{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:23:33Z","timestamp":1740108213133,"version":"3.37.3"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"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 Science Foundation of China","doi-asserted-by":"crossref","award":["6170134"],"award-info":[{"award-number":["6170134"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s00138-023-01462-7","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T16:02:06Z","timestamp":1695225726000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Group attention retention network for co-salient object detection"],"prefix":"10.1007","volume":"34","author":[{"given":"Jing","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weikang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiexiao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"1462_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, J., Cho, S., Kwak, S.: Weakly supervised learning of instance segmentation with inter-pixel relations. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2204\u20132213 (2019)","DOI":"10.1109\/CVPR.2019.00231"},{"key":"1462_CR2","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1109\/TMM.2021.3138246","volume":"25","author":"Z Bai","year":"2023","unstructured":"Bai, Z., Liu, Z., Li, G., Wang, Y.: Adaptive group-wise consistency network for co-saliency detection. IEEE Trans. Multimed. 25, 764\u2013776 (2023). https:\/\/doi.org\/10.1109\/TMM.2021.3138246","journal-title":"IEEE Trans. Multimed."},{"key":"1462_CR3","doi-asserted-by":"crossref","unstructured":"Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: icoseg: Interactive co-segmentation with intelligent scribble guidance. In: Computer Vision and Pattern Recognition (2010)","DOI":"10.1109\/CVPR.2010.5540080"},{"issue":"9","key":"1462_CR4","first-page":"4175","volume":"23","author":"X Cao","year":"2014","unstructured":"Cao, X., Tao, Z., Zhang, B., Fu, H., Feng, W.: Self-adaptively weighted co-saliency detection via rank constraint. IEEE Trans. Image Process. 23(9), 4175\u20134186 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"1462_CR5","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision\u2013ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision\u2013ECCV 2020, pp. 213\u2013229. Springer International Publishing, Cham (2020)"},{"key":"1462_CR6","doi-asserted-by":"crossref","unstructured":"Chang, K.-Y., Liu, T.-L., Lai, S.-H.: From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model. In: CVPR 2011, pp. 2129\u20132136 (2011)","DOI":"10.1109\/CVPR.2011.5995415"},{"issue":"12","key":"1462_CR7","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1109\/TPAMI.2015.2420556","volume":"37","author":"H-Y Chen","year":"2015","unstructured":"Chen, H.-Y., Lin, Y.-Y., Chen, B.-Y.: Co-segmentation guided hough transform for robust feature matching. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2388\u20132401 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1462_CR8","doi-asserted-by":"publisher","first-page":"7012","DOI":"10.1109\/TIP.2020.3028289","volume":"30","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Cong, R., Xu, Q., Huang, Q.: DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection. IEEE Trans. Image Process. 30, 7012\u20137024 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"1462_CR9","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2\u20137, 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"1462_CR10","doi-asserted-by":"crossref","unstructured":"Fan, Q., Fan, D.-P., Fu, H., Tang, C.-K., Shao, L., Tai, Y.-W.: Group collaborative learning for co-salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12288\u201312298 (2021)","DOI":"10.1109\/CVPR46437.2021.01211"},{"key":"1462_CR11","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:1805.10421 (2018)","DOI":"10.24963\/ijcai.2018\/97"},{"issue":"8","key":"1462_CR12","doi-asserted-by":"publisher","first-page":"4339","DOI":"10.1109\/TPAMI.2021.3060412","volume":"44","author":"D-P Fan","year":"2022","unstructured":"Fan, D.-P., Li, T., Lin, Z., Ji, G.-P., Zhang, D., Cheng, M.-M., Fu, H., Shen, J.: Re-thinking co-salient object detection. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4339\u20134354 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3060412","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"1462_CR13","doi-asserted-by":"publisher","first-page":"3766","DOI":"10.1109\/TIP.2013.2260166","volume":"22","author":"H Fu","year":"2013","unstructured":"Fu, H., Cao, X., Tu, Z.: Cluster-based co-saliency detection. IEEE Trans. Image Process. 