[1] |
Szeliski R. Computer Vision:Algorithms and Applications. New York:Springer-Verlag, 2010. 1-28 |
[2] |
Pinheiro P O, Collobert R. From image-level to pixel-level labeling with convolutional networks. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015. 1713-1721 https://www.researchgate.net/publication/308850633_From_image-level_to_pixel-level_labeling_with_Convolutional_Networks |
[3] |
Grauman K, Leibe B. Visual object recognition. Synthesis Lectures on Artificial Intelligence and Machine Learning. San Rafael: Morgan & Claypool Publishers, 2011. 1-181 |
[4] |
Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2):91-110 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025429678/ |
[5] |
Chen C C, Hsieh S L. Using binarization and hashing for efficient SIFT matching. Journal of Visual Communication and Image Representation, 2015, 30:86-93 doi: 10.1016/j.jvcir.2015.02.014 |
[6] |
Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robust features (SURF). Computer Vision and Image Understanding, 2008, 110(3):346-359 doi: 10.1016/j.cviu.2007.09.014 |
[7] |
Donahue J, Jia Y Q, Vinyals O, Hoffman J, Zhang N, Tzeng E, et al. DeCAF: a deep convolutional activation feature for generic visual recognition. In: Proceedings of the 31th International Conference on International Conference on Machine Learning. Beijing, China: ACM, 2014. I-647-I-655 |
[8] |
Friedman J H, Bentley J L, Finkel R A. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 1977, 3(3):209-226 doi: 10.1145/355744.355745 |
[9] |
Stewénius H, Gunderson S H, Pilet J. Size matters: exhaustive geometric verification for image retrieval. In: Proceedings of the 12th European Conference on Computer Vision. Florence, Italy: Springer, 2012. 674-687 |
[10] |
Deng J, Dong W, Socher R, Li L J, Li K, Li F F. ImageNet: a large-scale hierarchical image database. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 248-255 https://www.researchgate.net/publication/221361415_ImageNet_a_Large-Scale_Hierarchical_Image_Database |
[11] |
Xiao J X, Hays J, Ehinger K A, Oliva A, Torralba A. SUN database: large-scale scene recognition from abbey to zoo. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 3485-3492 |
[12] |
Torralba A, Efros A A. Unbiased look at dataset bias. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE, 2011. 1521-1528 |
[13] |
Tommasi T, Patricia N, Caputo B, Tuytelaars T. A deeper look at dataset bias. In: Proceedings of the 37th German Conference on Pattern Recognition. Aachen, Germany: Springer, 2015. 86-93 |
[14] |
Griffin G, Holub A, Perona P. Caltech-256 object category dataset, Technical Report CNSTR-2007-001, California Institute of Technology, USA, 2007. |
[15] |
Zeng M, Ren J T. Domain transfer dimensionality reduction via discriminant kernel learning. In: Proceedings of the 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Kuala Lumpur, Malaysia: Springer-Verlag, 2012. 280-291 |
[16] |
陶剑文, Chung F L, 王士同, 姚奇富.稀疏标签传播:一种鲁棒的领域适应学习方法.软件学报, 2015, 26(5):977-1000 http://d.old.wanfangdata.com.cn/Periodical/rjxb201505001
Tao Jian-Wen, Chung F L, Wang Shi-Tong, Yao Qi-Fu. Sparse label propagation:a robust domain adaptation learning method. Journal of Software, 2015, 26(5):977-1000 http://d.old.wanfangdata.com.cn/Periodical/rjxb201505001 |
[17] |
龙明盛.迁移学习问题与方法研究[博士学位论文], 清华大学, 中国, 2014 http://cdmd.cnki.com.cn/Article/CDMD-10003-1015039180.htm
Long Ming-sheng. Transfer Learning: Problems and Methods[Ph. D. dissertation], Tsinghua University, China, 2014 http://cdmd.cnki.com.cn/Article/CDMD-10003-1015039180.htm |
[18] |
Taylor M E, Kuhlmann G, Stone P. Autonomous transfer for reinforcement learning. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems. Estoril, Portugal: ACM, 2008. 283-290 |
[19] |
朱美强, 程玉虎, 李明, 王雪松, 冯涣婷.一类基于谱方法的强化学习混合迁移算法.自动化学报, 2012, 38(11):1765-1776 http://www.aas.net.cn/CN/abstract/abstract17783.shtml
