{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:12:07Z","timestamp":1784218327446,"version":"3.55.0"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T00:00:00Z","timestamp":1741219200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T00:00:00Z","timestamp":1741219200000},"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":["62261038"],"award-info":[{"award-number":["62261038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62261038"],"award-info":[{"award-number":["62261038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62261038"],"award-info":[{"award-number":["62261038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62261038"],"award-info":[{"award-number":["62261038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62261038"],"award-info":[{"award-number":["62261038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62261038"],"award-info":[{"award-number":["62261038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62261038"],"award-info":[{"award-number":["62261038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00371-025-03827-7","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T03:04:02Z","timestamp":1741230242000},"page":"7617-7640","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Red green blue-depth salient object detection based on multi-scale refinement and cross-modalities fusion network"],"prefix":"10.1007","volume":"41","author":[{"given":"Kehao","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiping","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kewei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taoyong","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaozhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenghao","family":"Ying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,6]]},"reference":[{"key":"3827_CR1","doi-asserted-by":"crossref","unstructured":"Nie, G.-Y., Cheng, M.-M., Liu, Y., Liang, Z., Fan, D.-P., Liu, Y., & Wang, Y.: Multi-level context ultra-aggregation for stereo matching. Paper presented at the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2019)","DOI":"10.1109\/CVPR.2019.00340"},{"key":"3827_CR2","doi-asserted-by":"crossref","unstructured":"Rapantzikos, K., Avrithis, Y., & Kollias, S.: Dense saliency-based spatiotemporal feature points for action recognition. Paper presented at the 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009)","DOI":"10.1109\/CVPRW.2009.5206525"},{"key":"3827_CR3","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. Paper presented at the International conference on medical image computing and computer-assisted intervention (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"issue":"8","key":"3827_CR4","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"D-P Fan","year":"2020","unstructured":"Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shao, L.: Inf-net: automatic covid-19 lung infection segmentation from ct images. IEEE Trans. Med. Imaging 39(8), 2626\u20132637 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3827_CR5","doi-asserted-by":"crossref","unstructured":"Zhu, C., Li, G., Wang, W., & Wang, R.: An innovative salient object detection using center-dark channel prior. Paper presented at the Proceedings of the IEEE international conference on computer vision workshops (2017)","DOI":"10.1109\/ICCVW.2017.178"},{"issue":"3","key":"3827_CR6","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, N.J., Huang, X., Torr, P.H., Hu, S.-M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569\u2013582 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3827_CR7","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-319-10578-9_7","volume-title":"Computer Vision \u2013 ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III","author":"H Peng","year":"2014","unstructured":"Peng, H., Li, B., Xiong, W., Weiming, Hu., Ji, R.: RGBD Salient Object Detection: A Benchmark and Algorithms. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision \u2013 ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp. 92\u2013109. Springer International Publishing, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_7"},{"key":"3827_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, J., Fan, D.