{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T04:02:01Z","timestamp":1751774521925,"version":"3.41.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2018,6,23]],"date-time":"2018-06-23T00:00:00Z","timestamp":1529712000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s11042-018-6179-y","type":"journal-article","created":{"date-parts":[[2018,6,23]],"date-time":"2018-06-23T07:24:32Z","timestamp":1529738672000},"page":"29949-29969","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Eclectic domain mixing for effective adaptation in action spaces"],"prefix":"10.1007","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7798-6099","authenticated-orcid":false,"given":"Arshad","family":"Jamal","sequence":"first","affiliation":[]},{"given":"Dipti","family":"Deodhare","sequence":"additional","affiliation":[]},{"given":"Vinay","family":"Namboodiri","sequence":"additional","affiliation":[]},{"given":"K S","family":"Venkatesh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,6,23]]},"reference":[{"key":"6179_CR1","doi-asserted-by":"crossref","unstructured":"Aljundi R, Emonet R, Muselet D, Sebban M (2015) Landmarks-based kernelized subspace alignment for unsupervised domain adaptation. In: CVPR","DOI":"10.1109\/CVPR.2015.7298600"},{"key":"6179_CR2","doi-asserted-by":"crossref","unstructured":"Baktashmotlagh M, Harandi M, Lovell B, Salzmann M (2013) Unsupervised domain adaptation by domain invariant projection. In: ICCV","DOI":"10.1109\/ICCV.2013.100"},{"key":"6179_CR3","doi-asserted-by":"crossref","unstructured":"Baktashmotlagh M, Harandi MT, Lovell BC, Salzmann M (2014) Domain adaptation on the statistical manifold. In: CVPR","DOI":"10.1109\/CVPR.2014.318"},{"key":"6179_CR4","doi-asserted-by":"crossref","unstructured":"Ben-David S, Blitzer J, Crammer K, Pereira F (2007) Analysis of representations for domain adaptation. In: NIPS, pp 137\u2013144","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"6179_CR5","unstructured":"Bergamo A, Torresani L (2010) Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach. In: NIPS"},{"key":"6179_CR6","doi-asserted-by":"crossref","unstructured":"Blitzer J, McDonald R, Pereira F (2006) Domain adaptation with structural correspondence learning. In: Proc of EMNLP, pp 120\u2013128","DOI":"10.3115\/1610075.1610094"},{"key":"6179_CR7","doi-asserted-by":"crossref","unstructured":"Caseiro R, Henriques JF, Martins P, Batista J (2015) Beyond the shortest path Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow. In: CVPR","DOI":"10.1109\/CVPR.2015.7299009"},{"key":"6179_CR8","unstructured":"Cross-Dataset Setup: https:\/\/sites.google.com\/site\/crossdataset\/"},{"key":"6179_CR9","doi-asserted-by":"crossref","unstructured":"Csurka G (2017) Domain adaptation for visual applications: a comprehensive survey. In: Arxiv","DOI":"10.1007\/978-3-319-58347-1"},{"key":"6179_CR10","unstructured":"Daume IIIH (2007) Frustratingly easy domain adaptation. In: Proc of ACL, pp 256\u2013263"},{"key":"6179_CR11","unstructured":"Daume IIIH, Kumar A, Saha A (2010) Co-regularization based semi-supervised domain adaptation. In: NIPS"},{"key":"6179_CR12","doi-asserted-by":"crossref","unstructured":"Daume IIIH, Marcu D (2006) Domain adaptation for statistical classifiers. JAIR","DOI":"10.1613\/jair.1872"},{"key":"6179_CR13","doi-asserted-by":"crossref","unstructured":"Donahue J, Hendricks LA, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: CVPR","DOI":"10.21236\/ADA623249"},{"key":"6179_CR14","doi-asserted-by":"crossref","unstructured":"Duan L, Tsang I, Xu D, Chua T (2009) Domain adaptation from multiple sources via auxiliary classifiers. In: ICML","DOI":"10.1145\/1553374.1553411"},{"key":"6179_CR15","unstructured":"Duan L, Tsang I, Xu D, Maybank S (2009) Domain transfer SVM for video concept detection. In: CVPR"},{"key":"6179_CR16","unstructured":"Duan L, Xu D, Tsang IW (2012) Domain adaptation from multiple Sources: a Domain-Dependent regularization