{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T05:13:22Z","timestamp":1744953202816,"version":"3.37.3"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172355)"],"award-info":[{"award-number":["62172355)"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10489-022-03612-2","type":"journal-article","created":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T03:28:47Z","timestamp":1659151727000},"page":"7957-7969","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["RGB-D saliency detection via complementary and selective learning"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6573-7439","authenticated-orcid":false,"given":"Wenwen","family":"Pan","sequence":"first","affiliation":[]},{"given":"Xiaofei","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yunsheng","family":"Qian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"issue":"1","key":"3612_CR1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TPAMI.2012.89","volume":"35","author":"A Borji","year":"2013","unstructured":"Borji A, Itti L (2013) State-of-the-art in visual attention modeling. IEEE Trans Pattern Anal Intell 35(1):185\u2013207. https:\/\/doi.org\/10.1109\/TPAMI.2012.89","journal-title":"IEEE Trans Pattern Anal Intell"},{"issue":"4","key":"3612_CR2","doi-asserted-by":"publisher","first-page":"24","DOI":"10.5594\/j18173","volume":"121","author":"MS Banks","year":"2012","unstructured":"Banks M S, Read J C A, Allison R S, Watt S J (2012) Stereoscopy and the human visual system. SMPTE Mot Imaging J 121(4):24\u201343. https:\/\/doi.org\/10.5594\/j18173","journal-title":"SMPTE Mot Imaging J"},{"key":"3612_CR3","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 B B, Choi C (2020) Visual saliency guided complex image retrieval. Pattern Recogn Lett 130:64\u201372. https:\/\/doi.org\/10.1016\/j.patrec.2018.08.010","journal-title":"Pattern Recogn Lett"},{"issue":"9","key":"3612_CR4","doi-asserted-by":"publisher","first-page":"4580","DOI":"10.1109\/TIP.2019.2913513","volume":"28","author":"S Wei","year":"2019","unstructured":"Wei S, Liao L, Li J, Zheng Q, Yang F, Zhao Y (2019) Saliency inside: learning attentive CNNs for content-based image retrieva. IEEE Trans Image Process 28(9):4580\u20134593. https:\/\/doi.org\/10.1109\/TIP.2019.2913513https:\/\/doi.org\/10.1109\/TIP.2019.2913513","journal-title":"IEEE Trans Image Process"},{"key":"3612_CR5","doi-asserted-by":"crossref","unstructured":"Yang S, Lin W, Jiang Q, Wang Y (2019) SGDNEt: An end-to-end saliency-guided deep neural network for no-reference image quality assessment. MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, 1383\u20131391","DOI":"10.1145\/3343031.3350990"},{"issue":"12","key":"3612_CR6","doi-asserted-by":"publisher","first-page":"14859","DOI":"10.1007\/s11042-017-5070-6","volume":"77","author":"S Jia","year":"2018","unstructured":"Jia S, Zhang Y (2018) Saliency-based deep convolutional neural network for no-reference image quality assessment. Multimed Tools Appl 77(12):14859\u201314872. https:\/\/doi.org\/10.1007\/s11042-017-5070-6https:\/\/doi.org\/10.1007\/s11042-017-5070-6","journal-title":"Multimed Tools Appl"},{"issue":"Wenhui Li","key":"3612_CR7","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.patrec.2019.01.009","volume":"120","author":"F Sun","year":"2019","unstructured":"Sun F, Li W (2019) Saliency guided deep network for weakly-supervised image segmentation. Pattern Recogn Lett 120(Wenhui Li):62\u201368. https:\/\/doi.org\/10.1016\/j.patrec.2019.01.009","journal-title":"Pattern Recogn Lett"},{"key":"3612_CR8","doi-asserted-by":"publisher","unstructured":"Zhou Y, Wang X, Jiao J, Darrell T, Yu F (2020) Learning saliency propagation for semi-supervised instance segmentation. In: Proceedings of the IEEE computer society conference on computer vision and Pattern Recognition, pp 10304\u201310313, DOI https:\/\/doi.org\/10.1109\/CVPR42600.2020.01032, (to appear in print)","DOI":"10.1109\/CVPR42600.2020.01032"},{"issue":"9","key":"3612_CR9","doi-asserted-by":"publisher","first-page":"2885","DOI":"10.1016\/j.patcog.2015.01.025","volume":"48","author":"C