{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:43:52Z","timestamp":1784097832559,"version":"3.55.0"},"reference-count":108,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51674255"],"award-info":[{"award-number":["51674255"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07368-1","type":"journal-article","created":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T09:03:16Z","timestamp":1653555796000},"page":"13387-13405","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Applications of graph convolutional networks in computer vision"],"prefix":"10.1007","volume":"34","author":[{"given":"Pingping","family":"Cao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeqi","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanping","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"issue":"11","key":"7368_CR1","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"7368_CR2","unstructured":"Zhou FY, Jin LP, Dong J (2017) Review of convolutional neural network. Chin J Comput"},{"issue":"1","key":"7368_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","volume":"6","author":"S Zhang","year":"2019","unstructured":"Zhang S, Tong H, Xu J, Maciejewski R (2019) Graph convolutional networks: a comprehensive review. Comput Social Netw 6(1):1\u201323","journal-title":"Comput Social Netw"},{"key":"7368_CR4","unstructured":"Gong Y, Jia Y, Leung T, Toshev A, Ioffe S (2013) Deep convolutional ranking for multilabel image annotation. arXiv preprint arXiv:1312.4894"},{"key":"7368_CR5","doi-asserted-by":"crossref","unstructured":"Wang J, Yang Y, Mao J, Huang Z, Huang C, Xu W (2016) Cnn-rnn: a unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285\u20132294","DOI":"10.1109\/CVPR.2016.251"},{"key":"7368_CR6","doi-asserted-by":"crossref","unstructured":"Zhu F, Li H, Ouyang W, Yu N, Wang X (2017) Learning spatial regularization with image-level supervisions for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5513\u20135522","DOI":"10.1109\/CVPR.2017.219"},{"key":"7368_CR7","doi-asserted-by":"crossref","unstructured":"Wang Z, Chen T, Li G, Xu R, Lin L (2017) Multi-label image recognition by recurrently discovering attentional regions. In: Proceedings of the IEEE international conference on computer vision, pp 464\u2013472","DOI":"10.1109\/ICCV.2017.58"},{"key":"7368_CR8","doi-asserted-by":"crossref","unstructured":"Li Q, Qiao M, Bian W, Tao D (2016) Conditional graphical lasso for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2977\u20132986","DOI":"10.1109\/CVPR.2016.325"},{"key":"7368_CR9","unstructured":"Li X, Zhao F, Guo Y (2014) Multi-label image classification with a probabilistic label enhancement model. In: UAI 1:1\u201310"},{"key":"7368_CR10","doi-asserted-by":"crossref","unstructured":"Lee C-W, Fang W, Yeh C-K, Wang Y-CF (2018) Multi-label zero-shot learning with structured knowledge graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1576\u20131585","DOI":"10.1109\/CVPR.2018.00170"},{"key":"7368_CR11","doi-asserted-by":"crossref","unstructured":"Chen Z-M, Wei X-S, Wang P, Guo Y (2019) Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5177\u20135186","DOI":"10.1109\/CVPR.2019.00532"},{"key":"7368_CR12","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203"},{"key":"7368_CR13","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"7368_CR14","first-page":"5165","volume":"31","author":"M Zhang","year":"2018","unstructured":"Zhang M, Chen Y (2018) Link prediction based on graph neural networks. Adv Neural Inf Process Syst 31:5165\u20135175","journal-title":"Adv Neural Inf Process Syst"},{"key":"7368_CR15","doi-asserted-by":"crossref","unstructured":"Simonovsky M, Komodakis N (2017) Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3693\u20133702","DOI":"10.1109\/CVPR.2017.11"},{"key":"7368_CR16","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826"},{"key":"7368_CR17","unstructured":"Ying R, You J, Morris C, Ren X, Hamilton WL, Leskovec J (2018) Hierarchical graph representation learning with differentiable pooling. arXiv preprint arXiv:1806.08804"},{"key":"7368_CR18","doi-asserted-by":"crossref","unstructured":"Zhang M, Cui Z, Neumann M, Chen Y (2018) An end-to-end deep learning architecture for graph classification. In: Thirty-Second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"7368_CR19","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems, pp 1025\u20131035"},{"key":"7368_CR20","unstructured":"Pei H, Wei B, Chang KC-C, Lei Y, Yang B (2020) Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287"},{"key":"7368_CR21","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"7368_CR22","unstructured":"Xu B, Shen H, Cao Q, Qiu Y, Cheng X (2019) Graph wavelet neural network. arXiv preprint arXiv:1904.07785"},{"key":"7368_CR23","doi-asserted-by":"crossref","unstructured":"Wang H, Zhang F, Zhang M, Leskovec J, Zhao M, Li W, Wang Z (2019) Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 968\u2013977","DOI":"10.1145\/3292500.3330836"},{"issue":"3","key":"7368_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3312738","volume":"37","author":"H Wang","year":"2019","unstructured":"Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2019) Exploring high-order user preference on the knowledge graph for recommender systems. ACM Trans Inf Syst (TOIS) 37(3):1\u201326","journal-title":"ACM Trans Inf Syst (TOIS)"},{"key":"7368_CR25","doi-asserted-by":"crossref","unstructured":"Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The World Wide Web conference, pp. 417\u2013426","DOI":"10.1145\/3308558.3313488"},{"key":"7368_CR26","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"issue":"11","key":"7368_CR27","doi-asserted-by":"publisher","first-page":"4883","DOI":"10.1109\/TITS.2019.2950416","volume":"21","author":"Z Cui","year":"2019","unstructured":"Cui Z, Henrickson K, Ke R, Wang Y (2019) Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans Intell Transp Syst 21(11):4883\u20134894","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"7368_CR28","unstructured":"Zhang J, Shi X, Xie J, Ma H, King I, Yeung D-Y (2018) Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294"},{"key":"7368_CR29","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","DOI":"10.24963\/ijcai.2018\/505"},{"key":"7368_CR30","unstructured":"Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, G\u00f3mez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP (2015)Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292"},{"issue":"8","key":"7368_CR31","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30(8):595\u2013608","journal-title":"J Comput Aided Mol Des"},{"issue":"13","key":"7368_CR32","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1093\/bioinformatics\/bty294","volume":"34","author":"M Zitnik","year":"2018","unstructured":"Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13):457\u2013466","journal-title":"Bioinformatics"},{"key":"7368_CR33","unstructured":"Fout AM (2017) Protein interface prediction using graph convolutional networks. Ph.D. thesis, Colorado State University"},{"key":"7368_CR34","unstructured":"De\u00a0Cao N, Kipf T (2018) Molgan: an implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973"},{"key":"7368_CR35","unstructured":"You J, Liu B, Ying R, Pande V, Leskovec J (2018) Graph convolutional policy network for goal-directed molecular graph generation. arXiv preprint arXiv:1806.02473"},{"key":"7368_CR36","doi-asserted-by":"crossref","unstructured":"Liu X, Luo Z, Huang H (2018) Jointly multiple events extraction via attention-based graph information aggregation. arXiv preprint arXiv:1809.09078","DOI":"10.18653\/v1\/D18-1156"},{"key":"7368_CR37","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Grishman R (2018) Graph convolutional networks with argument-aware pooling for event detection. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12039"},{"key":"7368_CR38","doi-asserted-by":"crossref","unstructured":"Beck D, Haffari G, Cohn T (2018) Graph-to-sequence learning using gated graph neural networks. arXiv preprint arXiv:1806.09835","DOI":"10.18653\/v1\/P18-1026"},{"key":"7368_CR39","doi-asserted-by":"crossref","unstructured":"Peng H, Li J, He Y, Liu Y, Bao M, Wang L, Song Y, Yang Q (2018) Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In: Proceedings of the 2018 world wide web conference, pp 1063\u20131072","DOI":"10.1145\/3178876.3186005"},{"key":"7368_CR40","doi-asserted-by":"crossref","unstructured":"Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 