{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T14:18:21Z","timestamp":1772806701489,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2017,8,16]],"date-time":"2017-08-16T00:00:00Z","timestamp":1502841600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Chinese National Natural Science Foundation","doi-asserted-by":"crossref","award":["61471049"],"award-info":[{"award-number":["61471049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Chinese National Natural Science Foundation","doi-asserted-by":"crossref","award":["61372169"],"award-info":[{"award-number":["61372169"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Chinese National Natural Science Foundation","doi-asserted-by":"crossref","award":["61532018"],"award-info":[{"award-number":["61532018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,2]]},"DOI":"10.1007\/s11042-017-5087-x","type":"journal-article","created":{"date-parts":[[2017,8,15]],"date-time":"2017-08-15T18:42:26Z","timestamp":1502822546000},"page":"3261-3277","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Weakly supervised detection with decoupled attention-based deep representation"],"prefix":"10.1007","volume":"77","author":[{"given":"Wenhui","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,8,16]]},"reference":[{"key":"5087_CR1","first-page":"1","volume-title":"Multiple object recognition with visual attention","author":"J Ba","year":"2015","unstructured":"Ba J, Mnih V, Kavukcuoglu K (2015) Multiple object recognition with visual attention. International Conference on Learning Representations, In, pp 1\u201310"},{"key":"5087_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_43","volume-title":"Weakly supervised localization using deep feature maps","author":"AJ Bency","year":"2016","unstructured":"Bency AJ, Kwon H, Lee H, Karthikeyan S, Manjunath BS (2016) Weakly supervised localization using deep feature maps. European Conference on Computer Vision"},{"key":"5087_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.311","volume-title":"Weakly supervised deep detection networks","author":"H Bilen","year":"2016","unstructured":"Bilen H, Vedaldi A (2016) Weakly supervised deep detection networks. IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"5087_CR4","doi-asserted-by":"crossref","unstructured":"Bilen H, Pedersoli M, Tuytelaars T (2015) Weakly supervised object detection with convex clustering. In: IEEE Conference on Computer Vision and Pattern Recognition. pp 1081\u20131089","DOI":"10.1109\/CVPR.2015.7298711"},{"issue":"10","key":"5087_CR5","doi-asserted-by":"publisher","first-page":"2294","DOI":"10.1109\/TNNLS.2016.2582746","volume":"28","author":"Xiaojun Chang","year":"2017","unstructured":"Chang X, Yang Y (2016) Semi-supervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw Learn Syst. doi:\n                    10.1109\/TNNLS.2016.2582746","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"5087_CR6","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1109\/TPAMI.2016.2608901","volume":"39","author":"X Chang","year":"2016","unstructured":"Chang X, Yu Y, Yang Y, Xing EP (2016) Semantic pooling for complex event analysis in untrimmed videos. IEEE Trans Pattern Anal Mach Intell 39:1617\u20131632. doi:\n                    10.1109\/TPAMI.2016.2608901","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5087_CR7","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1109\/TNNLS.2015.2441735","volume":"27","author":"X Chang","year":"2016","unstructured":"Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2016) Compound rank-k projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27:1502\u20131513","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5087_CR8","doi-asserted-by":"publisher","first-page":"3911","DOI":"10.1109\/TIP.2017.2708506","volume":"26","author":"X Chang","year":"2017","unstructured":"Chang X, Ma Z, Lin M, Yang Y, Hauptmann AG (2017) Feature interaction augmented sparse learning for fast Kinect motion detection. IEEE Trans Image Process 26:3911\u20133920","journal-title":"IEEE Trans Image Process"},{"key":"5087_CR9","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1109\/TCYB.2016.2539546","volume":"47","author":"X Chang","year":"2017","unstructured":"Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2017) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 47:1180\u20131197","journal-title":"IEEE Trans Cybern"},{"key":"5087_CR10","first-page":"1","volume-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","author":"L-C Chen","year":"2015","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2015) Semantic image segmentation with deep convolutional nets and fully connected CRFs. International Conference on Learning Representations, In, pp 1\u201314"},{"key":"5087_CR11","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TPAMI.2016.2535231","volume":"39","author":"RG Cinbis","year":"2017","unstructured":"Cinbis RG, Verbeek J, Schmid C (2017) Weakly supervised object localization with multi-fold multiple instance learning. IEEE Trans Pattern Anal Mach Intell 39:189\u2013203. doi:\n                    