Chen","year":"2015","unstructured":"Chen C, Li S, Qin H, Hao A (2015) Real-time and robust object tracking in video via low-rank coherency analysis in feature space. Pattern Recogn 48(9):2885\u20132905. https:\/\/doi.org\/10.1016\/j.patcog.2015.01.025https:\/\/doi.org\/10.1016\/j.patcog.2015.01.025","journal-title":"Pattern Recogn"},{"key":"3612_CR10","doi-asserted-by":"crossref","unstructured":"Babichev S A, Ries J, Lvovsky A I (2002) Quantum scissors: teleportation of single-mode optical states by means of a nonlocal single photon Preprint at arXiv:quant-ph\/0208066v1","DOI":"10.1209\/epl\/i2003-00504-y"},{"key":"3612_CR11","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/S0370-2693(96)01648-6","volume":"B393","author":"M Beneke","year":"1997","unstructured":"Beneke M, Buchalla G, Dunietz I (1997) Mixing induced CP asymmetries in inclusive B decays. Phys Lett B393:132\u2013142. arXiv:https:\/\/arxiv.org\/abs\/0707.3168 [gr-gc]","journal-title":"Phys Lett"},{"issue":"1","key":"3612_CR12","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TMM.2012.2225034","volume":"15","author":"N Imamoglu","year":"2013","unstructured":"Imamoglu N, Lin W, Fang Y (2013) A saliency detection model using low-level features based on wavelet transform. IEEE Trans Multimed 15(1):96\u2013105. https:\/\/doi.org\/10.1109\/TMM.2012.2225034https:\/\/doi.org\/10.1109\/TMM.2012.2225034","journal-title":"IEEE Trans Multimed"},{"issue":"3","key":"3612_CR13","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","volume":"37","author":"MM Cheng","year":"2015","unstructured":"Cheng M M, Mitra N J, Huang X, Torr P H S, Hu S M (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569\u2013582. https:\/\/doi.org\/10.1109\/TPAMI.2014.2345401https:\/\/doi.org\/10.1109\/TPAMI.2014.2345401","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"3612_CR14","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1109\/TPAMI.2016.2547384","volume":"39","author":"J Yang","year":"2017","unstructured":"Yang J, Yang M H (2017) Top-down visual saliency via joint CRF and dictionary learning. IEEE Trans Pattern Anal Mach Intell 39(3):576\u2013588. https:\/\/doi.org\/10.1109\/TPAMI.2016.2547384https:\/\/doi.org\/10.1109\/TPAMI.2016.2547384","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3612_CR15","doi-asserted-by":"publisher","unstructured":"He S, Lau R W H (2016) Exemplar-driven top-down saliency detection via deep association. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2016-Decem. https:\/\/doi.org\/10.1109\/CVPR.2016.617https:\/\/doi.org\/10.1109\/CVPR.2016.617, pp 5723\u20135732","DOI":"10.1109\/CVPR.2016.617 10.1109\/CVPR.2016.617"},{"key":"3612_CR16","doi-asserted-by":"publisher","unstructured":"Deng Z, Hu X, Zhu L, Xu X, Qin J, Han B, Heng P-A (2018) r3net: recurrent residual refinement network for saliency detection. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence (IJCAI-18), pp 684\u2013690, DOI https:\/\/doi.org\/10.24963\/ijcai.2018\/95, (to appear in print)","DOI":"10.24963\/ijcai.2018\/95"},{"key":"3612_CR17","doi-asserted-by":"publisher","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 https:\/\/doi.org\/10.48550\/arXiv.1903.00179, (to appear in print)","DOI":"10.48550\/arXiv.1903.00179"},{"key":"3612_CR18","doi-asserted-by":"publisher","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 international conference on computer vision, vol 2019-Octob, pp 7254\u20137263, DOI https:\/\/doi.org\/10.1109\/ICCV.2019.00735, (to appear in print)","DOI":"10.1109\/ICCV.2019.00735"},{"issue":"8","key":"3612_CR19","doi-asserted-by":"publisher","first-page":"5775","DOI":"10.1007\/s10489-020-02150-z","volume":"51","author":"Z Tan","year":"2021","unstructured":"Tan Z, Gu X (2021) Depth scale balance saliency detection with connective feature pyramid and edge guidance. Appl Intell 51(8):5775\u20135792. https:\/\/doi.org\/10.1007\/s10489-020-02150-z","journal-title":"Appl Intell"},{"key":"3612_CR20","doi-asserted-by":"crossref","unstructured":"Wang