{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:55:49Z","timestamp":1769637349089,"version":"3.49.0"},"reference-count":76,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region China","award":["CityU 11200314"],"award-info":[{"award-number":["CityU 11200314"]}]},{"name":"Research Grants Council of the Hong Kong Special Administrative Region China","award":["CityU 11212518"],"award-info":[{"award-number":["CityU 11212518"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/tpami.2019.2929034","type":"journal-article","created":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T20:54:33Z","timestamp":1563915273000},"page":"1-1","source":"Crossref","is-referenced-by-count":32,"title":["Visual Tracking via Dynamic Memory Networks"],"prefix":"10.1109","author":[{"given":"Tianyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antoni B.","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref73","first-page":"472","article-title":"Beyond correlation filters: Learning continuous convolution operators for visual tracking","author":"danelljan","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.354"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.108"},{"key":"ref70","article-title":"Convolutional features for correlation filter based visual tracking","author":"danelljan","year":"2016","journal-title":"Proc IEEE Int Conf Comput Vis Workshops"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2806280"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00934"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.733"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639344"},{"key":"ref33","first-page":"2440","article-title":"End-to-end memory networks","author":"sukhbaatar","year":"2015","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref32","article-title":"Memory networks","author":"weston","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref31","article-title":"Neural turing machines","author":"graves","year":"2014","journal-title":"arXiv 1410 5401"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2669880"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"ref36","article-title":"MAVOT: Memory-augmented video object tracking","author":"liu","year":"2017","journal-title":"arXiv 1711 09414"},{"key":"ref35","article-title":"One-shot learning with memory-augmented neural networks","author":"santoro","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1038\/nature20101"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.510"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.513"},{"key":"ref63","article-title":"Accurate scale estimation for robust visual tracking","author":"danelljan","year":"2014","journal-title":"Proc British Mach Vis Conf"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.63"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"ref27","first-page":"1929","article-title":"Dropout : A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref65","first-page":"4847","article-title":"Discriminative correlation filter with channel and spatial reliability","author":"luke\u017ei?","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.512"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.21"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.159"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.490"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.466"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00508"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_10"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00510"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.226"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88682-2_19"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.11.007"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.239"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref51","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learning Representations"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.585"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.230"},{"key":"ref56","first-page":"607","article-title":"The visual object tracking VOT2016 challenge results","author":"kristan","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref55","first-page":"564","article-title":"The visual object tracking VOT2015 challenge results","author":"kristan","year":"2015","journal-title":"Proc IEEE Int Conf Comput Vis Workshops"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2388226"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref52","article-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016","journal-title":"arXiv 1603 04467"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.279"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.357"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.196"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.158"},{"key":"ref16","first-page":"749","article-title":"Learning to track at 100 FPS with deep regression networks","author":"held","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.531"},{"key":"ref18","article-title":"Modeling and propagating CNNs in a tree structure for visual tracking","author":"nam","year":"2016","journal-title":"arXiv 1608 07242"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.235"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref6","first-page":"1137","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref8","first-page":"1520","article-title":"Learning deconvolution network for semantic segmentation","author":"noh","year":"2016","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.352"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref46","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"kendall","year":"2018","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref48","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref47","article-title":"Layer normalization","author":"ba","year":"2016","journal-title":"arXiv 1607 06450"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0767-8"},{"key":"ref41","first-page":"733","article-title":"A convex formulation for learning task relationships in multi-task learning","author":"zhang","year":"2010","journal-title":"Proc 26th Conf Uncertainty Artif Intell"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref43","first-page":"702","article-title":"Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation","author":"yao","year":"2012","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/08770289.pdf?arnumber=8770289","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:49:15Z","timestamp":1652194155000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8770289\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":76,"URL":"https:\/\/doi.org\/10.1109\/tpami.2019.2929034","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}