10.1109\/TPAMI.2016.2535231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5087_CR12","unstructured":"Dai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379\u2013387"},{"key":"5087_CR13","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s11263-012-0538-3","volume":"100","author":"T Deselaers","year":"2012","unstructured":"Deselaers T, Alexe B, Ferrari V (2012) Weakly supervised localization and learning with generic knowledge. Int J Comput Vis 100:275\u2013293. doi:\n                    10.1007\/s11263-012-0538-3","journal-title":"Int J Comput Vis"},{"key":"5087_CR14","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2014","unstructured":"Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A (2014) The Pascal visual object classes challenge: a retrospective. Int J Comput Vis 111:98\u2013136. doi:\n                    10.1007\/s11263-014-0733-5","journal-title":"Int J Comput Vis"},{"key":"5087_CR15","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the KITTI dataset. Int J Robot Res 32:1231\u20131237. doi:\n                    10.1177\/0278364913491297","journal-title":"Int J Robot Res"},{"key":"5087_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.135","volume-title":"Object detection via a multi-region & semantic segmentation-aware CNN model","author":"S Gidaris","year":"2015","unstructured":"Gidaris S, Komodakis N (2015) Object detection via a multi-region & semantic segmentation-aware CNN model. IEEE International Conference on Computer Vision"},{"key":"5087_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169","volume-title":"Fast R-CNN","author":"R Girshick","year":"2015","unstructured":"Girshick R (2015) Fast R-CNN. IEEE International Conference on Computer Vision"},{"key":"5087_CR18","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","volume":"53","author":"J Han","year":"2015","unstructured":"Han J, Zhang D, Cheng G, Guo L, Ren J (2015) Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans Geosci Remote Sens 53:3325\u20133337","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5087_CR19","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition. pp 171\u2013180","DOI":"10.1109\/CVPR.2016.90"},{"key":"5087_CR20","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia. pp 675\u2013678","DOI":"10.1145\/2647868.2654889"},{"key":"5087_CR21","doi-asserted-by":"publisher","first-page":"9095","DOI":"10.1007\/s11042-015-2939-0","volume":"75","author":"W Jiang","year":"2016","unstructured":"Jiang W, Zhao Z, Su F (2016) Bayes pooling of visual phrases for object retrieval. Multimed Tools Appl 75:9095\u20139119. doi:\n                    10.1007\/s11042-015-2939-0","journal-title":"Multimed Tools Appl"},{"key":"5087_CR22","doi-asserted-by":"publisher","unstructured":"Karthikeyan S, Ngo T, Eckstein M, Manjunath BS (2015) Eye tracking assisted extraction of attentionally important objects from videos. Proc IEEE Conf Comput Vis Pattern Recognit. doi:\n                    10.1109\/CVPR.2015.7298944","DOI":"10.1109\/CVPR.2015.7298944"},{"key":"5087_CR23","unstructured":"Krizhevsky A, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceeding NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, Curran Associates Inc., Lake Tahoe, Nevada \u2014 December 03\u201306, 2012, pp. 1097\u20131105"},{"key":"5087_CR24","volume-title":"SSD : single shot MultiBox detector","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S (2016) SSD : single shot MultiBox detector. European Conference on Computer Vision"},{"key":"5087_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965","volume-title":"Fully convolutional networks for semantic segmentation","author":"J Long","year":"2015","unstructured":"Long J, Shelhamer E (2015) Fully convolutional networks for semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"5087_CR26","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TMM.2017.2659221","volume":"19","author":"Z Ma","year":"2017","unstructured":"Ma Z, Chang X, Yang Y, Sebe N, Hauptmann AG (2017) The many shades of negativity. IEEE Trans Multimedia 19:1558\u20131568","journal-title":"IEEE Trans Multimedia"},{"key":"5087_CR27","doi-asserted-by":"publisher","unstructured":"Ma Z, Chang X, Xu Z, Sebe N, Hauptmann AG (2017) Joint attributes and event analysis for multimedia event detection. IEEE Trans Neural Netw Learn Syst. doi:\n                    10.1109\/TNNLS.2017.2709308","DOI":"10.1109\/TNNLS.2017.2709308"},{"key":"5087_CR28","first-page":"2204","volume-title":"Recurrent models of visual attention","author":"V Mnih","year":"2014","unstructured":"Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. Advances in Neural Information Processing Systems, In, pp 2204\u20132212"},{"key":"5087_CR29","volume-title":"(2014) learning and transferring mid-level image representations using convolutional neural networks","author":"M Oquab","year":"1717","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J (1717\u20131724) (2014) learning and transferring mid-level image representations using convolutional neural networks. IEEE Conference on Computer Vision and Pattern Recognition. pp, In"},{"key":"5087_CR30","first-page":"685","volume-title":"Is