J, Zhao Z, Yang S, Chai X, Zhang W, Zhang M (2021) Global contextual guided residual attention network for salient object detection. Applied Intelligence","DOI":"10.1007\/s10489-021-02713-8"},{"issue":"10","key":"3612_CR21","doi-asserted-by":"publisher","first-page":"6881","DOI":"10.1007\/s10489-020-02147-8","volume":"51","author":"J Jiao","year":"2021","unstructured":"Jiao J, Xue H, Ding J (2021) Non-local duplicate pooling network for salient object detection. Appl Intell 51(10):6881\u20136894. https:\/\/doi.org\/10.1007\/s10489-020-02147-8","journal-title":"Appl Intell"},{"issue":"6","key":"3612_CR22","doi-asserted-by":"publisher","first-page":"6787","DOI":"10.1007\/s11042-018-6319-4","volume":"78","author":"Z Liu","year":"2019","unstructured":"Liu Z, Song T, Xie F (2019) Rgb-d image saliency detection from 3d perspective. Multimed Tools Appl 78(6):6787\u20136804. https:\/\/doi.org\/10.1007\/s11042-018-6319-4","journal-title":"Multimed Tools Appl"},{"key":"3612_CR23","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neucom.2019.07.012","volume":"363","author":"Z Liu","year":"2019","unstructured":"Liu Z, Shi S, Duan Q, Zhang W, Zhao P (2019) Salient object detection for RGB-d image by single stream recurrent convolution neural network. Neurocomputing 363:46\u201357. https:\/\/doi.org\/10.1016\/j.neucom.2019.07.012https:\/\/doi.org\/10.1016\/j.neucom.2019.07.012","journal-title":"Neurocomputing"},{"issue":"9","key":"3612_CR24","doi-asserted-by":"publisher","first-page":"4204","DOI":"10.1109\/TIP.2017.2711277","volume":"26","author":"H Song","year":"2017","unstructured":"Song H, Liu Z, Du H, Sun G, Le meur O, Ren T (2017) Depth-Aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning. IEEE Trans Image Process 26(9):4204\u20134216. https:\/\/doi.org\/10.1109\/TIP.2017.2711277https:\/\/doi.org\/10.1109\/TIP.2017.2711277","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"3612_CR25","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TIP.2017.2682981","volume":"26","author":"L Qu","year":"2017","unstructured":"Qu L, He S, Zhang J, Tian J, Tang Y, Yang Q (2017) RGBD Salient object detection via deep fusion. IEEE Trans Image Process 26(5):2274\u20132285. https:\/\/doi.org\/10.1109\/TIP.2017.2682981","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"3612_CR26","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1109\/TNNLS.2020.2996406","volume":"32","author":"DP Fan","year":"2019","unstructured":"Fan D P, Lin Z, Zhao J X, Liu Y, Zhang Z, Hou Q, Zhu M, Cheng M M (2019) Rethinking RGB-d salient object detection: models, datasets, and large-scale benchmarks. IEEE Trans Neural Netw Learn Syst 32(5):2075\u20132089. https:\/\/doi.org\/10.1109\/tnnls.2020.2996406https:\/\/doi.org\/10.1109\/tnnls.2020.2996406","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3612_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICDSP.2018.8631584","volume":"DSP 2018-Novemb","author":"P Huang","year":"2019","unstructured":"Huang P, Shen C H, Hsiao H F (2019) RGBD Salient object detection using spatially coherent deep learning framework. International Conference on Digital Signal Processing DSP 2018-November:1\u20135. https:\/\/doi.org\/10.1109\/ICDSP.2018.8631584","journal-title":"International Conference on Digital Signal Processing"},{"key":"3612_CR28","doi-asserted-by":"publisher","unstructured":"Guo J, Ren T, Bei J (2016) Salient object detection for rgb-d image via saliency evolution. In: 2016 IEEE International conference on multimedia and expo (ICME), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME.2016.7552907https:\/\/doi.org\/10.1109\/ICME.2016.7552907","DOI":"10.1109\/ICME.2016.7552907 10.1109\/ICME.2016.7552907"},{"issue":"11","key":"3612_CR29","doi-asserted-by":"publisher","first-page":"3171","DOI":"10.1109\/TCYB.2017.2761775","volume":"48","author":"J Han","year":"2018","unstructured":"Han J (2018) Cnns-based rgb-d saliency detection via cross-view transfer and multiview fusion. IEEE Trans Cybern 