-P., Dai, Y., Yu, X., Zhong, Y., Barnes, N., & Shao, L.: RGB-D saliency detection via cascaded mutual information minimization. Paper presented at the Proceedings of the IEEE\/CVF international conference on computer vision (2021)","DOI":"10.1109\/ICCV48922.2021.00430"},{"key":"3827_CR9","unstructured":"Niu, Y., Geng, Y., Li, X., & Liu, F.: Leveraging stereopsis for saliency analysis. Paper presented at the 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012)"},{"key":"3827_CR10","doi-asserted-by":"crossref","unstructured":"Ju, R., Ge, L., Geng, W., Ren, T., & Wu, G.: Depth saliency based on anisotropic center-surround difference. Paper presented at the 2014 IEEE international conference on image processing (ICIP) (2014)","DOI":"10.1109\/ICIP.2014.7025222"},{"key":"3827_CR11","doi-asserted-by":"crossref","unstructured":"Piao, Y., Ji, W., Li, J., Zhang, M., & Lu, H.: Depth-induced multi-scale recurrent attention network for saliency detection. Paper presented at the Proceedings of the IEEE\/CVF international conference on computer vision (2019)","DOI":"10.1109\/ICCV.2019.00735"},{"key":"3827_CR12","doi-asserted-by":"crossref","unstructured":"Chen, H., & Li, Y.: Progressively complementarity-aware fusion network for RGB-D salient object detection. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00322"},{"key":"3827_CR13","doi-asserted-by":"crossref","unstructured":"Ren, J., Gong, X., Yu, L., Zhou, W., & Ying Yang, M.: Exploiting global priors for RGB-D saliency detection. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition workshops (2015)","DOI":"10.1109\/CVPRW.2015.7301391"},{"issue":"9","key":"3827_CR14","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.: Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning. IEEE Trans. Image Process. 26(9), 4204 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"3827_CR15","doi-asserted-by":"publisher","first-page":"7632","DOI":"10.1109\/TCSVT.2022.3180274","volume":"32","author":"X Jin","year":"2022","unstructured":"Jin, X., Yi, K., Xu, J.: MoADNet: mobile asymmetric dual-stream networks for real-time and lightweight RGB-D salient object detection. IEEE Trans. Circuits Syst. Video Technol. 32(11), 7632\u20137645 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3827_CR16","doi-asserted-by":"publisher","first-page":"6800","DOI":"10.1109\/TIP.2022.3216198","volume":"31","author":"R Cong","year":"2022","unstructured":"Cong, R., Lin, Q., Zhang, C., Li, C., Cao, X., Huang, Q., Zhao, Y.: CIR-Net: Cross-modality interaction and refinement for RGB-D salient object detection. IEEE Trans. Image Process. 31, 6800\u20136815 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR17","doi-asserted-by":"publisher","first-page":"4873","DOI":"10.1109\/TIP.2020.2976689","volume":"29","author":"G Li","year":"2020","unstructured":"Li, G., Liu, Z., Ling, H.: ICNet: Information conversion network for RGB-D based salient object detection. IEEE Trans. Image Process. 29, 4873\u20134884 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR18","doi-asserted-by":"crossref","unstructured":"Fu, K., Fan, D.-P., Ji, G.-P., & Zhao, Q.: JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection. Paper presented at the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00312"},{"issue":"11","key":"3827_CR19","doi-asserted-by":"publisher","first-page":"3171","DOI":"10.1109\/TCYB.2017.2761775","volume":"48","author":"J Han","year":"2017","unstructured":"Han, J., Chen, H., Liu, N., Yan, C., Li, X.: CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion. IEEE Trans. Cybern. 48(11), 3171\u20133183 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"3827_CR20","doi-asserted-by":"crossref","unstructured":"Desingh, K., Krishna, K. M., Rajan, D., & Jawahar, C.: Depth really Matters: Improving Visual Salient Region Detection with Depth. Paper presented at the BMVC (2013)","DOI":"10.5244\/C.27.98"},{"key":"3827_CR21","doi-asserted-by":"crossref","unstructured":"Li, G., Liu, Z., Ye, L., Wang, Y., & Ling, H.: Cross-modal weighting network for RGB-D salient object detection. Paper presented at the European conference on computer vision (2020)","DOI":"10.1007\/978-3-030-58520-4_39"},{"key":"3827_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, J.-X., Cao, Y., Fan, D.-P., Cheng, M.-M., Li, X.-Y., & Zhang, L.: Contrast prior and fluid pyramid integration for RGBD salient object detection. Paper presented at the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2019)","DOI":"10.1109\/CVPR.2019.00405"},{"issue":"5","key":"3827_CR23","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.: Rethinking RGB-D salient object detection: models, data sets, and large-scale benchmarks. IEEE Trans. Neural Netw. Learn. Syst. 32(5), 2075\u20132089 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3827_CR24","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.: CDNet: Complementary depth network for RGB-D salient object detection. IEEE Trans. Image Process. 