{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T04:06:38Z","timestamp":1783656398409,"version":"3.55.0"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11263-024-02051-5","type":"journal-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T06:01:59Z","timestamp":1717480919000},"page":"5173-5191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection"],"prefix":"10.1007","volume":"132","author":[{"given":"Junhao","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3871-663X","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxing","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liansheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"2051_CR1","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 (pp. 1597\u20131604).","DOI":"10.1109\/CVPRW.2009.5206596"},{"issue":"2","key":"2051_CR2","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/TPAMI.2011.130","volume":"34","author":"S Alpert","year":"2011","unstructured":"Alpert, S., Galun, M., Brandt, A., & Basri, R. (2011). Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2), 315\u2013327.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"7","key":"2051_CR3","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1109\/TIP.2017.2670143","volume":"26","author":"C Chen","year":"2017","unstructured":"Chen, C., Li, S., Wang, Y., Qin, H., & Hao, A. (2017). Video saliency detection via spatial\u2013temporal fusion and low-rank coherency diffusion. IEEE Transactions on Image Processing, 26(7), 3156\u20133170.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2051_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., & Li, Y. (2018). Progressively complementarity-aware fusion network for RGB-D salient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 3051\u20133060).","DOI":"10.1109\/CVPR.2018.00322"},{"key":"2051_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, H. K. & Schwing, A. G. (2022). XMem: Long-term video object segmentation with an Atkinson\u2013Shiffrin memory model. In ECCV.","DOI":"10.1007\/978-3-031-19815-1_37"},{"key":"2051_CR6","first-page":"11781","volume-title":"Advances in neural information processing systems","author":"HK Cheng","year":"2021","unstructured":"Cheng, H. K., Tai, Y. W., & Tang, C. K. (2021). Rethinking space-time networks with improved memory coverage for efficient video object segmentation. In M. Ranzato, A. Beygelzimer, & Y. Dauphin (Eds.), Advances in neural information processing systems (Vol. 34, pp. 11781\u201311794). Berlin: Curran Associates, Inc."},{"issue":"3","key":"2051_CR7","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","volume":"37","author":"MM Cheng","year":"2014","unstructured":"Cheng, M. M., Mitra, N. J., Huang, X., Torr, P. H., & Hu, S. M. (2014). Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 569\u2013582.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2051_CR8","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Fu, H., Wei, X., Xiao, J., & Cao, X. (2014b). Depth enhanced saliency detection method. In Proceedings of international conference on internet multimedia computing and service, ICIMCS \u201914(pp. 23\u201327). Association for Computing Machinery.","DOI":"10.1145\/2632856.2632866"},{"key":"2051_CR9","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/978-3-031-20047-2_26","volume-title":"Computer vision\u2013ECCV 2022","author":"S Cho","year":"2022","unstructured":"Cho, S., Lee, H., Lee, M., Park, C., Jang, S., Kim, M., & Lee, S. (2022). Tackling background distraction in video object segmentation. In S. Avidan, G. Brostow, M. Ciss\u00e9, G. M. Farinella, & T. Hassner (Eds.), Computer vision\u2013ECCV 2022 (pp. 446\u2013462). Springer."},{"key":"2051_CR10","doi-asserted-by":"publisher","DOI":"10.5244\/C.27.112","volume-title":"An in depth view of saliency","author":"A Ciptadi","year":"2013","unstructured":"Ciptadi, A., Hermans, T., & Rehg, J. M. (2013). An in depth view of saliency. Georgia Institute of Technology."},{"issue":"10","key":"2051_CR11","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1109\/TCSVT.2018.2870832","volume":"29","author":"R Cong","year":"2019","unstructured":"Cong, R., Lei, J., Fu, H., Cheng, M. M., Lin, W., & Huang, Q. (2019). Review of visual saliency detection with comprehensive information. IEEE Transactions on Circuits and Systems for Video Technology, 29(10), 2941\u20132959.