Zhu Mei-Qiang, Cheng Yu-Hu, Li Ming, Wang Xue-Song, Feng Huan-Ting. A hybrid transfer algorithm for reinforcement learning based on spectral method. Acta Automatica Sinica, 2012, 38(11):1765-1776 http://www.aas.net.cn/CN/abstract/abstract17783.shtml |
[20] |
Cheng B, Liu M X, Suk H I, Shen D G, Zhang D Q. Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging and Behavior, 2015, 9(4):913-926 doi: 10.1007/s11682-015-9356-x |
[21] |
Long M S, Wang J M, Cao Y, Sun J G, Yu P S. Deep learning of transferable representation for scalable domain adaptation. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(8):2027-2040 doi: 10.1109/TKDE.2016.2554549 |
[22] |
Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359 doi: 10.1109/TKDE.2009.191 |
[23] |
Aytar Y. Transfer learning for object category detection[Ph. D. thesis], University of Oxford, UK, 2014 |
[24] |
Caruana R. Multitask learning. Machine Learning, 1997, 28(1):41-75 doi: 10.1023/A:1007379606734 |
[25] |
Zhang Y, Yang Q. A survey on multi-task learning. arXiv: 1707.08114v1, 2017 |
[26] |
Ding Z M, Shao M, Fu Y. Incomplete multisource transfer learning. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(2):310-323 doi: 10.1109/TNNLS.2016.2618765 |
[27] |
顾鑫, 王士同, 许敏.基于多源的跨领域数据分类快速新算法.自动化学报, 2014, 40(3):531-547 http://www.aas.net.cn/CN/abstract/abstract18319.shtml
Gu Xin, Wang Shi-Tong, Xu Min. A new cross-multidomain classification algorithm and its fast version for large datasets. Acta Automatica Sinica, 2014, 40(3):531-547 http://www.aas.net.cn/CN/abstract/abstract18319.shtml |
[28] |
Zhang Q, Li H G, Zhang Y, Li M. Instance transfer learning with multisource dynamic TrAdaBoost. The Scientific World Journal, 2014, 2014: Article No. 282747 |
[29] |
Pan J, Wang X, Cheng Y, Cao G. Multi-source transfer ELM-based Q learning. Neurocomputing, 2014, 137:57-64 doi: 10.1016/j.neucom.2013.04.045 |
[30] |
Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning. Journal of Big Data, 2016, 3: Article No. 9 |
[31] |
Li X. Regularized Adaptation: Theory, Algorithms and Applications[Ph. D. thesis], University of Washington, USA, 2007 |
[32] |
刘建伟, 孙正康, 罗雄麟.域自适应学习研究进展.自动化学报, 2014, 40(8):1576-1600 http://www.aas.net.cn/CN/abstract/abstract18427.shtml
Liu Jian-Wei, Sun Zheng-Kang, Luo Xiong-Lin. Review and research development on domain adaptation learning. Acta Automatica Sinica, 2014, 40(8):1576-1600 http://www.aas.net.cn/CN/abstract/abstract18427.shtml |
[33] |
Patel V M, Gopalan R, Li R N, Chellappa R. Visual domain adaptation:a survey of recent advances. IEEE Signal Processing Magazine, 2015, 32(3):53-69 doi: 10.1109/MSP.2014.2347059 |
[34] |
Shao H. Kernel methods for transfer learning to avoid negative transfer. International Journal of Computing Science and Mathematics, 2016, 7(2):190-199 |
[35] |
Ge L, Gao J, Ngo H, Li K, Zhang A D. On handling negative transfer and imbalanced distributions in multiple source transfer learning. Statistical Analysis and Data Mining, 2014, 7(4):254-271 doi: 10.1002/sam.11217 |
[36] |
Mihalkova L, Huynh T, Mooney R J. Mapping and revising markov logic networks for transfer learning. In: Proceedings of the 22nd National Conference on Artificial Intelligence. Vancouver, Canada: AAAI, 2007. 608-614 |
[37] |
Dai W Y, Yang Q, Xue G R, Yu Y. Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning. Corvalis, USA: ACM, 2007. 193-200 |
[38] |
Eaton E, DesJardins M. Set-based boosting for instance-level transfer. In: Proceedings of the 2009 IEEE International Conference on Data Mining Workshops. Miami, USA: IEEE, 2009. 422-428 |
[39] |
Kong S, Wang D H. Transfer heterogeneous unlabeled data for unsupervised clustering. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Tsukuba, Japan: IEEE, 2012. 1193-1196 |
[40] |