30, 3376\u20133390 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhao, Y., Li, J., & Chen, X.: Is depth really necessary for salient object detection? Paper presented at the Proceedings of the 28th ACM international conference on multimedia (2020)","DOI":"10.1145\/3394171.3413855"},{"key":"3827_CR26","doi-asserted-by":"publisher","first-page":"8407","DOI":"10.1109\/TIP.2020.3014734","volume":"29","author":"H Chen","year":"2020","unstructured":"Chen, H., Deng, Y., Li, Y., Hung, T.-Y., Lin, G.: RGBD salient object detection via disentangled cross-modal fusion. IEEE Trans. Image Process. 29, 8407\u20138416 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR27","unstructured":"Pang, Y., Zhang, L., Zhao, X., & Lu, H.: Hierarchical dynamic filtering network for RGB-D salient object detection. Paper presented at the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXV 16 (2020)"},{"issue":"2","key":"3827_CR28","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2019","unstructured":"Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., Torr, P.: Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652\u2013662 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3827_CR29","doi-asserted-by":"crossref","unstructured":"Cong, R., Liu, H., Zhang, C., Zhang, W., Zheng, F., Song, R., & Kwong, S.: Point-aware interaction and cnn-induced refinement network for RGB-D salient object detection. Paper presented at the Proceedings of the 31st ACM International Conference on Multimedia (2023)","DOI":"10.1145\/3581783.3611982"},{"key":"3827_CR30","doi-asserted-by":"crossref","unstructured":"Jiang, Z., & Davis, L. S.: Submodular salient region detection. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2013)","DOI":"10.1109\/CVPR.2013.266"},{"key":"3827_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104351","volume":"117","author":"C Yao","year":"2022","unstructured":"Yao, C., Feng, L., Kong, Y., Li, S., Li, H.: Double cross-modality progressively guided network for RGB-D salient object detection. Image Vis. Comput. 117, 104351 (2022)","journal-title":"Image Vis. Comput."},{"issue":"9","key":"3827_CR32","first-page":"5761","volume":"44","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F., Aliakbarian, S., Barnes, N.: Uncertainty inspired RGB-D saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5761\u20135779 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3827_CR33","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, W., Wang, H., Li, S., & Li, X.: Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion. Paper presented at the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00146"},{"key":"3827_CR34","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., Arasanipalai, A. U., & Hou, Q.: Rotate to attend: Convolutional triplet attention module. Paper presented at the Proceedings of the IEEE\/CVF winter conference on applications of computer vision (2021)","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"3827_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, W., Jiang, Y., Fu, K., & Zhao, Q.: BTS-Net: Bi-directional transfer-and-selection network for RGB-D salient object detection. Paper presented at the 2021 IEEE International Conference on Multimedia and Expo (ICME) (2021)","DOI":"10.1109\/ICME51207.2021.9428263"},{"key":"3827_CR36","doi-asserted-by":"publisher","first-page":"8727","DOI":"10.1109\/TIP.2021.3116793","volume":"30","author":"Y Zhai","year":"2021","unstructured":"Zhai, Y., Fan, D.-P., Yang, J., Borji, A., Shao, L., Han, J., Wang, L.: Bifurcated backbone strategy for RGB-D salient object detection. IEEE Trans. Image Process. 30, 8727\u20138742 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR37","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.1109\/TIP.2021.3052069","volume":"30","author":"C Chen","year":"2021","unstructured":"Chen, C., Wei, J., Peng, C., Qin, H.: Depth-quality-aware salient object detection. IEEE Trans. Image Process. 30, 2350\u20132363 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR38","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/TIP.2020.3037470","volume":"30","author":"X Wang","year":"2020","unstructured":"Wang, X., Li, S., Chen, C., Fang, Y., Hao, A., Qin, H.: Data-level recombination and lightweight fusion scheme for RGB-D salient object detection. IEEE Trans. Image Process. 30, 458\u2013471 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR39","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: EAPT: efficient attention pyramid transformer for image processing. IEEE Trans. Multimedia 25, 50\u201361 (2021)","journal-title":"IEEE Trans. Multimedia"},{"key":"3827_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2022.104549","volume":"127","author":"X Jia","year":"2022","unstructured":"Jia, X., DongYe, C., Peng, Y.: SiaTrans: Siamese transformer network for RGB-D salient object detection with depth image classification. Image Vis. Comput. 