48(11):3171\u20133183. https:\/\/doi.org\/10.1109\/TCYB.2017.2761775","journal-title":"IEEE Trans Cybern"},{"key":"3612_CR30","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. https:\/\/doi.org\/10.1109\/ACCESS.2019.2913107https:\/\/doi.org\/10.1109\/ACCESS.2019.2913107","journal-title":"IEEE Access"},{"key":"3612_CR31","doi-asserted-by":"publisher","unstructured":"Chen H, Li Y (2018) Progressively complementarity-aware fusion network for rgb-d salient object detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2018.00322https:\/\/doi.org\/10.1109\/CVPR.2018.00322, pp 3051\u20133060","DOI":"10.1109\/CVPR.2018.00322 10.1109\/CVPR.2018.00322"},{"key":"3612_CR32","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. https:\/\/doi.org\/10.1016\/j.patcog.2018.08.007","journal-title":"Pattern Recogn"},{"key":"3612_CR33","doi-asserted-by":"publisher","unstructured":"Liu N, Zhang N, Han J (2020) Learning selective self-mutual attention for RGB-d saliency detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01377https:\/\/doi.org\/10.1109\/CVPR42600.2020.01377, pp 13753\u201313762","DOI":"10.1109\/CVPR42600.2020.01377 10.1109\/CVPR42600.2020.01377"},{"issue":"1","key":"3612_CR34","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. https:\/\/doi.org\/10.1109\/TCYB.2020.2969255","journal-title":"IEEE Trans Cybern"},{"key":"3612_CR35","doi-asserted-by":"publisher","first-page":"2428","DOI":"10.1109\/TMM.2020.3011327","volume":"23","author":"N Huang","year":"2021","unstructured":"Huang N, Liu Y, Zhang Q, Han J (2021) Joint cross-modal and unimodal features for RGB-d salient object detection. IEEE Trans Multimed 23:2428\u20132441. https:\/\/doi.org\/10.1109\/TMM.2020.3011327https:\/\/doi.org\/10.1109\/TMM.2020.3011327","journal-title":"IEEE Trans Multimed"},{"key":"3612_CR36","doi-asserted-by":"publisher","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 computer society conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00353, pp 3472\u20133481","DOI":"10.1109\/CVPR42600.2020.00353"},{"key":"3612_CR37","doi-asserted-by":"publisher","unstructured":"Ji W, Li J, Zhang M, Piao Y, Lu H (2020) Accurate RGB-d Salient Object Detection Via Collaborative Learning vol 12363 LNCS, pp 52\u201369. https:\/\/doi.org\/10.1007\/978-3-030-58523-5_4","DOI":"10.1007\/978-3-030-58523-5_4"},{"key":"3612_CR38","doi-asserted-by":"publisher","unstructured":"Li G, Liu Z, Ye L, Wang Y, Ling H (2020) Cross-modal weighting network for RGB-d salient object detection. In: Computer vision - ECCV 2020: 16th european conference, pp 665\u2013681, DOI https:\/\/doi.org\/10.1007\/978-3-030-58520-4_39, (to appear in print)","DOI":"10.1007\/978-3-030-58520-4_39"},{"issue":"6","key":"3612_CR39","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. https:\/\/doi.org\/10.1109\/TIP.2019.2891104","journal-title":"IEEE Trans Image Process"},{"key":"3612_CR40","doi-asserted-by":"publisher","unstructured":"Zhang Y, Jiang G, Yu M, Chen K (2010) Stereoscopic visual attention model for 3d video. In: Advances in multimedia modeling. https:\/\/doi.org\/10.1007\/978-3-642-11301-7_33. Springer, Berlin, Heidelberg, pp 314\u2013324","DOI":"10.1007\/978-3-642-11301-7_33"},{"key":"3612_CR41","doi-asserted-by":"publisher","unstructured":"Desingh K, K MK, Rajan D, Jawahar C (2014) Depth really matters: improving visual salient region detection with depth. pp 98\u201319811. https:\/\/doi.org\/10.5244\/c.27.98","DOI":"10.5244\/c.27.98"},{"key":"3612_CR42","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.image.2015.07.002","volume":"38","author":"R Ju","year":"2015","unstructured":"Ju R, Liu Y, Ren T, Ge L, Wu G (2015) Depth-aware salient object detection using anisotropic center-surround difference. Signal Process Image Commun 38:115\u2013126. https:\/\/doi.org\/10.1016\/j.image.2015.07.002","journal-title":"Signal Process Image Commun"},{"key":"3612_CR43","doi-asserted-by":"publisher","unstructured":"Cheng Y, Fu H, Wei X, Xiao J, Cao X (2014) Depth enhanced saliency detection method. In: ACM International conference proceeding