Wang C, Xu S, Huang Y (2023) Depth-aware lightweight network for rgb-d salient object detection. IET Image Processing","DOI":"10.1049\/ipr2.12796"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04784-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04784-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04784-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T14:21:54Z","timestamp":1698070914000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04784-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,9]]},"references-count":87,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["4784"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04784-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,9]]},"assertion":[{"value":"11 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no competing interests regarding the content of this paper","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}