127, 104549 (2022)","journal-title":"Image Vis. Comput."},{"key":"3827_CR41","doi-asserted-by":"crossref","unstructured":"Fang, X., Zhu, J., Shao, X., & Wang, H.: GroupTransNet: Group transformer network for RGB-D salient object detection. arXiv preprint arXiv:2203.10785 (2022).","DOI":"10.2139\/ssrn.4585918"},{"key":"3827_CR42","doi-asserted-by":"crossref","unstructured":"Liu, N., Zhang, N., & Han, J.: Learning selective self-mutual attention for RGB-D saliency detection. Paper presented at the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01377"},{"key":"3827_CR43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J.: Deep residual learning for image recognition. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3827_CR44","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., & He, K.: Aggregated residual transformations for deep neural networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"3827_CR45","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., . . . Tang, X.: Residual attention network for image classification. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2017)","DOI":"10.1109\/CVPR.2017.683"},{"issue":"8","key":"3827_CR46","doi-asserted-by":"publisher","first-page":"4499","DOI":"10.1109\/TNNLS.2021.3116209","volume":"34","author":"Z Xie","year":"2021","unstructured":"Xie, Z., Zhang, W., Sheng, B., Li, P., Chen, C.P.: BaGFN: broad attentive graph fusion network for high-order feature interactions. IEEE Trans. Neural Netw. Learn. Syst. 34(8), 4499\u20134513 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3827_CR47","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., & Belongie, S.: Feature pyramid networks for object detection. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"3827_CR48","unstructured":"Ronneberger, O., Fischer, P., & Brox, T.: U-net: Convolutional networks for biomedical image segmentation. Paper presented at the Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18 (2015)"},{"key":"3827_CR49","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J.: Path aggregation network for instance segmentation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"3827_CR50","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Zhang, L., & Lu, H.: Multi-scale interactive network for salient object detection. Paper presented at the Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"3827_CR51","unstructured":"Jaderberg, M., Simonyan, K., & Zisserman, A.: Spatial transformer networks. Advances in neural information processing systems, 28 (2015)."},{"key":"3827_CR52","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G.: Squeeze-and-excitation networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3827_CR53","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S.: Cbam: Convolutional block attention module. Paper presented at the Proceedings of the European conference on computer vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3827_CR54","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I.: Attention is all you need. Advances in neural information processing systems, 30 (2017)."},{"key":"3827_CR55","unstructured":"Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., & Shao, L.: Learning enriched features for real image restoration and enhancement. Paper presented at the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXV 16 (2020)"},{"issue":"9","key":"3827_CR56","doi-asserted-by":"publisher","first-page":"3855","DOI":"10.1109\/TCYB.2020.2992433","volume":"50","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Wei, Y., Yang, Y., Huang, T.S.: Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE Trans. Cybern 50(9), 3855\u20133865 (2020)","journal-title":"IEEE Trans. Cybern"},{"key":"3827_CR57","doi-asserted-by":"crossref","unstructured":"Li, N., Ye, J., Ji, Y., Ling, H., & Yu, J.: Saliency detection on light field. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition (2014)","DOI":"10.1109\/CVPR.2014.359"},{"key":"3827_CR58","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S.: Frequency-tuned salient region detection. Paper presented at the 2009 IEEE conference on computer vision and pattern recognition (2009)","DOI":"10.1109\/CVPRW.2009.5206596"},{"key":"3827_CR59","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., & Hornung, A.: Saliency filters: Contrast based filtering for salient region detection. Paper presented at the 2012 IEEE conference on computer vision and pattern recognition (2012)","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"3827_CR60","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., & Borji, A.: Structure-measure: A new way to evaluate foreground maps. Paper presented at the Proceedings of the IEEE international conference on computer vision (2017)","DOI":"10.1109\/ICCV.2017.487"},{"key":"3827_CR61","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., & Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018).","DOI":"10.24963\/ijcai.2018\/97"},{"key":"3827_CR62","unstructured":"Glorot, X., & Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. Paper presented at the Proceedings of the thirteenth international conference on artificial intelligence and statistics (2010)"},{"issue":"8","key":"3827_CR63","doi-asserted-by":"publisher","first-page":"5346","DOI":"10.1109\/TCSVT.2022.3144852","volume":"32","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Qin, Q., Luo, Y., Liu, Y., Zhang, Q., Han, J.: Bi-directional progressive guidance network for RGB-D salient object detection. IEEE Trans. Circuits Syst. Video Technol. 32(8), 5346\u20135360 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3827_CR64","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.1109\/TMM.2023.3294003","volume":"26","author":"F Sun","year":"2023","unstructured":"Sun, F., Ren, P., Yin, B., Wang, F., Li, H.: CATNet: a cascaded and aggregated transformer network for RGB-D salient object detection. IEEE Trans. Multimed. 26, 2249\u20132262 (2023)","journal-title":"IEEE Trans. Multimed."},{"issue":"4","key":"3827_CR65","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1109\/TCSVT.2022.3215979","volume":"33","author":"G Chen","year":"2022","unstructured":"Chen, G., Shao, F., Chai, X., Chen, H., Jiang, Q., Meng, X., Ho, Y.-S.: Modality-induced transfer-fusion network for RGB-D and RGB-T salient object detection. IEEE Trans. Circuits Syst. Video Technol. 33(4), 1787\u20131801 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3827_CR66","doi-asserted-by":"publisher","first-page":"6124","DOI":"10.1109\/TIP.2022.3205747","volume":"31","author":"M Song","year":"2022","unstructured":"Song, M., Song, W., Yang, G., Chen, C.: Improving RGB-D salient object detection via modality-aware decoder. IEEE Trans. Image Process. 31, 6124\u20136138 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR67","doi-asserted-by":"publisher","first-page":"4253","DOI":"10.1109\/TMM.2022.3172852","volume":"25","author":"X Cheng","year":"2022","unstructured":"Cheng, X., Zheng, X., Pei, J., Tang, H., Lyu, Z., Chen, C.: Depth-induced gap-reducing network for RGB-D salient object detection: an interaction, guidance and refinement approach. IEEE Trans. Multimed. 25, 4253\u20134266 (2022)","journal-title":"IEEE Trans. Multimed."},{"key":"3827_CR68","first-page":"1","volume":"25","author":"Z Miao","year":"2022","unstructured":"Miao, Z., Shunyu, Y., Beiqi, H.: C2DFNet: Criss-cross Dynamic Filter Network for RGB-D Salient Object Detection [J\/OL]. IEEE Trans. Multimed. 25, 1\u201313 (2022)","journal-title":"IEEE Trans. Multimed."},{"key":"3827_CR69","doi-asserted-by":"crossref","unstructured":"Lee, M., Park, C., Cho, S., & Lee, S.: Spsn: Superpixel prototype sampling network for rgb-d salient object detection. Paper presented at the European conference on computer vision (2022)","DOI":"10.1007\/978-3-031-19818-2_36"},{"key":"3827_CR70","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.: Hidanet: Rgb-d salient object detection via hierarchical depth awareness. IEEE Trans. Image Process. 32, 2160\u20132173 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR71","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TCSVT.2023.3296581","volume":"34","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Qin, Q., Yang, Y., Jiao, Q., Han, J.: Feature calibrating and fusing network for RGB-D salient object detection. IEEE Trans. Circuits Syst. Video Technol. 34, 1493\u20131507 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3827_CR72","doi-asserted-by":"publisher","first-page":"5340","DOI":"10.1109\/TIP.2023.3315511","volume":"32","author":"S Yao","year":"2023","unstructured":"Yao, S., Zhang, M., Piao, Y., Qiu, C., Lu, H.: Depth Injection Framework for RGBD Salient Object Detection. IEEE Trans. Image Process. 