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"10","key":"2051_CR12","doi-asserted-by":"publisher","first-page":"4819","DOI":"10.1109\/TIP.2019.2910377","volume":"28","author":"R Cong","year":"2019","unstructured":"Cong, R., Lei, J., Fu, H., Porikli, F., Huang, Q., & Hou, C. (2019). Video saliency detection via sparsity-based reconstruction and propagation. IEEE Transactions on Image Processing, 28(10), 4819\u20134831.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2051_CR13","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. (2022). CIR-Net: Cross-modality interaction and refinement for RGB-D salient object detection. IEEE Transactions on Image Processing, 31, 6800\u20136815.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2051_CR14","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248\u2013255). IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2051_CR15","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 (ICCV).","DOI":"10.1109\/ICCV.2017.487"},{"key":"2051_CR16","doi-asserted-by":"crossref","unstructured":"Fan, D. P., Cheng, M. M., Liu, J. J., Gao, S. H., Hou, Q., & Borji, A. (2018). Salient objects in clutter: Bringing salient object detection to the foreground. In ECCV (pp. 186\u2013202).","DOI":"10.1007\/978-3-030-01267-0_12"},{"key":"2051_CR17","doi-asserted-by":"crossref","unstructured":"Fan, D. P., Wang, W., Cheng, M. M., & Shen, J. (2019). Shifting more attention to video salient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2019.00875"},{"issue":"5","key":"2051_CR18","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1109\/TNNLS.2020.2996406","volume":"32","author":"DP 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 Transactions on Neural Networks and Learning Systems, 32(5), 2075\u20132089.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2051_CR19","doi-asserted-by":"crossref","unstructured":"Fan, D. P., Zhai, Y., Borji, A., Yang, J., & Shao, L. (2020b). BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network. In Computer Vision\u2014ECCV (pp. 275\u2013292).","DOI":"10.1007\/978-3-030-58610-2_17"},{"key":"2051_CR20","doi-asserted-by":"crossref","unstructured":"Feng, C. M., Yan, Y., Fu, H., Chen, L., & Xu, Y. (2021). Task transformer network for joint MRI reconstruction and super-resolution. arXiv preprint arXiv:2106.06742","DOI":"10.1007\/978-3-030-87231-1_30"},{"key":"2051_CR21","doi-asserted-by":"crossref","unstructured":"Fu, K., Fan, D. P., 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\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 3052\u20133062).","DOI":"10.1109\/CVPR42600.2020.00312"},{"key":"2051_CR22","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S Gao","year":"2019","unstructured":"Gao, S., Cheng, M. M., Zhao, K., Zhang, X. Y., Yang, M. H., & Torr, P. (2019). Res2net: A new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652\u2013662.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2051_CR23","doi-asserted-by":"crossref","unstructured":"Gu, Y., Wang, L., Wang, Z., Liu, Y., Cheng, M. M., & Lu, S. P. (2020). Pyramid constrained self-attention network for fast video salient object detection. In Proceedings of the AAAI conference on artificial intelligence (pp. 10869\u201310876).","DOI":"10.1609\/aaai.v34i07.6718"},{"key":"2051_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778). IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2051_CR25","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., & Cheng, L. (2021). Calibrated RGB-D salient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 9466\u20139476).","DOI":"10.1109\/CVPR46437.2021.00935"},{"key":"2051_CR26","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) (pp. 1115\u20131119).","DOI":"10.1109\/ICIP.2014.7025222"},{"issue":"3","key":"2051_CR27","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.cmpb.2014.09.005","volume":"117","author":"B Kwolek","year":"2014","unstructured":"Kwolek, B., & Kepski, M. (2014). Human fall detection on embedded platform using depth maps and wireless accelerometer. Computer Methods and Programs in Biomedicine, 117(3), 489\u2013501.