22(10), 3766\u20133778 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"1462_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103973","volume":"101","author":"X Gong","year":"2020","unstructured":"Gong, X., Liu, X., Li, Y., Li, H.: A novel co-attention computation block for deep learning based image co-segmentation. Image Vis. Comput. 101, 103973 (2020). https:\/\/doi.org\/10.1016\/j.imavis.2020.103973","journal-title":"Image Vis. Comput."},{"key":"1462_CR15","doi-asserted-by":"crossref","unstructured":"Guo, R., Niu, D., Qu, L., Li, Z.: Sotr: Segmenting objects with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7157\u20137166 (2021)","DOI":"10.1109\/ICCV48922.2021.00707"},{"key":"1462_CR16","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1109\/TCSVT.2017.2706264","volume":"28","author":"J Han","year":"2017","unstructured":"Han, J., Cheng, G., Li, Z., Zhang, D.: A unified metric learning-based framework for co-saliency detection. IEEE Trans. Circuits Syst. Video Technol. 28, 2473\u20132483 (2017)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1462_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3152247","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., Xu, Y., Yang, Z., Zhang, Y., Tao, D.: A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3152247","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"1462_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-022-01312-y","volume":"33","author":"H He","year":"2022","unstructured":"He, H., Wang, J., Li, X., Hong, M., Huang, S., Zhou, T.: EAF-Net: an enhancement and aggregation-feedback network for RGB-T salient object detection. Mach. Vis. Appl. 33(4), 1\u201315 (2022)","journal-title":"Mach. Vis. Appl."},{"key":"1462_CR19","doi-asserted-by":"crossref","unstructured":"Hsu, K.J., Tsai, C.C., Lin, Y., Qian, X., Chuang, Y.: Unsupervised CNN-based co-saliency detection with graphical optimization (2018)","DOI":"10.1007\/978-3-030-01228-1_30"},{"key":"1462_CR20","doi-asserted-by":"crossref","unstructured":"Jiang, B., Jiang, X., Tang, J., Luo, B., Huang, S.: Multiple graph convolutional networks for co-saliency detection. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 332\u2013337 (2019)","DOI":"10.1109\/ICME.2019.00065"},{"key":"1462_CR21","first-page":"18749","volume-title":"Advances in Neural Information Processing Systems","author":"W-D Jin","year":"2020","unstructured":"Jin, W.-D., Xu, J., Cheng, M.-M., Zhang, Y., Guo, W.: Icnet: Intra-saliency correlation network for co-saliency detection. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 18749\u201318759. Curran Associates Inc., Red Hook (2020)"},{"key":"1462_CR22","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-319-10599-4_17","volume-title":"Computer Vision\u2013ECCV 2014","author":"A Joulin","year":"2014","unstructured":"Joulin, A., Tang, K., Fei-Fei, L.: Efficient image and video co-localization with Frank\u2013Wolfe algorithm. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision\u2013ECCV 2014, pp. 253\u2013268. Springer International Publishing, Cham (2014)"},{"key":"1462_CR23","doi-asserted-by":"crossref","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. (2021)","DOI":"10.1145\/3505244"},{"key":"1462_CR24","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)"},{"key":"1462_CR25","unstructured":"Kolesnikov, A., Dosovitskiy, A., Weissenborn, D., Heigold, G., Uszkoreit, J., Beyer, L., Minderer, M., Dehghani, M., Houlsby, N., Gelly, S., Unterthiner, T., Zhai, X.: An image is worth $$16\\times 16$$ words: transformers for image recognition at scale (2021)"},{"key":"1462_CR26","doi-asserted-by":"publisher","first-page":"84989","DOI":"10.1109\/ACCESS.2022.3197752","volume":"10","author":"J Korczakowski","year":"2022","unstructured":"Korczakowski, J., Sarwas, G., Czajewski, W.: CoU2Net and CoLDF: two novel methods built on basis of double-branch co-salient object detection framework. IEEE Access 10, 84989\u201385001 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3197752","journal-title":"IEEE Access"},{"key":"1462_CR27","doi-asserted-by":"crossref","unstructured":"Le, M. O., Wenbin, Z., Liquan, S., Lina, L., Zhi: Co-saliency detection based on hierarchical segmentation. IEEE Signal Process. Lett. 21(1), 88\u201392 (2014)","DOI":"10.1109\/LSP.2013.2292873"},{"key":"1462_CR28","doi-asserted-by":"publisher","unstructured":"Li, B., Sun, Z., Li, Q., Wu, Y., Anqi, H.: Group-wise deep object co-segmentation with co-attention recurrent neural network. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8518\u20138527 (2019a). https:\/\/doi.org\/10.1109\/ICCV.2019.00861","DOI":"10.1109\/ICCV.2019.00861"},{"key":"1462_CR29","doi-asserted-by":"crossref","unstructured":"Li, B., Sun, Z., Tang, L., Sun, Y., Shi, J.: Detecting robust co-saliency with recurrent co-attention neural network. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pp. 818\u2013825. International Joint Conferences on Artificial Intelligence Organization, 7 (2019b)","DOI":"10.24963\/ijcai.2019\/115"},{"key":"1462_CR30","doi-asserted-by":"crossref","unstructured":"Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., Tu, Z.: Pose recognition with cascade transformers. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1944\u20131953 (2021)","DOI":"10.1109\/CVPR46437.2021.00198"},{"issue":"12","key":"1462_CR31","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TIP.2011.2156803","volume":"20","author":"H Li","year":"2011","unstructured":"Li, H., Ngan, K.N.: A co-saliency model of image pairs. IEEE Trans. Image Process. 20(12), 3365\u20133375 (2011)","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"1462_CR32","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1109\/LSP.2014.2364896","volume":"22","author":"Y Li","year":"2015","unstructured":"Li, Y., Fu, K., Liu, Z., Yang, J.: Efficient saliency-model-guided visual co-saliency detection. IEEE Signal Process. Lett. 22(5), 588\u2013592 (2015)","journal-title":"IEEE Signal Process. Lett."},{"key":"1462_CR33","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.neucom.2019.12.109","volume":"389","author":"T Li","year":"2020","unstructured":"Li, T., Song, H., Zhang, K., Liu, Q.: Recurrent reverse attention guided residual learning for saliency object detection. Neurocomputing 389, 170\u2013178 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.12.109","journal-title":"Neurocomputing"},{"key":"1462_CR34","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TMM.2021.3054526","volume":"24","author":"T Li","year":"2022","unstructured":"Li, T., Zhang, K., Shen, S., Liu, B., Liu, Q., Li, Z.: Image co-saliency detection and instance co-segmentation using attention graph clustering based graph convolutional network. IEEE Trans. Multimed. 24, 492\u2013505 (2022). https:\/\/doi.org\/10.1109\/TMM.2021.3054526","journal-title":"IEEE Trans. Multimed."},{"key":"1462_CR35","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., Hays, J., Zitnick, C. L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1462_CR36","doi-asserted-by":"crossref","unstructured":"Lin, K., Wang, L., Liu, Z.: End-to-end human pose and mesh reconstruction with transformers. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1954\u20131963 (2021)","DOI":"10.1109\/CVPR46437.2021.00199"},{"key":"1462_CR37","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9992\u201310002 (2021b). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1462_CR38","doi-asserted-by":"crossref","unstructured":"Liu, N., Zhang, N., Wan, K., Shao, L., Han, J.: Visual saliency transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4722\u20134732 (2021a)","DOI":"10.1109\/ICCV48922.2021.00468"},{"key":"1462_CR39","doi-asserted-by":"publisher","first-page":"6438","DOI":"10.1109\/TIP.2020.2988568","volume":"99","author":"N Liu","year":"2020","unstructured":"Liu, N., Han, J., Yang, M.H.: PiCANet: pixel-wise contextual attention learning for accurate saliency detection. IEEE Trans. Image Process. 99, 6438\u20136451 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1462_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103425","volume":"126","author":"J Liu","year":"2022","unstructured":"Liu, J., Yuan, M., Huang, X., Su, Y., Yang, X.: Diponet: Dual-information progressive optimization network for salient object detection. Digit. Signal Process. 126, 103425 (2022). https:\/\/doi.org\/10.1016\/j.dsp.2022.103425","journal-title":"Digit. Signal Process."},{"issue":"5","key":"1462_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-022-01325-7","volume":"33","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Dong, H., Zhang, Z., Xiao, Y.: Global-guided cross-reference network for co-salient object detection. Mach. Vis. Appl. 33(5), 1\u201313 (2022)","journal-title":"Mach. Vis. Appl."},{"issue":"7","key":"1462_CR42","doi-asserted-by":"publisher","first-page":"4486","DOI":"10.1109\/TCSVT.2021.3127149","volume":"32","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Tan, Y., He, Q., Xiao, Y.: SwinNet: Swin transformer drives edge-aware RGB-D and RGB-T salient object detection. IEEE Trans. Circuits Syst. Video Technol. 32(7), 4486\u20134497 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3127149","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"1462_CR43","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"issue":"10","key":"1462_CR44","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1109\/TPAMI.2015.2392783","volume":"37","author":"Y Luo","year":"2015","unstructured":"Luo, Y., Jiang, M., Wong, Y., Zhao, Q.: Multi-camera saliency. IEEE Trans. Pattern Anal. Mach. Intell. 