series, pp 23\u201327. https:\/\/doi.org\/10.1145\/2632856.2632866","DOI":"10.1145\/2632856.2632866"},{"key":"3612_CR44","doi-asserted-by":"publisher","unstructured":"Liu J J, Hou Q, Cheng M M, Feng J, Jiang J (2019) A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2019-June, pp 3912\u20133921, DOI https:\/\/doi.org\/10.1109\/CVPR.2019.00404, (to appear in print)","DOI":"10.1109\/CVPR.2019.00404"},{"key":"3612_CR45","doi-asserted-by":"publisher","unstructured":"Zhao J, 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 international conference on computer vision 2019-Octob(Iccv), pp 8778\u20138787, DOI https:\/\/doi.org\/10.1109\/ICCV.2019.00887https:\/\/doi.org\/10.1109\/ICCV.2019.00887, (to appear in print)","DOI":"10.1109\/ICCV.2019.00887 10.1109\/ICCV.2019.00887"},{"key":"3612_CR46","doi-asserted-by":"publisher","unstructured":"Feng M, Lu H, Ding E (2019) Attentive feedback network for boundary-aware salient object detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2019-June. https:\/\/doi.org\/10.1109\/CVPR.2019.00172, pp 1623\u20131632","DOI":"10.1109\/CVPR.2019.00172"},{"key":"3612_CR47","doi-asserted-by":"publisher","unstructured":"Qin X, Zhang Z, Huang C, Gao C, Dehghan M, Jagersand M (2019) Basnet: Boundary-aware salient object detection. In: 2019 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 7471\u20137481. https:\/\/doi.org\/10.1109\/CVPR.2019.00766","DOI":"10.1109\/CVPR.2019.00766"},{"issue":"8","key":"3612_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2021.3073689","volume":"14","author":"K Fu","year":"2021","unstructured":"Fu K, Fan D P, Ji G P, Zhao Q, Shen J, Zhu C (2021) Siamese network for RGB-d salient object detection and beyond. IEEE Trans Pattern Anal Mach Intell 14(8):1\u201318. https:\/\/doi.org\/10.1109\/TPAMI.2021.3073689","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3612_CR49","doi-asserted-by":"publisher","first-page":"3828","DOI":"10.1109\/TMM.2020.3032023","volume":"23","author":"H Wang","year":"2021","unstructured":"Wang H, Wang Y, Zhang Z, Fu X, Zhuo L, Xu M, Wang M (2021) Kernelized multiview subspace analysis by Self-Weighted learning. IEEE Trans Multimed 23:3828\u20133840. https:\/\/doi.org\/10.1109\/TMM.2020.3032023","journal-title":"IEEE Trans Multimed"},{"key":"3612_CR50","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1007\/978-3-030-01231-1_35","volume":"11210 LNCS","author":"R Deng","year":"2018","unstructured":"Deng R, Shen C, Liu S, Wang H, Liu X (2018) Learning to predict crisp boundaries. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11210 LNCS:570\u2013586. https:\/\/doi.org\/10.1007\/978-3-030-01231-1_35","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"issue":"8","key":"3612_CR51","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1109\/TPAMI.2018.2878849","volume":"41","author":"Y Liu","year":"2019","unstructured":"Liu Y, Cheng M M, Hu X, Bian J W, Zhang L, Bai X, Tang J (2019) Richer convolutional features for edge detection. IEEE Trans Pattern Anal Mach Intell 41(8):1939\u20131946. https:\/\/doi.org\/10.1109\/TPAMI.2018.2878849","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3612_CR52","doi-asserted-by":"publisher","unstructured":"Chen Z, Xu Q, Cong R, Huang Q (2020) Global context-aware progressive aggregation network for salient object detection. In: Arxiv, DOI https:\/\/doi.org\/10.1609\/aaai.v34i07.6633, (to appear in print)","DOI":"10.1609\/aaai.v34i07.6633"},{"key":"3612_CR53","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1109\/CVPRW.2015.7301391","volume":"2015-Octob","author":"J Ren","year":"2015","unstructured":"Ren J, Gong X, Yu L, Zhou W, Yang M Y (2015) Exploiting global priors for RGB-d saliency detection. IEEE Comput Soc Conf Comput Vision Pattern Recog Work 2015-Octob:25\u201332. https:\/\/doi.org\/10.1109\/CVPRW.2015.7301391","journal-title":"IEEE Comput Soc Conf Comput Vision Pattern Recog Work"},{"issue":"(PART 3","key":"3612_CR54","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-319-10578-9_7","volume":"8691 LNCS","author":"H Peng","year":"2014","unstructured":"Peng H, Li B, Xiong W, Hu W, Ji R (2014) RGBD Salient object detection: a benchmark and algorithms. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8691 LNCS((PART 3)):92\u2013109. https:\/\/doi.org\/10.1007\/978-3-319-10578-9_7","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"3612_CR55","doi-asserted-by":"publisher","unstructured":"Niu Y, Geng Y, Li X, Liu F (2012) Leveraging stereopsis for saliency analysis. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 454\u2013461, DOI https:\/\/doi.org\/10.1109\/CVPR.2012.6247708, (to appear in print)","DOI":"10.1109\/CVPR.2012.6247708"},{"issue":"8","key":"3612_CR56","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1109\/TPAMI.2016.2610425","volume":"39","author":"N Li","year":"2017","unstructured":"Li N, Ye J, Ji Y, Ling H, Yu J (2017) Saliency detection on light field. IEEE Trans Pattern Anal Mach Intell 39(8):1605\u20131616. https:\/\/doi.org\/10.1109\/TPAMI.2016.2610425","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3612_CR57","doi-asserted-by":"publisher","unstructured":"Fu K, Fan DP, Ji G P, Zhao Q (2020) JL-DCF: Joint Learning and densely-cooperative fusion framework for RGB-d salient object detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3049\u20133059, DOI https:\/\/doi.org\/10.1109\/CVPR42600.2020.00312, (to appear in print)","DOI":"10.1109\/CVPR42600.2020.00312"},{"issue":"12","key":"3612_CR58","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. https:\/\/doi.org\/10.1109\/TIP.2015.2487833","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"3612_CR59","doi-asserted-by":"publisher","first-page":"2622","DOI":"10.1007\/s11263-021-01490-8","volume":"129","author":"MM Cheng","year":"2021","unstructured":"Cheng M M, Fan D P (2021) Structure-measure: A new way to evaluate foreground maps. Int J Comput Vis 129(9):2622\u20132638. https:\/\/doi.org\/10.1007\/s11263-021-01490-8","journal-title":"Int J Comput Vis"},{"key":"3612_CR60","doi-asserted-by":"publisher","unstructured":"Fan D P, Gong C, Cao Y, Ren B, Cheng M M, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. In: IJCAI International Joint Conference on Artificial Intelligence 2018-July. https:\/\/doi.org\/10.24963\/ijcai.2018\/97https:\/\/doi.org\/10.24963\/ijcai.2018\/97, pp 698\u2013704","DOI":"10.24963\/ijcai.2018\/97 10.24963\/ijcai.2018\/97"},{"issue":"6","key":"3612_CR61","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 Processing Letters 23 (6):819\u2013823. https:\/\/doi.org\/10.1109\/LSP.2016.2557347","journal-title":"IEEE Signal Processing Letters"},{"key":"3612_CR62","doi-asserted-by":"publisher","unstructured":"Zhao JX, Cao Y, Fan DP et al (2019) Contrast prior and Fluid pyramid integration for rgbd salient object detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 2019-June, pp 3922\u20133931. https:\/\/doi.org\/10.1109\/CVPR.2019.00405","DOI":"10.1109\/CVPR.2019.00405"},{"key":"3612_CR63","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1109\/TMM.2020.2991523","volume":"23","author":"D Liu","year":"2020","unstructured":"Liu D, Zhang K, Chen Z (2020) Attentive cross-modal fusion network for RGB-D saliency detection. IEEE Trans Multimed 23:967\u2013981","journal-title":"IEEE Trans Multimed"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03612-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03612-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03612-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T02:43:29Z","timestamp":1678934609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03612-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,30]]},"references-count":63,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["3612"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03612-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,7,30]]},"assertion":[{"value":"10 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2022","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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}