object localization for free? - weakly-supervised learning with convolutional neural networks","author":"M Oquab","year":"2015","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J (2015) Is object localization for free? - weakly-supervised learning with convolutional neural networks. IEEE Conference on Computer Vision and Pattern Recognition, In, pp 685\u2013694"},{"key":"5087_CR31","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-319-10602-1_24","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Dim P. Papadopoulos","year":"2014","unstructured":"Papadopoulos DP, Clarke ADF, Keller F, Ferrari V (2014) Training object class detectors from eye tracking data. In: European Conference on Computer Vision. pp 1\u201316"},{"key":"5087_CR32","volume-title":"You only look once: unified, real-time object detection","author":"J Redmon","year":"2016","unstructured":"Redmon J, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"5087_CR33","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceeding NIPS'15 Proceedings of the 28th International Conference on Neural Information Processing Systems, MIT Press Cambridge, Montreal, Canada \u2014 December 07\u201312, 2015, pp. 91\u201399"},{"key":"5087_CR34","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TPAMI.2015.2456908","volume":"38","author":"W Ren","year":"2016","unstructured":"Ren W, Member S, Huang K, Member S (2016) Weakly supervised large scale object localization with multiple instance learning and bag splitting. IEEE Trans Pattern Anal Mach Intell 38:405\u2013416. doi:\n                    10.1109\/TPAMI.2015.2456908","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5087_CR35","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252. doi:\n                    10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis"},{"key":"5087_CR36","first-page":"1","volume-title":"Action recognition using visual attention","author":"S Sharma","year":"2016","unstructured":"Sharma S, Kiros R, Salakhutdinov R (2016) Action recognition using visual attention. International Conference on Learning Representations, In, pp 1\u201311"},{"key":"5087_CR37","doi-asserted-by":"crossref","unstructured":"Shi M, Ferrari V (2016) Weakly supervised object localization using size estimates. In: European Conference on Computer Vision","DOI":"10.1109\/ICCV.2017.366"},{"key":"5087_CR38","doi-asserted-by":"crossref","unstructured":"Shih KJ, Singh S, Hoiem D (2016) Where to look: focus regions for visual question answering. IEEE, Las Vegas","DOI":"10.1109\/CVPR.2016.499"},{"key":"5087_CR39","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations. pp 1\u201314"},{"key":"5087_CR40","unstructured":"Song HO, Girshick R, Jegelka S, Mairal J, Harchaoui Z, Darrell T (2014) On learning to localize objects with minimal supervision. In: Proceeding ICML'14 Proceedings of the 31st International Conference on International Conference on Machine Learning vol. 32, Beijing, China, 21\u201326 June, 2014"},{"key":"5087_CR41","unstructured":"Song HO, Lee YJ, Jegelka S, Darrell T (2014) Weakly-supervised discovery of visual pattern configurations. In: Proceeding NIPS'14 Proceedings of the 27th International Conference on Neural Information Processing Systems, MIT Press Cambridge, Montreal, Canada, 8\u201313 December, 2014"},{"key":"5087_CR42","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1038\/21176","volume":"399","author":"S Treue","year":"1999","unstructured":"Treue S, Martinez Trujillo JC (1999) Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399:575\u2013579. doi:\n                    10.1038\/21176","journal-title":"Nature"},{"issue":"2","key":"5087_CR43","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"J. R. R. Uijlings","year":"2013","unstructured":"Uijlings JRR, Sande KE a., Gevers T, Smeulders a. WM (2013) Selective search for object recognition. Int J Comput Vis 104:154\u2013171","journal-title":"International Journal of Computer Vision"},{"key":"5087_CR44","volume-title":"We don\u2019t need no bounding-boxes: training object class detectors using only human verification","author":"JRR Uijlings","year":"2016","unstructured":"Uijlings JRR, Keller F, Ferrari V (2016) We don\u2019t need no bounding-boxes: training object class detectors using only human verification. IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"5087_CR45","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TIP.2015.2396361","volume":"24","author":"C Wang","year":"2015","unstructured":"Wang C, Huang K, Ren W, Zhang J, Maybank S (2015) Large-scale weakly supervised object localization via latent category learning. IEEE Trans Image Process 24:1371\u20131385. doi:\n                    10.1109\/TIP.2015.2396361","journal-title":"IEEE Trans Image Process"},{"key":"5087_CR46","first-page":"451","volume-title":"Ask, attend and answer: exploring question-guided spatial attention for visual question answering","author":"H Xu","year":"2016","unstructured":"Xu H, Saenko K (2016) Ask, attend and answer: exploring question-guided spatial attention for visual question answering. European Conference