approach. In: IEEE Transactions on neural networks and learning systems"},{"key":"6179_CR17","unstructured":"Duan L, Xu D, Tsang IW, Luo J (2012) Visual event recognition in videos by learning from web data. In: IEEE Transactions on pattern analysis and machine intelligence"},{"key":"6179_CR18","unstructured":"Faraji Davar N, deCampos TE, Windridge D, Kittler J, Christmas W (2011) Domain adaptation in the context of sport video action recognition. In: Domain adaptation workshop in conjunction with NIPS"},{"key":"6179_CR19","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Arxiv","DOI":"10.1109\/CVPR.2016.213"},{"key":"6179_CR20","doi-asserted-by":"crossref","unstructured":"Fernando B, Gavves EM, Jos O, Ghodrati A, Tuytelaars T (2015) Modeling video evolution for action recognition. In: CVPR","DOI":"10.1109\/CVPR.2015.7299176"},{"key":"6179_CR21","doi-asserted-by":"crossref","unstructured":"Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: ICCV","DOI":"10.1109\/ICCV.2013.368"},{"key":"6179_CR22","unstructured":"Ganin Y, Lempitsky VS (2015) Unsupervised domain adaptation by backpropagation. In: ICML"},{"key":"6179_CR23","unstructured":"Gong B, Grauman K, Sha F (2013) Connecting the dots with landmarks discriminatively learning domain-invariant features for unsupervised domain adaptation. In: ICML"},{"key":"6179_CR24","unstructured":"Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: CVPR"},{"key":"6179_CR25","doi-asserted-by":"crossref","unstructured":"Gopalan R, Li R, Chellappa R (2014) Unsupervised adaptation across domain shifts by generating intermediate data representations. In: IEEE Trans on pattern anal Mach Intell","DOI":"10.1109\/TPAMI.2013.249"},{"key":"6179_CR26","doi-asserted-by":"crossref","unstructured":"Gopalan R, Li R, Patel V, Chellappa R (2015) Domain adaptation for visual recognition. In: Found Trends Comput Graph Vis","DOI":"10.1561\/0600000057"},{"key":"6179_CR27","doi-asserted-by":"crossref","unstructured":"Hoffman J, Kulis B, Darrell T, Saenko K (2012) Discovering latent domains for multi-source domain adaptation. In: ECCV","DOI":"10.1007\/978-3-642-33709-3_50"},{"key":"6179_CR28","first-page":"227","volume":"16","author":"F Jiang","year":"2015","unstructured":"Jiang F, Zhang S, Wu S, Gao Y, Zhao D (2015) Multi-layered gesture recognition with Kinect. J Mach Learn Res 16:227\u2013254","journal-title":"J Mach Learn Res"},{"key":"6179_CR29","unstructured":"KTH and MSR Action II Dataset: http:\/\/www.cs.utexas.edu\/~chaoyeh\/web_action_data\/dataset_list.html"},{"key":"6179_CR30","doi-asserted-by":"crossref","unstructured":"Kuhne H, Jhuang H, Garrote E, Poggio T, Serre T (2011) HMDB a large video database for human motion recognition. In: ICCV","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"6179_CR31","doi-asserted-by":"crossref","unstructured":"Kulis B, Saenko K, Darrell T (2011) What you saw is not what you get Domain adaptation using asymmetric kernel transforms. In: Proc of CVPR, pp 1785\u20131792","DOI":"10.1109\/CVPR.2011.5995702"},{"key":"6179_CR32","doi-asserted-by":"crossref","unstructured":"Li Ruonan (2012) Discriminative virtual views for cross-view action recognition. In: CVPR","DOI":"10.1109\/CVPR.2012.6248011"},{"key":"6179_CR33","unstructured":"Long M, Cao Y, Wang J, Jordan MI (2015) Learning transferable features with deep adaptation networks. In: ICML"},{"key":"6179_CR34","unstructured":"Long M, Wang J, Jordan MI (2016) Unsupervised domain adaptation with residual transfer networks. In: Arxiv"},{"key":"6179_CR35","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: Arxiv"},{"key":"6179_CR36","doi-asserted-by":"crossref","unstructured":"Ni J, Qiu Q, Chellappa R (2013) Subspace interpolation via dictionary learning for unsupervised domain adaptation. In: CVPR","DOI":"10.1109\/CVPR.2013.95"},{"key":"6179_CR37","doi-asserted-by":"crossref","unstructured":"Niebles JC, Chen C-W, Fei-Fei L (2010) Modeling