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"2051_CR28","doi-asserted-by":"crossref","unstructured":"Lai, K., Bo, L., & Fox, D. (2014). Unsupervised feature learning for 3D scene labeling. In IEEE international conference on robotics and automation (ICRA) (pp. 3050\u20133057). IEEE.","DOI":"10.1109\/ICRA.2014.6907298"},{"key":"2051_CR29","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-031-19818-2_36","volume-title":"Computer vision\u2013ECCV 2022","author":"M Lee","year":"2022","unstructured":"Lee, M., Park, C., Cho, S., & Lee, S. (2022). SPSN: Superpixel prototype sampling network for RGB-D salient object detection. In S. Avidan, G. Brostow, M. Ciss\u00e9, G. M. Farinella, & T. Hassner (Eds.), Computer vision\u2013ECCV 2022 (pp. 630\u2013647). Springer."},{"key":"2051_CR30","doi-asserted-by":"crossref","unstructured":"Li, F., Kim, T., Humayun, A., Tsai, D., & Rehg, J. M. (2013). Video segmentation by tracking many figure-ground segments. In Proceedings of the IEEE international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2013.273"},{"key":"2051_CR31","doi-asserted-by":"crossref","unstructured":"Li, G., Xie, Y., Lin, L., & Yu, Y. (2017). Instance-level salient object segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 2386\u20132395).","DOI":"10.1109\/CVPR.2017.34"},{"key":"2051_CR32","doi-asserted-by":"crossref","unstructured":"Li, G., & Yu, Y. (2015). Visual saliency based on multiscale deep features. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 5455\u20135463)","DOI":"10.1109\/CVPR.2015.7299184"},{"key":"2051_CR33","doi-asserted-by":"crossref","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\u2014ECCV (pp. 665\u2013681). Springer.","DOI":"10.1007\/978-3-030-58520-4_39"},{"key":"2051_CR34","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, G., Li, G., & Yu, Y. (2019). Motion guided attention for video salient object detection. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 7274\u20137283).","DOI":"10.1109\/ICCV.2019.00737"},{"issue":"1","key":"2051_CR35","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1109\/TIP.2017.2762594","volume":"27","author":"J Li","year":"2018","unstructured":"Li, J., Xia, C., & Chen, X. (2018). A benchmark dataset and saliency-guided stacked autoencoders for video-based salient object detection. IEEE Transactions on Image Processing, 27(1), 349\u2013364.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2051_CR36","doi-asserted-by":"crossref","unstructured":"Li, N., Ye, J., Ji, Y., Ling, H., & Yu, J. (2014a). Saliency detection on light field. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2014.359"},{"key":"2051_CR37","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, X., Koch, C., Rehg, J. M., & Yuille, A. L. (2014b). The secrets of salient object segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 280\u2013287).","DOI":"10.1109\/CVPR.2014.43"},{"key":"2051_CR38","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 (CVPR) (pp. 13753\u201313762).","DOI":"10.1109\/CVPR42600.2020.01377"},{"key":"2051_CR39","doi-asserted-by":"crossref","unstructured":"Liu, N., Zhang, N., Wan, K., Shao, L., & Han, J. (2021a). Visual saliency transformer. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 4722\u20134732).","DOI":"10.1109\/ICCV48922.2021.00468"},{"issue":"12","key":"2051_CR40","doi-asserted-by":"publisher","first-page":"9026","DOI":"10.1109\/TPAMI.2021.3122139","volume":"44","author":"N Liu","year":"2022","unstructured":"Liu, N., Zhang, N., Shao, L., & Han, J. (2022). Learning selective mutual attention and contrast for RGB-D saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9026\u20139042. https:\/\/doi.org\/10.1109\/TPAMI.2021.3122139","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2051_CR41","doi-asserted-by":"crossref","unstructured":"Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., & Shum, H. Y. (2010). Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2), 353\u2013367.","DOI":"10.1109\/TPAMI.2010.70"},{"key":"2051_CR42","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/978-3-031-19818-2_27","volume-title":"Computer vision\u2014ECCV 2022","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Yu, R., Yin, F., Zhao, X., Zhao, W., Xia, W., & Yang, Y. (2022). Learning quality-aware dynamic memory for video object segmentation. In S. Avidan, G. Brostow, M. Ciss\u00e9, G. M. Farinella, & T. Hassner (Eds.), Computer vision\u2014ECCV 2022 (pp. 468\u2013486). Springer."