{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T09:32:14Z","timestamp":1750930334200,"version":"3.28.0"},"reference-count":52,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9534302","type":"proceedings-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T20:40:52Z","timestamp":1632256852000},"page":"1-10","source":"Crossref","is-referenced-by-count":7,"title":["Fast Interactive Video Object Segmentation with Graph Neural Networks"],"prefix":"10.1109","author":[{"given":"Viktor","family":"Varga","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andras","family":"Lorincz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Interactive video object segmentation using global and local transfer modules","author":"heo","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00539"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"grabcut&#x201D; interactive foreground extraction using iterated graph cuts","volume":"23","author":"rother","year":"2004","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"ref32","article-title":"Learning video instance segmentation with recurrent graph neural networks","author":"johnander","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00933"},{"key":"ref30","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"wang","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459293"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.127"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.273"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_36"},{"journal-title":"A unified model for semi-supervised and interactive video object segmentation using space-time memory networks","year":"0","author":"oh","key":"ref28"},{"key":"ref27","article-title":"Unsupervised video object segmentation for deep reinforcement learning","author":"goel","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref29","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref2","article-title":"The 2017 davis challenge on video object segmentation","author":"pont-tuset","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3391743"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299114"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24947-6_46"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.82"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00932"},{"key":"ref23","article-title":"Lucid data dreaming for object tracking","author":"khoreva","year":"2017","journal-title":"The DAVIS Challenge on Video Object Segmentation"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2662005"},{"journal-title":"A kernel-based approach for video object segmentation","year":"0","author":"seong","key":"ref25"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1262177"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.60"},{"key":"ref52","article-title":"Use of watersheds in contour detection","author":"beucher","year":"1979","journal-title":"Proceedings of the International Workshop on Image Processing CCETT"},{"key":"ref10","article-title":"Graph neural networks: A review of methods and applications","author":"zhou","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01038"},{"key":"ref12","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref13","article-title":"Graph attention networks","author":"velickovic","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref14","article-title":"Residual gated graph convnets","author":"bresson","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00478"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00628"},{"key":"ref17","first-page":"399","article-title":"Videos as space-time region graphs","author":"wang","year":"2018","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref19","first-page":"656","article-title":"Supervoxel-consistent foreground propagation in video","author":"jain","year":"2014","journal-title":"European Conference on Computer Vision"},{"key":"ref4","article-title":"The 2018 davis challenge on video object segmentation","author":"caelles","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref3","article-title":"Youtube-vos: A large-scale video object segmentation benchmark","author":"xu","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref6","first-page":"1760","article-title":"Seednet: Automatic seed generation with deep reinforcement learning for robust interactive segmentation","author":"song","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00096"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"ref7","first-page":"3235","article-title":"Video segmentation with just a few strokes","author":"nagaraja","year":"2015","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/34.969114"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"journal-title":"Similarity learning for dense label transfer","year":"2018","author":"najafi","key":"ref48"},{"key":"ref47","first-page":"6","article-title":"Interactive video object segmentation using sparsee-to-dense networks","volume":"2","author":"heo","year":"2019","journal-title":"CVPR Workshops"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref41","article-title":"Slic superpixels","author":"achanta","year":"2010","journal-title":"Tech Rep"},{"key":"ref44","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Int Conference on Machine Learning"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09534302.pdf?arnumber=9534302","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:45:51Z","timestamp":1652197551000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9534302\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9534302","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}