Kotzias D, Denil M, Blunsom P, De Freitas N. Deep multi-instance transfer learning. arXiv: 1411.3128, 2014 |
[41] |
张倩, 李明, 王雪松, 程玉虎, 朱美强.一种面向多源领域的实例迁移学习.自动化学报, 2014, 40(6):1176-1183 http://www.aas.net.cn/CN/abstract/abstract18387.shtml
Zhang Qian, Li Ming, Wang Xue-Song, Cheng Yu-Hu, Zhu Mei-Qiang. Instance-based transfer learning for multi-source domains. Acta Automatica Sinica, 2014, 40(6):1176-1183 http://www.aas.net.cn/CN/abstract/abstract18387.shtml |
[42] |
Zhou S, Schoenmakers G, Smirnov E, Peeters R, Driessens K, Chen S Q. Largest source subset selection for instance transfer. In: Proceedings of the 7th Asian Conference on Machine Learning. Hong Kong, China, 2015. 423-438 |
[43] |
Zhou S, Smirnov E N, Schoenmakers G, Peeters R. Conformal decision-tree approach to instance transfer. Annals of Mathematics and Artificial Intelligence, Switzerland:Springer, 2017, 81(1-2):85-104 doi: 10.1007/s10472-017-9554-x |
[44] |
Blitzer J, McDonald R, Pereira F. Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Sydney, Australia: ACM, 2006. 120-128 |
[45] |
Daumé Ⅲ H. Frustratingly easy domain adaptation. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague, Czech Republic: ACL, 2007. 256-263 |
[46] |
Pan S J, Tsang I W, Kwok J T, Yang Q. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 2011, 22(2):199-210 doi: 10.1109/TNN.2010.2091281 |
[47] |
Tahmoresnezhad J, Hashemi S. Common feature extraction in multi-source domains for transfer learning. In: Proceedings of the 7th Conference on Information and Knowledge Technology (IKT). Urmia, Iran: IEEE, 2015. 1-5 |
[48] |
Xue S, Lu J, Zhang G Q, Xiong L. Heterogeneous feature space based task selection machine for unsupervised transfer learning. In: Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). Taipei, China: IEEE, 2015. 46-51 |
[49] |
Deng J, Frühholz S, Zhang Z X, Schuller B. Recognizing emotions from whispered speech based on acoustic feature transfer learning. IEEE Access, 2017, 5:5235-5246 |
[50] |
Aytar Y, Zisserman A. Tabula rasa: model transfer for object category detection. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 2252-2259 |
[51] |
Zhou Y, Hospedales T M, Fenton N. When and where to transfer for Bayesian network parameter learning. Expert Systems with Applications, 2016, 55:361-373 doi: 10.1016/j.eswa.2016.02.011 |
[52] |
Tajbakhsh N, Shin J Y, Gurudu S R, Hurst R T, Kendall C B, Gotway M B, et al. Convolutional neural networks for medical image analysis:full training or fine tuning? IEEE Transactions on Medical Imaging, 2016, 35(5):1299-1312 doi: 10.1109/TMI.2016.2535302 |
[53] |
Segev N, Harel M, Mannor S, Crammer K, El-Yaniv R. Learn on source, refine on target:a model transfer learning framework with random forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(9):1811-1824 doi: 10.1109/TPAMI.2016.2618118 |
[54] |
Mihalkova L, Mooney R J. Transfer learning from minimal target data by mapping across relational domains. In: Proceedings of the 21st International Jont Conference on Artificial Intelligence. Pasadena, CA: Morgan Kaufmann, 2009. 1163-1168 |
[55] |
Myeong H, Lee K M. Tensor-based high-order semantic relation transfer for semantic scene segmentation. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013. 3073-3080 |
[56] |
Xia R, Zong C Q, Hu X L, Cambria E. Feature ensemble plus sample selection:domain adaptation for sentiment classification. IEEE Intelligent Systems, 2013, 28(3):10-18 doi: 10.1109/MIS.2013.27 |
[57] |
Mo Y, Zhang Z X, Wang Y H. A hybrid transfer learning mechanism for object classification across view. In: Proceedings of the 11th International Conference on Machine Learning and Applications. Boca Raton, USA: IEEE, 2012. 226-231 |
[58] |
Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). New York, USA: IEEE, 2006. 2169-2178 |