},{"key":"2051_CR43","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.","journal-title":"Neurocomputing"},{"key":"2051_CR44","doi-asserted-by":"publisher","unstructured":"Liu, Z., Wang, Y., Tu, Z., Xiao, Y., & Tang, B. (2021b). Tritransnet: RGB-D salient object detection with a triplet transformer embedding network. In Proceedings of the 29th ACM international conference on multimedia, MM \u201921 (pp. 4481\u20134490). Association for Computing Machinery https:\/\/doi.org\/10.1145\/3474085.3475601","DOI":"10.1145\/3474085.3475601"},{"key":"2051_CR45","doi-asserted-by":"crossref","unstructured":"Movahedi, V., Elder, J. H. (2010). Design and perceptual validation of performance measures for salient object segmentation. In 2010 IEEE computer society conference on computer vision and pattern recognition\u2014Workshops (pp. 49\u201356).","DOI":"10.1109\/CVPRW.2010.5543739"},{"key":"2051_CR46","doi-asserted-by":"crossref","unstructured":"Niu, Y., Geng, Y., Li, X., & Liu, F. (2012). Leveraging stereopsis for saliency analysis. In 2012 IEEE conference on computer vision and pattern recognition (pp. 454\u2013461).","DOI":"10.1109\/CVPR.2012.6247708"},{"key":"2051_CR47","doi-asserted-by":"crossref","unstructured":"Oh, S. W., Lee, J. Y., Xu, N., & Kim, S. J. (2019). Video object segmentation using space-time memory networks. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 9225\u20139234).","DOI":"10.1109\/ICCV.2019.00932"},{"key":"2051_CR48","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 European conference on computer vision (pp. 92\u2013109). Springer.","DOI":"10.1007\/978-3-319-10578-9_7"},{"key":"2051_CR49","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 Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 733\u2013740).","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"2051_CR50","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., & Sorkine-Hornung, A. (2016). A benchmark dataset and evaluation methodology for video object segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2016.85"},{"key":"2051_CR51","doi-asserted-by":"crossref","unstructured":"Piao, Y., Ji, W., Li, J., Zhang, M., & Lu, H. (2019). Depth-induced multi-scale recurrent attention network for saliency detection. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2019.00735"},{"key":"2051_CR52","doi-asserted-by":"crossref","unstructured":"Rajpal, A., Cheema, N., Illgner-Fehns, K., Slusallek, P., & Jaiswal, S. (2023). High-resolution synthetic RGB-D datasets for monocular depth estimation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 1188\u20131198).","DOI":"10.1109\/CVPRW59228.2023.00126"},{"key":"2051_CR53","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., & Koltun, V. (2021). Vision transformers for dense prediction. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 12179\u201312188).","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"2051_CR54","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1109\/TMM.2020.2997178","volume":"23","author":"Q Ren","year":"2021","unstructured":"Ren, Q., Lu, S., Zhang, J., & Hu, R. (2021). Salient object detection by fusing local and global contexts. IEEE Transactions on Multimedia, 23, 1442\u20131453. https:\/\/doi.org\/10.1109\/TMM.2020.2997178","journal-title":"IEEE Transactions on Multimedia"},{"key":"2051_CR55","doi-asserted-by":"crossref","unstructured":"Ren, S., Han, C., Yang, X., Han, G., & He, S. (2020). Tenet: Triple excitation network for video salient object detection. In Computer vision\u2014ECCV (pp. 212\u2013228). Springer.","DOI":"10.1007\/978-3-030-58558-7_13"},{"issue":"3","key":"2051_CR56","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211\u2013252.","journal-title":"International Journal of Computer Vision"},{"issue":"4","key":"2051_CR57","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1109\/TPAMI.2015.2465960","volume":"38","author":"J Shi","year":"2015","unstructured":"Shi, J., Yan, Q., Xu, L., & Jia, J. (2015). Hierarchical image saliency detection on extended CSSD. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(4), 717\u2013729.