37(10), 2057\u20132070 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1462_CR45","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Zhang, L., Lu, H.: Multi-scale interactive network for salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"1462_CR46","doi-asserted-by":"crossref","unstructured":"Prakash, A., Chitta, K., Geiger, A.: Multi-modal fusion transformer for end-to-end autonomous driving. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7073\u20137083 (2021)","DOI":"10.1109\/CVPR46437.2021.00700"},{"key":"1462_CR47","doi-asserted-by":"publisher","unstructured":"Ren, G., Dai, T., Stathaki, T.: Adaptive intra-group aggregation for co-saliency detection. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2520\u20132524 (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746218","DOI":"10.1109\/ICASSP43922.2022.9746218"},{"key":"1462_CR48","doi-asserted-by":"crossref","unstructured":"Shen, T., Lin, G., Liu, L., Shen, C., Reid, I. D.: Weakly supervised semantic segmentation based on web image co-segmentation. Comput. Vis. Pattern Recognit. (2017)","DOI":"10.5244\/C.31.17"},{"key":"1462_CR49","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)"},{"issue":"3","key":"1462_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01065-6","volume":"31","author":"G Srivastava","year":"2020","unstructured":"Srivastava, G., Srivastava, R.: User-interactive salient object detection using YOLOv2, lazy snapping, and gabor filters. Mach. Vis. Appl. 31(3), 1\u20137 (2020)","journal-title":"Mach. Vis. Appl."},{"key":"1462_CR51","doi-asserted-by":"crossref","unstructured":"Su, Y., Deng, J., Sun, R., Lin, G., Wu, Q.: A unified transformer framework for group-based segmentation: co-segmentation, co-saliency detection and video salient object detection. (2022) arXiv:2203.04708","DOI":"10.1109\/TMM.2023.3264883"},{"key":"1462_CR52","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.: A unified transformer framework for group-based segmentation: co-segmentation, co-saliency detection and video salient object detection. IEEE Trans. Multimed. (2023). https:\/\/doi.org\/10.1109\/TMM.2023.3264883","journal-title":"IEEE Trans. Multimed."},{"key":"1462_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109356","volume":"252","author":"Z Tan","year":"2022","unstructured":"Tan, Z., Gu, X.: Co-saliency detection with intra-group two-stage group semantics propagation and inter-group contrastive learning. Knowl. Based Syst. 252, 109356 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109356","journal-title":"Knowl. Based Syst."},{"issue":"8","key":"1462_CR54","doi-asserted-by":"publisher","first-page":"5453","DOI":"10.1109\/TCSVT.2022.3150923","volume":"32","author":"L Tang","year":"2022","unstructured":"Tang, L., Li, B., Kuang, S., Song, M., Ding, S.: Re-thinking the relations in co-saliency detection. IEEE Trans. Circuits Syst. Video Technol. 32(8), 5453\u20135466 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2022.3150923","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1462_CR55","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jegou, H.: Training data-efficient image transformers and distillation through attention. In: Meila, M., Zhang, T. (eds) Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pp. 10347\u201310357. PMLR (2021)"},{"key":"1462_CR56","volume-title":"Advances in Neural Information Processing Systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.U., Polosukhin, I.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates Inc., Red Hook (2017)"},{"key":"1462_CR57","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1462_CR58","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Wang, Y., Feng, M., Xiang, R.: Learning to detect salient objects with image-level supervision. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.404"},{"key":"1462_CR59","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., Xia, H.: End-to-end video instance segmentation with transformers. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8737\u20138746 (2021b)","DOI":"10.1109\/CVPR46437.2021.00863"},{"key":"1462_CR60","doi-asserted-by":"crossref","unstructured":"Wang, C., Zha, Z.-J., Liu, D., Xie, H.: Robust deep co-saliency detection with group semantic. In: Proceedings of the AAAI Conference on Artificial Intelligence vol. 33, pp. 8917\u20138924 (2019)","DOI":"10.1609\/aaai.v33i01.33018917"},{"key":"1462_CR61","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhou, W., Wang, J., Li, H.: Transformer meets tracker: exploiting temporal context for robust visual tracking. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1571\u20131580 (2021a)","DOI":"10.1109\/CVPR46437.2021.00162"},{"key":"1462_CR62","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q. (2019) Stacked cross refinement network for edge-aware salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7264\u20137273 (2019)","DOI":"10.1109\/ICCV.2019.00736"},{"issue":"1","key":"1462_CR63","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"1462_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01172-y","volume":"32","author":"X Yan","year":"2021","unstructured":"Yan, X., Chen, Z., Wu, Q., Lu, M., Sun, L.: 3MNet: multi-task, multi-level and multi-channel feature aggregation network for salient object detection. Mach. Vis. Appl. 32(2), 1\u201313 (2021)","journal-title":"Mach. Vis. Appl."},{"issue":"6","key":"1462_CR65","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1109\/TMM.2011.2162399","volume":"13","author":"L Yang","year":"2011","unstructured":"Yang, L., Geng, B., Cai, Y., Hanjalic, A., Hua, X.-S.: Object retrieval using visual query context. IEEE Trans. Multimed. 13(6), 1295\u20131307 (2011)","journal-title":"IEEE Trans. Multimed."},{"issue":"7","key":"1462_CR66","doi-asserted-by":"publisher","first-page":"3196","DOI":"10.1109\/TIP.2017.2694222","volume":"26","author":"X Yao","year":"2017","unstructured":"Yao, X., Han, J., Zhang, D., Nie, F.: Revisiting co-saliency detection: a novel approach based on two-stage multi-view spectral rotation co-clustering. IEEE Trans. Image Process. 26(7), 3196\u20133209 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"1462_CR67","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.1109\/LSP.2015.2458434","volume":"22","author":"L Ye","year":"2015","unstructured":"Ye, L., Liu, Z., Li, J., Zhao, W.L., Shen, L.: Co-saliency detection via co-salient object discovery and recovery. IEEE Signal Process. Lett. 22(11), 2073\u20132077 (2015)","journal-title":"IEEE Signal Process. Lett."},{"key":"1462_CR68","doi-asserted-by":"publisher","unstructured":"Yu, S., Xiao, J., Zhang, B., Lim, E. G.: Democracy does matter: comprehensive feature mining for co-salient object detection. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 969\u2013978 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00105","DOI":"10.1109\/CVPR52688.2022.00105"},{"key":"1462_CR69","doi-asserted-by":"crossref","unstructured":"Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z.-H., Tay, F. E., Feng, J., Yan, S.: Tokens-to-token vit: training vision transformers from scratch on imagenet. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 558\u2013567 (October 2021)","DOI":"10.1109\/ICCV48922.2021.00060"},{"issue":"7","key":"1462_CR70","first-page":"2398","volume":"31","author":"Z-J Zha","year":"2020","unstructured":"Zha, Z.-J., Wang, C., Liu, D., Xie, H., Zhang, Y.: Robust deep co-saliency detection with group semantic and pyramid attention. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2398\u20132408 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1462_CR71","doi-asserted-by":"crossref","unstructured":"Zhang, D., Han, J., Li, C., Wang, J., Li, X.: Detection of co-salient objects by looking deep and wide. Int. J. Comput. Vis. (2016b)","DOI":"10.1109\/CVPR.2015.7298918"},{"key":"1462_CR72","doi-asserted-by":"crossref","unstructured":"Zhang, D., Han, J., Li, C., Wang, J.: Co-saliency detection via looking deep and wide. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2994\u20133002 (2015)","DOI":"10.1109\/CVPR.2015.7298918"},{"key":"1462_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, K., Li, T., Liu, B., Liu, Q.: Co-saliency detection via mask-guided fully convolutional networks with multi-scale label smoothing. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019a)","DOI":"10.1109\/CVPR.2019.00321"},{"key":"1462_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, K., Li, T., Liu, B., Liu, Q.: Co-saliency detection via mask-guided fully convolutional networks with multi-scale label smoothing. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3090\u20133099 (2019b)","DOI":"10.1109\/CVPR.2019.00321"},{"key":"1462_CR75","doi-asserted-by":"crossref","unstructured":"Zhang, K., Li, T., Shen, S., Liu, B., Chen, J., Liu, Q.: Adaptive graph convolutional network with attention graph clustering for co-saliency detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9047\u20139056 (2020a)","DOI":"10.1109\/CVPR42600.2020.00907"},{"key":"1462_CR76","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, T., Qi, J., Lu, H., Gang, W.: Progressive attention guided recurrent network for salient object detection. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00081"},{"issue":"6","key":"1462_CR77","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1109\/TNNLS.2015.2495161","volume":"27","author":"D Zhang","year":"2016","unstructured":"Zhang, D., Han, J., Han, J., Ling, S.: Cosaliency detection based on intrasaliency prior transfer and deep intersaliency mining. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1163\u20131176 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"1462_CR78","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1109\/TPAMI.2016.2567393","volume":"39","author":"D Zhang","year":"2017","unstructured":"Zhang, D., Meng, D., Han, J.: Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 865\u2013878 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1462_CR79","first-page":"6959","volume-title":"Advances in Neural Information Processing Systems","author":"Q Zhang","year":"2020","unstructured":"Zhang, Q., Cong, R., Hou, J., Li, C., Zhao, Y.: Coadnet: collaborative aggregation-and-distribution networks for co-salient object detection. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 6959\u20136970. Curran Associates Inc., Red Hook (2020)"},{"key":"1462_CR80","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/978-3-030-58610-2_27","volume-title":"Computer Vision\u2014ECCV 2020","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Jin, W., Xu, J., Cheng, M.-M.: Gradient-induced co-saliency detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision\u2014ECCV 2020, pp. 455\u2013472. Springer International Publishing, Cham (2020)"},{"key":"1462_CR81","doi-asserted-by":"publisher","unstructured":"Zhang, K., Wu, Y., Dong, M., Liu, B., Liu, D., Liu, Q.: Deep object co-segmentation and co-saliency detection via high-order spatial-semantic network modulation. IEEE Trans. Multimed. 7, 1\u201314 (2022). https:\/\/doi.org\/10.1109\/TMM.2022.3198848","DOI":"10.1109\/TMM.2022.3198848"},{"key":"1462_CR82","doi-asserted-by":"crossref","unstructured":"Zhao, J., Liu, J.-J., Fan, D.-P., Cao, Y., Yang, J., Cheng, M.-M.: EGNet: edge guidance network for salient object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8778\u20138787 (2019)","DOI":"10.1109\/ICCV.2019.00887"},{"key":"1462_CR83","doi-asserted-by":"crossref","unstructured":"Zhao, W., Zhang, J., Li, L., Barnes, N., Liu, N., Han, J.: Weakly supervised video salient object detection. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16821\u201316830 (2021)","DOI":"10.1109\/CVPR46437.2021.01655"},{"key":"1462_CR84","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H., Zhang, L.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"1462_CR85","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3264571","author":"P Zheng","year":"2023","unstructured":"Zheng, P., Fu, H., Fan, D.-P., Fan, Q., Qin, J., Tai, Y.-W., Tang, C.-K., Van Gool, L.: GCoNEt+: A stronger group collaborative co-salient object detector. IEEE Trans. Pattern Anal. Mach. Intell. (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3264571","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1462_CR86","doi-asserted-by":"crossref","unstructured":"Zhou, H., Xie, X., Lai, J.-H., Chen, Z., Yang, L.: Interactive two-stream decoder for accurate and fast saliency detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9138\u20139147 (2020)","DOI":"10.1109\/CVPR42600.2020.00916"},{"key":"1462_CR87","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Dai, J.: Deformable detr: deformable transformers for end-to-end object detection (2020)"},{"key":"1462_CR88","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3234586","author":"Z Zhu","year":"2023","unstructured":"Zhu, Z., Zhang, Z., Lin, Z., Sun, X., Cheng, M.-M.: Co-salient object detection with co-representation purification. IEEE Trans. Pattern Anal. Mach. Intell. (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3234586","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01462-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01462-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01462-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T13:38:42Z","timestamp":1730122722000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01462-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,20]]},"references-count":88,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["1462"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01462-7","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2023,9,20]]},"assertion":[{"value":"4 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"107"}}