32, 5340\u20135352 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR73","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.neucom.2022.10.081","volume":"520","author":"C Yao","year":"2023","unstructured":"Yao, C., Feng, L., Kong, Y., Xiao, L., Chen, T.: Transformers and CNNs fusion network for salient object detection. Neurocomputing 520, 342\u2013355 (2023)","journal-title":"Neurocomputing"},{"key":"3827_CR74","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.: Caver: Cross-modal view-mixed transformer for bi-modal salient object detection. IEEE Trans. Image Process. 32, 892\u2013904 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"3827_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126779","volume":"559","author":"C Zeng","year":"2023","unstructured":"Zeng, C., Kwong, S., Ip, H.: Dual Swin-transformer based mutual interactive network for RGB-D salient object detection. Neurocomputing 559, 126779 (2023)","journal-title":"Neurocomputing"},{"key":"3827_CR76","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.: Transformer fusion and pixel-level contrastive learning for RGB-D salient object detection. IEEE Trans. Multimed. 26, 1011\u20131026 (2023)","journal-title":"IEEE Trans. Multimed."},{"key":"3827_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120075","volume":"225","author":"S Kanwal","year":"2023","unstructured":"Kanwal, S., Taj, I.A.: CVit-Net: a conformer driven RGB-D salient object detector with operation-wise attention learning. Expert Syst. Appl. 225, 120075 (2023)","journal-title":"Expert Syst. Appl."},{"key":"3827_CR78","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.neucom.2022.11.031","volume":"518","author":"L Gao","year":"2023","unstructured":"Gao, L., Liu, B., Fu, P., Xu, M.: Depth-aware inverted refinement network for RGB-D salient object detection. Neurocomputing 518, 507\u2013522 (2023)","journal-title":"Neurocomputing"},{"key":"3827_CR79","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ins.2023.01.032","volume":"626","author":"L Wei","year":"2023","unstructured":"Wei, L., Zong, G.: EGA-Net: Edge feature enhancement and global information attention network for RGB-D salient object detection. Inf. Sci. 626, 223\u2013248 (2023)","journal-title":"Inf. Sci."},{"key":"3827_CR80","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2023.103880","volume":"95","author":"X Li","year":"2023","unstructured":"Li, X., Zhang, Q., Yan, W., Dai, M.: Depth cue enhancement and guidance network for RGB-D salient object detection. J. Vis. Commun. Image Represent. 95, 103880 (2023)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"3827_CR81","doi-asserted-by":"crossref","unstructured":"Gao, L., Fu, P., Feng, L., Wang, T., & Liu, B.: Cross-Modality Global Correlational-based Visual Transformer for RGB-D Salient Object Detection. Paper presented at the 2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) (2022)","DOI":"10.1109\/ICSMD57530.2022.10058434"},{"key":"3827_CR82","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3521699","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Zhang, R., Xu, L., Lu, X., Yu, Y., Xu, M., Zhao, H.: FasterSal: Robust and Real-time Single-Stream Architecture for RGB-D Salient Object Detection. IEEE Trans. Multimed. (2024). https:\/\/doi.org\/10.1109\/TMM.2024.3521699","journal-title":"IEEE Trans. Multimed."},{"key":"3827_CR83","doi-asserted-by":"publisher","first-page":"111996","DOI":"10.1016\/j.knosys.2024.111996","volume":"299","author":"K Zuo","year":"2024","unstructured":"Zuo, K., Xiao, H., Zhang, H., Chen, D., Liu, T., Li, Y., Wen, H.: Improving RGB-D salient object detection by addressing inconsistent saliency problems. Knowl.-Based Syst. 299, 111996 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"3827_CR84","doi-asserted-by":"publisher","first-page":"11592","DOI":"10.1109\/TCSVT.2024.3424651","volume":"34","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Zhang, L., Zhang, P., Zhuge, Y., Wu, J., Yu, H., Lu, H.: Learning Local-Global Representation for Scribble-based RGB-D Salient Object Detection via Transformer. IEEE Trans. Circuits Syst. Video Technol. 34, 11592\u201311604 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03827-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-03827-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03827-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:56:14Z","timestamp":1757141774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-03827-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,6]]},"references-count":84,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["3827"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-03827-7","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,6]]},"assertion":[{"value":"21 January 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2025","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}