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2051_CR58","doi-asserted-by":"crossref","unstructured":"Song, S., & Xiao, J. (2013). Tracking revisited using RGBD camera: Unified benchmark and baselines. In Proceedings of the IEEE international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2013.36"},{"key":"2051_CR59","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., & Cremers, D. (2012). A benchmark for the evaluation of RGB-D slam systems. In Proceedings of the international conference on intelligent robot systems (IROS).","DOI":"10.1109\/IROS.2012.6385773"},{"key":"2051_CR60","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3264883","author":"Y Su","year":"2023","unstructured":"Su, Y., Deng, J., Sun, R., Lin, G., Su, H., & Wu, Q. (2023). A unified transformer framework for group-based segmentation: Co-segmentation, co-saliency detection and video salient object detection. IEEE Transactions on Multimedia. https:\/\/doi.org\/10.1109\/TMM.2023.3264883","journal-title":"IEEE Transactions on Multimedia"},{"key":"2051_CR61","doi-asserted-by":"crossref","unstructured":"Teed, Z., Deng, J. (2020). Raft: Recurrent all-pairs field transforms for optical flow. In Computer vision\u2014ECCV 2020 (pp. 402\u2013419). Springer.","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"2051_CR62","doi-asserted-by":"crossref","unstructured":"Wang, F., Hauser, K. (2019). In-hand object scanning via RGB-D video segmentation. In International conference on robotics and automation (ICRA) (pp. 3296\u20133302). IEEE.","DOI":"10.1109\/ICRA.2019.8794467"},{"key":"2051_CR63","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., & Ruan, X. (2017a). Learning to detect salient objects with image-level supervision. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 136\u2013145).","DOI":"10.1109\/CVPR.2017.404"},{"key":"2051_CR64","unstructured":"Wang, W., Shen, J., & Porikli, F. (2015a). Saliency-aware geodesic video object segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)."},{"issue":"11","key":"2051_CR65","doi-asserted-by":"publisher","first-page":"4185","DOI":"10.1109\/TIP.2015.2460013","volume":"24","author":"W Wang","year":"2015","unstructured":"Wang, W., Shen, J., & Shao, L. (2015). Consistent video saliency using local gradient flow optimization and global refinement. IEEE Transactions on Image Processing, 24(11), 4185\u20134196.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"1","key":"2051_CR66","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TIP.2017.2754941","volume":"27","author":"W Wang","year":"2017","unstructured":"Wang, W., Shen, J., & Shao, L. (2017). Video salient object detection via fully convolutional networks. IEEE Transactions on Image Processing, 27(1), 38\u201349.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2051_CR67","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, R., Fan, X., Wang, T., & He, X. (2023). Pixels, regions, and objects: Multiple enhancement for salient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 10031\u201310040).","DOI":"10.1109\/CVPR52729.2023.00967"},{"key":"2051_CR68","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, S., Huang, Q. (2020). F$$^3$$net: Fusion, feedback and focus for salient object detection. In Proceedings of the AAAI conference on artificial intelligence (pp. 12321\u201312328).","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"2051_CR69","doi-asserted-by":"crossref","unstructured":"Xia, C., Li, J., Chen, X., Zheng, A., & Zhang, Y. (2017). What is and what is not a salient object? Learning salient object detector by ensembling linear exemplar regressors. In Proceedings of the IEEE\/CVF conference on computer vision and pattern Recognition (CVPR) (pp. 4142\u20134150).","DOI":"10.1109\/CVPR.2017.468"},{"key":"2051_CR70","doi-asserted-by":"crossref","unstructured":"Yan, P., Li, G., Xie, Y., Li, Z., Wang, C., Chen, T., & Lin, L. (2019). Semi-supervised video salient object detection using pseudo-labels. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2019.00738"},{"key":"2051_CR71","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhang, P., Zhang, J., Lin, Z., & Lu, H. (2019). Towards high-resolution salient object detection. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 7234\u20137243).","DOI":"10.1109\/ICCV.2019.00733"},{"key":"2051_CR72","doi-asserted-by":"crossref","unstructured":"Zhai, Y., Fan, D. P., Yang, J., Borji, A., Shao, L., Han, J., & Wang, L. (2021). Bifurcated backbone strategy for RGB-D salient object detection. IEEE Transactions on Image Processing, 30, 8727\u20138742.","DOI":"10.1109\/TIP.2021.3116793"},{"key":"2051_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, J., Fan, D. P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020a). UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 8579\u20138588).","DOI":"10.1109\/CVPR42600.2020.00861"},{"key":"2051_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, J., Fan, D. P., Dai, Y., Yu, X., Zhong, Y., Barnes, N., & Shao, L. (2021a). RGB-D saliency detection via cascaded mutual information minimization. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 4338\u20134347).","DOI":"10.1109\/ICCV48922.2021.00430"},{"key":"2051_CR75","doi-asserted-by":"crossref","unstructured":"Zhang, J., Fan, D. P., Dai, Y., Yu, X., Zhong, Y., Barnes, N., & Shao, L. (2021b). RGB-D saliency detection via cascaded mutual information minimization. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 4338\u20134347).","DOI":"10.1109\/ICCV48922.2021.00430"},{"key":"2051_CR76","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, S., Sameki, M., Sclaroff, S., Betke, M., Lin, Z., Shen, X., Price, B., & Mech, R. (2015). Salient object subitizing. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 4045\u20134054).","DOI":"10.1109\/CVPR.2015.7299031"},{"key":"2051_CR77","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhang, J., Lin, Z., Lu, H., & He, Y. (2019) Capsal: Leveraging captioning to boost semantics for salient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 6024\u20136033).","DOI":"10.1109\/CVPR.2019.00618"},{"key":"2051_CR78","doi-asserted-by":"crossref","unstructured":"Zhang, M., Liu, J., Wang, Y., Piao, Y., Yao, S., Ji, W., Li, J., Lu, H., & Luo, Z. (2021c). Dynamic context-sensitive filtering network for video salient object detection. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 1553\u20131563).","DOI":"10.1109\/ICCV48922.2021.00158"},{"key":"2051_CR79","doi-asserted-by":"crossref","unstructured":"Zhang, M., Ren, W., Piao, Y., Rong, Z., & Lu, H. (2020b). Select, supplement and focus for RGB-D saliency detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR42600.2020.00353"},{"key":"2051_CR80","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 (ICCV).","DOI":"10.1109\/ICCV.2019.00887"},{"key":"2051_CR81","doi-asserted-by":"crossref","unstructured":"Zhao, W., Zhang, J., Li, L., Barnes, N., Liu, N., & Han, J. (2021). Weakly supervised video salient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 16826\u201316835).","DOI":"10.1109\/CVPR46437.2021.01655"},{"key":"2051_CR82","doi-asserted-by":"crossref","unstructured":"Zhou, T., Fu, H., Chen, G., Zhou, Y., Fan, D. P., & Shao, L. (2021). Specificity-preserving RGB-D saliency detection. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 4681\u20134691).","DOI":"10.1109\/ICCV48922.2021.00464"},{"key":"2051_CR83","unstructured":"Zhu, C., Li, G. (2017). A three-pathway psychobiological framework of salient object detection using stereoscopic technology. In Proceedings of the IEEE international conference on computer vision (ICCV) workshops."},{"key":"2051_CR84","doi-asserted-by":"publisher","unstructured":"Zhuge, M., Fan, D. P., Liu, N., Zhang, D., Xu, D., & Shao, L. (2023). Salient object detection via integrity learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3738\u20133752. https:\/\/doi.org\/10.1109\/TPAMI.2022.3179526","DOI":"10.1109\/TPAMI.2022.3179526"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02051-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02051-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02051-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T05:16:19Z","timestamp":1729919779000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02051-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,4]]},"references-count":84,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["2051"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02051-5","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,4]]},"assertion":[{"value":"1 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}