[59] |
黄凯奇, 任伟强, 谭铁牛.图像物体分类与检测算法综述.计算机学报, 2014, 36(6):1225-1240 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201406001
Huang Kai-Qi, Ren Wei-Qiang, Tan Tie-Niu. A review on image object classification and detection. Chinese Journal of Computers, 2014, 36(6):1225-1240 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201406001 |
[60] |
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S A, et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115(3):211-252 http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC0214533907/ |
[61] |
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, USA: Curran Associates Inc., 2012. 1097-1105 |
[62] |
Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. In: Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 818-833 |
[63] |
Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015. 1-9 |
[64] |
Simonyan K, Zisserman A. Very deep convolutional networks for large-Scale image recognition. arXiv: 1409.1556v6, 2015 |
[65] |
He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016. 770-778 |
[66] |
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. arXiv: 1709.01507v2, 2017 |
[67] |
Felzenszwalb P F, Girshick R B, McAllester D, Ramanan D. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9):1627-1645 doi: 10.1109/TPAMI.2009.167 |
[68] |
Girshick R, Donahue J, Darrell T, Malik J. Region-based convolutional networks for accurate object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(1):142-158 doi: 10.1109/TPAMI.2015.2437384 |
[69] |
He K M, Zhang X Y, Ren S Q, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916 doi: 10.1109/TPAMI.2015.2389824 |
[70] |
Girshick R. Fast R-CNN. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 1440-1448 |
[71] |
Ren S Q, He K M, Girshick R, Sun J. Faster R-CNN:towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149 doi: 10.1109/TPAMI.2016.2577031 |
[72] |
He K M, Gkioxari G, Dollár P, Girshick P. Mask R-CNN. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 2980-2988 |
[73] |
Redmon J, Divvala S K, Girshick R, Farhadi A. You only look once: unified, real-Time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 779-788 |
[74] |
Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017. 6517-6525 |
[75] |
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y, et al. SSD: single shot MultiBox detector. In: Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 21-37 |
[76] |
Najibi M, Rastegari M, Davis L S. G-CNN: an iterative grid based object detector. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016. 2369-2377 |
[77] |
Kong T, Sun F C, Yao A B, Liu H P, Lu M, Chen Y R. RON: reverse connection with objectness prior networks for object detection. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017. 5244-5252 |
[78] |
Weiss G M. Mining with rarity:a unifying framework. ACM SIGKDD Explorations Newsletter, 2004, 6(1):7-19 doi: 10.1145/1007730 |
[79] |
He H B, Garcia E A. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9):1263-1284 doi: 10.1109/TKDE.2008.239 |
[80] |
Zhou Z H, Liu X Y. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1):63-77 doi: 10.1109/TKDE.2006.17 |
[81] |
Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P. SMOTE:synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, 16(1):321-357 http://d.old.wanfangdata.com.cn/Periodical/dianzixb200911024 |
[82] |
Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C. Safe-Level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Bangkok, Thailand: Springer, 2009. 475-482 |
[83] |
Yang P Y, Yoo P D, Fernando J, Zhou B B, Zhang Z L, Zomaya A Y. Sample subset optimization techniques for imbalanced and ensemble learning problems in bioinformatics applications. IEEE Transactions on Cybernetics, 2014, 44(3):445-455 doi: 10.1109/TCYB.2013.2257480 |
[84] |