temporal structure of decomposable motion segments for activity classification. In: ECCV","DOI":"10.1007\/978-3-642-15552-9_29"},{"key":"6179_CR38","first-page":"1","volume":"99","author":"S Pan","year":"2009","unstructured":"Pan S, Tsang I, Kwok J, Yang Q (2009) Domain adaptation via transfer component analysis. IEEE Trans Neural Nets 99:1\u201312","journal-title":"IEEE Trans Neural Nets"},{"key":"6179_CR39","doi-asserted-by":"crossref","unstructured":"Qiu Q, Patel V, Turaga P, Chellappa R (2012) Domain adaptive dictionary learning. In: ECCV","DOI":"10.1007\/978-3-642-33765-9_45"},{"key":"6179_CR40","doi-asserted-by":"crossref","unstructured":"Reddy KK, Shah M (2013) Recognizing 50 human action categories of web videos. In: Mach Vision appl","DOI":"10.1007\/s00138-012-0450-4"},{"key":"6179_CR41","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: ECCV","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"6179_CR42","unstructured":"Simonyan K, Zisserman A (2014) Two-Stream Convolutional networks for action rec. In: NIPS"},{"key":"6179_CR43","doi-asserted-by":"crossref","unstructured":"Smola A, Gretton A, Song L, Sch\u00f6lkopf B (2007) A Hilbert space embedding for distributions. In: Algorithmic learning theory","DOI":"10.1007\/978-3-540-75225-7_5"},{"key":"6179_CR44","doi-asserted-by":"crossref","unstructured":"Sultani W, Saleemi I (2014) Human action recognition across datasets by Foreground-Weighted histogram decomposition. In: CVPR","DOI":"10.1109\/CVPR.2014.103"},{"key":"6179_CR45","doi-asserted-by":"crossref","unstructured":"Sun B, Feng J, Saenko K (2016) Return of frustratingly easy domain adaptation. In: Conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"6179_CR46","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2015) Subspace distribution alignment for unsupervised domain adaptation. In: BMVC","DOI":"10.5244\/C.29.24"},{"key":"6179_CR47","doi-asserted-by":"crossref","unstructured":"Tommasi T, Tuytelaars T (2014) A testbed for cross-dataset analysis. In: ECCV","DOI":"10.1007\/978-3-319-16199-0_2"},{"key":"6179_CR48","doi-asserted-by":"crossref","unstructured":"Tran D u, Bourdev LD, Fergus R, Torresani L, Paluri M (2015) Learning Spatio-temporal features with 3D convolutional networks. In: ICCV","DOI":"10.1109\/ICCV.2015.510"},{"key":"6179_CR49","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman T, Darrell J, Saenko K (2015) Simultaneous deep transfer across domains and tasks. In: Arxiv","DOI":"10.1109\/ICCV.2015.463"},{"key":"6179_CR50","doi-asserted-by":"crossref","unstructured":"Wang H, Schmid C (2013) Action recognition with improved trajectories. In: Proc. ICCV, pp 3551\u20133558","DOI":"10.1109\/ICCV.2013.441"},{"key":"6179_CR51","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.ins.2013.12.052","volume":"281","author":"S Zhang","year":"2014","unstructured":"Zhang S, Yao H, Sun X, Wang K, Zhang J, Lu X, Zhang Y (2014) Action recognition based on overcomplete independent components analysis. Inf Sci 281:635\u2013647","journal-title":"Inf Sci"},{"key":"6179_CR52","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang C, Xiao B, Zhou W, Liu S, Shi C (2013) Cross-View Action recognition via a continuous virtual path. In: CVPR","DOI":"10.1109\/CVPR.2013.347"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-018-6179-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-6179-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-6179-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T08:19:54Z","timestamp":1751703594000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-018-6179-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,23]]},"references-count":52,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["6179"],"URL":"https:\/\/doi.org\/10.1007\/s11042-018-6179-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2018,6,23]]},"assertion":[{"value":"31 August 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}