on Computer Vision, In, pp 451\u2013466"},{"key":"5087_CR47","volume-title":"Show, attend and tell: neural image caption generation with visual attention","author":"K Xu","year":"2015","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. International Conference on Machine learning"},{"key":"5087_CR48","first-page":"10","volume-title":"Image captioning with semantic attention","author":"Q You","year":"2016","unstructured":"You Q, Jin H, Wang Z, Fang C, Luo J (2016) Image captioning with semantic attention. In, IEEE Conference on Computer Vision and Pattern Recognition, p 10"},{"key":"5087_CR49","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11263-016-0907-4","volume":"120","author":"D Zhang","year":"2016","unstructured":"Zhang D, Han J, Li C, Wang J, Li X (2016) Detection of co-salient objects by looking deep and wide. Int J Comput Vis 120:215\u2013232. doi:\n                    10.1007\/s11263-016-0907-4","journal-title":"Int J Comput Vis"},{"key":"5087_CR50","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, Shao L (2016) Cosaliency detection based on Intrasaliency prior transfer and deep Intersaliency mining. IEEE Trans Neural Netw Learn Syst 27:1163\u20131176. doi:\n                    10.1109\/TNNLS.2015.2495161","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5087_CR51","unstructured":"Zhang D, Meng D, Zhao L, Han J (2016) Bridging saliency detection to weakly supervised object detection based on self-paced curriculum learning. In: Proceeding IJCAI'16 Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, AAAI Press, New York, USA, 9\u201315 July, 2016, pp. 3538\u20133544"},{"key":"5087_CR52","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 (2017) Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans Pattern Anal Mach Intell 39:865\u2013878. doi:\n                    10.1109\/TPAMI.2016.2567393","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5087_CR53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319","volume-title":"Learning deep features for discriminative localization","author":"B Zhou","year":"2016","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"5087_CR54","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1109\/TMM.2015.2431496","volume":"17","author":"L Zhu","year":"2015","unstructured":"Zhu L, Shen J, Jin H, Xie L, Zheng R (2015) Landmark classification with hierarchical multi-modal exemplar feature. IEEE Trans Multimedia 17:981\u2013993. doi:\n                    10.1109\/TMM.2015.2431496","journal-title":"IEEE Trans Multimedia"},{"key":"5087_CR55","doi-asserted-by":"publisher","first-page":"2756","DOI":"10.1109\/TCYB.2014.2383389","volume":"45","author":"L Zhu","year":"2015","unstructured":"Zhu L, Shen J, Jin H, Zheng R, Xie L (2015) Content-based visual landmark search via multimodal hypergraph learning. IEEE Trans Cybern 45:2756\u20132769. doi:\n                    10.1109\/TCYB.2014.2383389","journal-title":"IEEE Trans Cybern"},{"key":"5087_CR56","doi-asserted-by":"crossref","unstructured":"Zhu Z, Liang D, Zhang S, Huang X, Baoli Li SH (2016) Traffic-sign detection and classification in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition. pp 2110\u20132118","DOI":"10.1109\/CVPR.2016.232"},{"key":"5087_CR57","unstructured":"Zhu L, Shen J, Liu X, Xie L, Nie L (2016) Learning compact visual representation with canonical views for robust mobile landmark search. In: Proceeding IJCAI'16 Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, AAAI Press, New York, USA, 9\u201315 July 2016, pp. 3959\u20133965"},{"issue":"11","key":"5087_CR58","doi-asserted-by":"publisher","first-page":"3941","DOI":"10.1109\/TCYB.2016.2591068","volume":"47","author":"Lei Zhu","year":"2017","unstructured":"Zhu L, Shen J, Xie L, Cheng Z (2016) Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE Trans Cybern. doi:\n                    10.1109\/TCYB.2016.2591068","journal-title":"IEEE Transactions on Cybernetics"},{"key":"5087_CR59","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1109\/TKDE.2016.2562624","volume":"29","author":"L Zhu","year":"2017","unstructured":"Zhu L, Shen J, Xie L, Cheng Z (2017) Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans Knowl Data Eng 29:472\u2013486. doi:\n                    10.1109\/TKDE.2016.2562624","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-017-5087-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5087-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5087-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T11:15:54Z","timestamp":1562584554000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-017-5087-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,16]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,2]]}},"alternative-id":["5087"],"URL":"https:\/\/doi.org\/10.1007\/s11042-017-5087-x","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,16]]},"assertion":[{"value":"8 March 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2017","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2017","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2017","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}