7370\u20137377","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"7368_CR41","unstructured":"Song L, Wang Z, Yu M, Zhang Y, Florian R, Gildea D (2018) Exploring graph-structured passage representation for multi-hop reading comprehension with graph neural networks. arXiv preprint arXiv:1809.02040"},{"key":"7368_CR42","unstructured":"Gupta A, Narayan S, Khan S, Khan FS, Shao L, van\u00a0de Weijer J (2021) Generative multi-label zero-shot learning. arXiv preprint arXiv:2101.11606"},{"key":"7368_CR43","doi-asserted-by":"publisher","first-page":"104238","DOI":"10.1016\/j.imavis.2021.104238","volume":"113","author":"P Cao","year":"2021","unstructured":"Cao P, Chen P, Niu Q (2021) Multi-label image recognition with two-stream dynamic graph convolution networks. Image Vis Comput 113:104238","journal-title":"Image Vis Comput"},{"issue":"5","key":"7368_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2019) Dynamic graph cnn for learning on point clouds. Acm Trans Graph (tog) 38(5):1\u201312","journal-title":"Acm Trans Graph (tog)"},{"key":"7368_CR45","doi-asserted-by":"crossref","unstructured":"Michieli U, Borsato E, Rossi L, Zanuttigh P (2020) Gmnet: Graph matching network for large scale part semantic segmentation in the wild. In: European conference on computer vision, pp 397\u2013414. Springer","DOI":"10.1007\/978-3-030-58598-3_24"},{"key":"7368_CR46","doi-asserted-by":"crossref","unstructured":"Hu R, Rohrbach A, Darrell T, Saenko K (2019) Language-conditioned graph networks for relational reasoning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10294\u201310303","DOI":"10.1109\/ICCV.2019.01039"},{"key":"7368_CR47","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"key":"7368_CR48","unstructured":"Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng"},{"issue":"1","key":"7368_CR49","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7368_CR50","unstructured":"Chami I, Abu-El-Haija S, Perozzi B, R\u00e9 C, Murphy K (2020) Machine learning on graphs: a model and comprehensive taxonomy"},{"key":"7368_CR51","first-page":"3844","volume":"29","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844\u20133852","journal-title":"Adv Neural Inf Process Syst"},{"key":"7368_CR52","doi-asserted-by":"crossref","unstructured":"Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data 29:93\u201393","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"7368_CR53","doi-asserted-by":"crossref","unstructured":"Wang Y, He D, Li F, Long X, Zhou Z, Ma J, Wen S (2020) Multi-label classification with label graph superimposing. Proc AAAI Conf Artif Intell 34:12265\u201312272","DOI":"10.1609\/aaai.v34i07.6909"},{"key":"7368_CR54","doi-asserted-by":"crossref","unstructured":"Ye J, He J, Peng X, Wu W, Qiao Y (2020) Attention-driven dynamic graph convolutional network for multi-label image recognition. In: European conference on computer vision, pp 649\u2013665. Springer","DOI":"10.1007\/978-3-030-58589-1_39"},{"key":"7368_CR55","doi-asserted-by":"crossref","unstructured":"Akata Z, Reed S, Walter D, Lee H, Schiele B (2015) Evaluation of output embeddings for fine-grained image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2927\u20132936","DOI":"10.1109\/CVPR.2015.7298911"},{"key":"7368_CR56","doi-asserted-by":"crossref","unstructured":"Long Y, Liu L, Shao L, Shen F, Ding G, Han J (2017) From zero-shot learning to conventional supervised classification: Unseen visual data synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1627\u20131636","DOI":"10.1109\/CVPR.2017.653"},{"key":"7368_CR57","unstructured":"Romera-Paredes B, Torr P (2015) An embarrassingly simple approach to zero-shot learning. In: International conference on machine learning, pp 2152\u20132161. PMLR"},{"key":"7368_CR58","unstructured":"Frome A, Corrado G, Shlens J, Bengio S, Dean J, Ranzato M, Mikolov T (2013) Devise: a deep visual-semantic embedding model"},{"key":"7368_CR59","doi-asserted-by":"crossref","unstructured":"Li Y, Wang D, Hu H, Lin Y, Zhuang Y (2017) Zero-shot recognition using dual visual-semantic mapping paths. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3279\u20133287","DOI":"10.1109\/CVPR.2017.553"},{"key":"7368_CR60","doi-asserted-by":"crossref","unstructured":"Kampffmeyer M, Chen Y, Liang X, Wang H, Zhang Y, Xing EP (2019) Rethinking knowledge graph propagation for zero-shot learning. In: Proceedings of the IEEE\/cvf conference on computer vision and pattern recognition, pp 11487\u201311496","DOI":"10.1109\/CVPR.2019.01175"},{"key":"7368_CR61","unstructured":"Rong Y, Huang W, Xu T, Huang J (2019) Dropedge: towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903"},{"key":"7368_CR62","unstructured":"Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning, pp 1725\u20131735. PMLR"},{"key":"7368_CR63","doi-asserted-by":"crossref","unstructured":"Li G, Muller M, Thabet A, Ghanem B (2019) Deepgcns: Can gcns go as deep as cnns? In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9267\u20139276","DOI":"10.1109\/ICCV.2019.00936"},{"key":"7368_CR64","doi-asserted-by":"crossref","unstructured":"Caramalau R, Bhattarai B, Kim T-K (2021) Sequential graph convolutional network for active learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9583\u20139592","DOI":"10.1109\/CVPR46437.2021.00946"},{"key":"7368_CR65","doi-asserted-by":"crossref","unstructured":"Chai Y, Sun P, Ngiam J, Wang W, Caine B, Vasudevan V, Zhang X, Anguelov D (2021) To the point: Efficient 3d object detection in the range image with graph convolution kernels. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16000\u201316009","DOI":"10.1109\/CVPR46437.2021.01574"},{"key":"7368_CR66","doi-asserted-by":"crossref","unstructured":"Li H, Miao S, Feng R (2020) Dg-fpn: Learning dynamic feature fusion based on graph convolution network for object detection. In: 2020 IEEE international conference on multimedia and expo (ICME), pp 1\u20136. IEEE","DOI":"10.1109\/ICME46284.2020.9102838"},{"key":"7368_CR67","doi-asserted-by":"publisher","first-page":"4183","DOI":"10.1109\/TIP.2021.3070200","volume":"30","author":"M Xu","year":"2021","unstructured":"Xu M, Fu P, Liu B, Li J (2021) Multi-stream attention-aware graph convolution network for video salient object detection. IEEE Trans Image Process 30:4183\u20134197","journal-title":"IEEE Trans Image Process"},{"issue":"10","key":"7368_CR68","doi-asserted-by":"publisher","first-page":"2433","DOI":"10.1007\/s00371-020-01955-w","volume":"36","author":"W Zhang","year":"2020","unstructured":"Zhang W, Lin Z, Cheng J, Ma C, Deng X, Wang H (2020) Sta-gcn: two-stream graph convolutional network with spatial-temporal attention for hand gesture recognition. Vis Comput 36(10):2433\u20132444","journal-title":"Vis Comput"},{"key":"7368_CR69","doi-asserted-by":"crossref","unstructured":"Liu Y, Wang R, Shan S, Chen X (2018) Structure inference net: Object detection using scene-level context and instance-level relationships. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6985\u20136994","DOI":"10.1109\/CVPR.2018.00730"},{"key":"7368_CR70","unstructured":"Du X, Shi X, Huang R (2019) Repgn: Object detection with relational proposal graph network. arXiv preprint arXiv:1904.08959"},{"key":"7368_CR71","doi-asserted-by":"crossref","unstructured":"Landrieu L, Simonovsky M (2018) Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4558\u20134567","DOI":"10.1109\/CVPR.2018.00479"},{"key":"7368_CR72","doi-asserted-by":"crossref","unstructured":"Qi X, Liao R, Jia J, Fidler S, Urtasun R (2017) 3d graph neural networks for rgbd semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 5199\u20135208","DOI":"10.1109\/ICCV.2017.556"},{"key":"7368_CR73","doi-asserted-by":"crossref","unstructured":"Te G, Hu W, Zheng A, Guo Z (2018) Rgcnn: Regularized graph cnn for point cloud segmentation. In: Proceedings of the 26th ACM international conference on multimedia, pp 746\u2013754","DOI":"10.1145\/3240508.3240621"},{"key":"7368_CR74","unstructured":"Yang L, Zhuang J, Fu H, Zhou K, Zheng Y (2020) Sketchgcn: Semantic sketch segmentation with graph convolutional networks. arXiv e-prints, 2003"},{"key":"7368_CR75","doi-asserted-by":"crossref","unstructured":"Lu Y, Chen Y, Zhao D, Chen J (2019) Graph-fcn for image semantic segmentation. In: International symposium on neural networks, pp 97\u2013105. Springer","DOI":"10.1007\/978-3-030-22796-8_11"},{"key":"7368_CR76","doi-asserted-by":"crossref","unstructured":"Ke L, Tai Y-W, Tang C-K (2021) Deep occlusion-aware instance segmentation with overlapping bilayers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4019\u20134028","DOI":"10.1109\/CVPR46437.2021.00401"},{"key":"7368_CR77","doi-asserted-by":"crossref","unstructured":"Ling H, Gao J, Kar A, Chen W, Fidler S (2019) Fast interactive object annotation with curve-gcn. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5257\u20135266","DOI":"10.1109\/CVPR.2019.00540"},{"key":"7368_CR78","doi-asserted-by":"crossref","unstructured":"Dong Z, Zhang R, Shao X (2019) Automatic annotation and segmentation of object instances with deep active curve network, vol 7, pp 147501\u2013147512. IEEE","DOI":"10.1109\/ACCESS.2019.2946650"},{"key":"7368_CR79","doi-asserted-by":"crossref","unstructured":"Kim N, Kang B, Cho Y (2021) Split gcn: Effective interactive annotation for segmentation of disconnected instance","DOI":"10.1109\/TPAMI.2022.3229091"},{"key":"7368_CR80","doi-asserted-by":"crossref","unstructured":"Wang N, Zhang Y, Li Z, Fu Y, Liu W, Jiang Y-G (2018) Pixel2mesh: Generating 3d mesh models from single rgb images. In: Proceedings of the European conference on computer vision (ECCV), pp 52\u201367","DOI":"10.1007\/978-3-030-01252-6_4"},{"key":"7368_CR81","doi-asserted-by":"crossref","unstructured":"Guo D, Shao Y, Cui Y, Wang Z, Zhang L, Shen C (2021) Graph attention tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9543\u20139552","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"7368_CR82","doi-asserted-by":"crossref","unstructured":"Li J, Gao X, Jiang T (2020) Graph networks for multiple object tracking. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 719\u2013728","DOI":"10.1109\/WACV45572.2020.9093347"},{"key":"7368_CR83","doi-asserted-by":"crossref","unstructured":"Bras\u00f3 G, Leal-Taix\u00e9 L (2020) Learning a neural solver for multiple object tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6247\u20136257","DOI":"10.1109\/CVPR42600.2020.00628"},{"key":"7368_CR84","unstructured":"Jiang X, Li P, Li Y, Zhen X (2019) Graph neural based end-to-end data association framework for online multiple-object tracking. arXiv preprint arXiv:1907.05315"},{"key":"7368_CR85","doi-asserted-by":"crossref","unstructured":"Shi Y, Zheng Y, Guo K, Zhu L, Qu Y (2018) Intrinsic or extrinsic evaluation: an overview of word embedding evaluation. In: 2018 IEEE international conference on data mining workshops (ICDMW), pp 1255\u20131262. IEEE","DOI":"10.1109\/ICDMW.2018.00179"},{"key":"7368_CR86","first-page":"2017","volume":"28","author":"M Jaderberg","year":"2015","unstructured":"Jaderberg M, Simonyan K, Zisserman A et al (2015) Spatial transformer networks. Adv Neural Inf Process Syst 28:2017\u20132025","journal-title":"Adv Neural Inf Process Syst"},{"key":"7368_CR87","first-page":"794","volume":"32","author":"G Yang","year":"2019","unstructured":"Yang G, Ramanan D (2019) Volumetric correspondence networks for optical flow. Adv Neural Inf Process Syst 32:794\u2013805","journal-title":"Adv Neural Inf Process Syst"},{"key":"7368_CR88","doi-asserted-by":"crossref","unstructured":"Kang K, Ouyang W, Li H, Wang X (2016) Object detection from video tubelets with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 817\u2013825","DOI":"10.1109\/CVPR.2016.95"},{"issue":"10","key":"7368_CR89","doi-asserted-by":"publisher","first-page":"2896","DOI":"10.1109\/TCSVT.2017.2736553","volume":"28","author":"K Kang","year":"2017","unstructured":"Kang K, Li H, Yan J, Zeng X, Yang B, Xiao T, Zhang C, Wang Z, Wang R, Wang X et al (2017) T-cnn: Tubelets with convolutional neural networks for object detection from videos. IEEE Trans Circuits Syst Video Technol 28(10):2896\u20132907","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7368_CR90","doi-asserted-by":"crossref","unstructured":"Li Y, Huang C, Loy CC, Tang X (2016) Human attribute recognition by deep hierarchical contexts. In: European conference on computer vision, pp 684\u2013700. Springer","DOI":"10.1007\/978-3-319-46466-4_41"},{"key":"7368_CR91","unstructured":"Ge Z, Mahapatra D, Sedai S, Garnavi R, Chakravorty R (2018) Chest x-rays classification: a multi-label and fine-grained problem. arXiv preprint arXiv:1807.07247"},{"key":"7368_CR92","doi-asserted-by":"crossref","unstructured":"Jain H, Prabhu Y, Varma M (2016) Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 935\u2013944","DOI":"10.1145\/2939672.2939756"},{"key":"7368_CR93","doi-asserted-by":"crossref","unstructured":"Yang X, Li Y, Luo J (2015) Pinterest board recommendation for twitter users. In: Proceedings of the 23rd ACM international conference on multimedia, pp 963\u2013966","DOI":"10.1145\/2733373.2806375"},{"key":"7368_CR94","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","DOI":"10.1109\/CVPR.2016.90"},{"key":"7368_CR95","doi-asserted-by":"crossref","unstructured":"Liu L, Guo S, Huang W, Scott MR (2019) Decoupling category-wise independence and relevance with self-attention for multi-label image classification. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1682\u20131686. IEEE","DOI":"10.1109\/ICASSP.2019.8683665"},{"key":"7368_CR96","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"7368_CR97","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91\u201399","journal-title":"Adv Neural Inf Process Syst"},{"key":"7368_CR98","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"7368_CR99","unstructured":"Ma L, Kan X, Xiao Q, Liu W, Sun P (2017) Yes-net: an effective detector based on global information. arXiv preprint arXiv:1706.09180"},{"key":"7368_CR100","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"7368_CR101","unstructured":"O\u00a0Pinheiro PO, Collobert R, Doll\u00e1r P (2015) Learning to segment object candidates. Adv Neural Inf Process Syst 28"},{"key":"7368_CR102","doi-asserted-by":"crossref","unstructured":"Pinheiro PO, Lin T-Y, Collobert R, Doll\u00e1r P (2016) Learning to refine object segments. In: European conference on computer vision, pp 75\u201391. Springer","DOI":"10.1007\/978-3-319-46448-0_5"},{"key":"7368_CR103","doi-asserted-by":"crossref","unstructured":"Castrejon L, Kundu K, Urtasun R, Fidler S (2017) Annotating object instances with a polygon-rnn. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5230\u20135238","DOI":"10.1109\/CVPR.2017.477"},{"key":"7368_CR104","doi-asserted-by":"crossref","unstructured":"Chen J, Sheng H, Zhang Y, Xiong Z (2017) Enhancing detection model for multiple hypothesis tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 18\u201327","DOI":"10.1109\/CVPRW.2017.266"},{"issue":"11","key":"7368_CR105","doi-asserted-by":"publisher","first-page":"3269","DOI":"10.1109\/TCSVT.2018.2882192","volume":"29","author":"H Sheng","year":"2018","unstructured":"Sheng H, Zhang Y, Chen J, Xiong Z, Zhang J (2018) Heterogeneous association graph fusion for target association in multiple object tracking. IEEE Trans Circuits Syst Video Technol 29(11):3269\u20133280","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7368_CR106","doi-asserted-by":"crossref","unstructured":"Sanchez-Matilla R, Poiesi F, Cavallaro A (2016) Online multi-target tracking with strong and weak detections. In: european conference on computer vision, pp 84\u201399. Springer","DOI":"10.1007\/978-3-319-48881-3_7"},{"issue":"9","key":"7368_CR107","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1109\/TMM.2019.2902480","volume":"21","author":"Z Fu","year":"2019","unstructured":"Fu Z, Angelini F, Chambers J, Naqvi SM (2019) Multi-level cooperative fusion of gm-phd filters for online multiple human tracking. IEEE Trans Multimedia 21(9):2277\u20132291","journal-title":"IEEE Trans Multimedia"},{"key":"7368_CR108","doi-asserted-by":"crossref","unstructured":"Kim C, Li F, Rehg JM (2018) Multi-object tracking with neural gating using bilinear lstm. In: Proceedings of the European conference on computer vision (ECCV), pp 200\u2013215","DOI":"10.1007\/978-3-030-01237-3_13"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07368-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07368-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07368-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T08:48:24Z","timestamp":1675673304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07368-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,26]]},"references-count":108,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7368"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07368-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,26]]},"assertion":[{"value":"16 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}