Al-Stouhi S, Reddy C K. Transfer learning for class imbalance problems with inadequate data. Knowledge and Information Systems, 2016, 48(1):201-228 doi: 10.1007/s10115-015-0870-3 |
[85] |
Weiss K R, Khoshgoftaar T M. Comparing transfer learning and traditional learning under domain class imbalance. In: Proceedings of the 16th IEEE International Conference on Machine Learning and Applications (ICMLA). Cancun, Mexico: IEEE, 2017. 337-343 |
[86] |
Wang K, Wu B. Power Equipment fault diagnosis model based on deep transfer learning with balanced distribution adaptation. In: Proceedings of the Advanced Data Mining and Applications. ADMA, Cham: Springer, 2018. 178-188 |
[87] |
Su K M, Hairston W D, Robbins K A. Adaptive thresholding and reweighting to improve domain transfer learning for unbalanced data with applications to EEG imbalance. In: Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Anaheim, CA, USA: IEEE, 2016. 320-325 |
[88] |
Zhang X S, Zhuang Y, Hu H S, Wang W. 3-D laser-based multiclass and multiview object detection in cluttered indoor scenes. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(1):177-190 doi: 10.1109/TNNLS.2015.2496195 |
[89] |
Hsu T M H, Chen W Y, Hou C A, Tsai Y H H, Yeh Y R, Wang Y C F. Unsupervised domain adaptation with imbalanced cross-domain data. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 4121-4129 |
[90] |
Tsai Y H H, Hou C A, Chen W Y, Yeh Y R, Wang Y C F. Domain-constraint transfer coding for imbalanced unsupervised domain adaptation. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. Phoenix, Arizona, USA: AAAI Press, 2016. 3597-3603 |
[91] |
Wang J D, Chen Y Q, Hao S J, Feng W J, Shen Z Q. Balanced distribution adaptation for transfer learning. In: Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM). New Orleans, LA, USA: IEEE, 2017. 1129-1134 |
[92] |
Zhang X S, Zhuang Y, Wang W, Pedrycz W. Transfer boosting with synthetic instances for class imbalanced object recognition. IEEE Transactions on Cybernetics, 2018, 48(1):357-370 doi: 10.1109/TCYB.2016.2636370 |
[93] |
Saenko K, Kulis B, Fritz M, Darrell T. Adapting visual category models to new domains. In: Proceedings of the 11th European Conference on Computer Vision. Crete, Greece: Springer-Verlag, 2010. 213-226 |
[94] |
Davis J V, Kulis B, Jain P, Sra S, Dhillon I S. Information-theoretic metric learning. In: Proceedings of the 24th International Conference on Machine Learning. Corvalis, Oregon, USA: ACM, 2007. 209-216 |
[95] |
Hoffman J, Darrell T, Saenko K. Continuous manifold based adaptation for evolving visual domains. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014. 867-874 |
[96] |
Xu J L, Ramos S, Vázquez D, López A M. Domain adaptation of deformable part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(12):2367-2380 doi: 10.1109/TPAMI.2014.2327973 |
[97] |
Zhang X, Yu F X, Chang S F, Wang S J. Deep transfer network: unsupervised domain adaptation. arXiv: 1503.00591, 2015. |
[98] |
Yang X S, Zhang T Z, Xu C S, Yang M H. Boosted Multifeature learning for cross-domain transfer. ACM Transactions on Multimedia Computing, Communications, and Applications, 2015, 11(3): Article No. 35 |
[99] |
Long M, Cao Y, Wang J, Jordan M I. Unsupervised domain adaptation with residual transfer networks. In: Proceedings of the 32th International Conference on Machine Learning. Lille, France: ACM, 2015. 97-105 |
[100] |
Yan K, Kou L, Zhang D. Learning domain-invariant subspace using domain features and independence maximization. arXiv: 1603.04535, 2016. |
[101] |
Long M S, Zhu H, Wang J M, Jordan M I. Unsupervised domain adaptation with residual transfer networks. In: Proceedings of the 30th Conference on Neural Information Processing Systems. Barcelona, Spain: ACM Press 2016. 136-144 |
[102] |
Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, et al. Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 2016, 17(1):2096-2030 http://d.old.wanfangdata.com.cn/Periodical/chxb201712005 |
[103] |
Long M S, Zhu H, Wang J M, Jordan M I. Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: ACM, 2017. 2208-2217 |
[104] |
Chen Y H, Li W, Sakaridis C, Dai D X, Van Gool L. Domain adaptive faster R-CNN for object detection in the wild. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, USA: IEEE, 2018. 3339-3348 |
[105] |
Inoue N, Furuta R, Yamasaki T, Aizawa K. Cross-domain weakly-supervised object detection through progressive domain adaptation. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, USA: IEEE, 2018. 5001-5009. DOI: 10.1109/CVPR.2018.00525 |
[106] |
Kulis B, Saenko K, Darrell T. What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, RI, USA: IEEE, 2011. 1785-1792 |
[107] |
Li W, Duan L X, Xu D, Tsang I W. Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(6):1134-1148 doi: 10.1109/TPAMI.2013.167 |
[108] |
Xiao M, Guo Y H. Feature space independent semi-supervised domain adaptation via kernel matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1):54-66 doi: 10.1109/TPAMI.2014.2343216 |
[109] |
Tsai Y H H, Yeh Y R, Wang Y C F. Learning cross-domain landmarks for heterogeneous domain adaptation. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 5081-5090 |
[110] |
Zhao P L, Hoi S C H, Wang J L, Li B. Online transfer learning. Artificial Intelligence, 2014, 216:76-102 doi: 10.1016/j.artint.2014.06.003 |
[111] |
Zhan Y S, Taylor M E. Online transfer learning in reinforcement learning domains. arXiv: 1507.00436, 2015. |
[112] |
Zhang X S, Zhuang Y, Wang W, Pedrycz W. Online feature transformation learning for cross-domain object category recognition. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(7):2857-2871 |
[113] |
Ghifary M, Kleijn W B, Zhang M J, Balduzzi D. Domain generalization for object recognition with multi-task autoencoders. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 2551-2559 |
[114] |
Fan J P, Zhao T Y, Kuang Z Z, Zheng Y, Zhang J, Yu J, et al. HD-MTL:hierarchical deep multi-task learning for large-scale visual recognition. IEEE Transactions on Image Processing, 2017, 26(4):1923-1938 doi: 10.1109/TIP.2017.2667405 |
[115] |
Torralba A, Murphy K P, Freeman W T. Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5):854-869 doi: 10.1109/TPAMI.2007.1055 |
[116] |
Li X, Zhao L M, Wei L N, Yang M H, Wu F, Zhuang Y T, et al. Deepsaliency:multi-task deep neural network model for salient object detection. IEEE Transactions on Image Processing, 2016, 25(8):3919-3930 doi: 10.1109/TIP.2016.2579306 |
[117] |
Kalogeiton V, Weinzaepfel P, Ferrari V, Schmid C. Joint learning of object and action detectors. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 2001-2010 |
[118] |
Chu W Q, Liu Y, Shen C, Cai D, Hua X S. Multi-task vehicle detection with region-of-interest voting. IEEE Transactions on Image Processing, 2018, 27(1):432-441 doi: 10.1109/TIP.2017.2762591 |
[119] |
Lu X, Wang Y N, Zhou X Y, Zhang Z J, Ling Z G. Traffic sign recognition via multi-modal tree-structure embedded multi-task learning. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(4):960-972 doi: 10.1109/TITS.2016.2598356 |
[120] |
Zhang Y X, Du B, Zhang L P, Liu T L. Joint sparse representation and multitask learning for hyperspectral target detection. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2):894-906 doi: 10.1109/TGRS.2016.2616649 |
[121] |
Zhang T Z, Xu C S, Yang M H. Multi-task correlation particle filter for robust object tracking. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017. 4819-4827 |
[122] |
Lim J J, Salakhutdinov R, Torralba A. Transfer learning by borrowing examples for multiclass object detection. In: Proceedings of 24th International Conference on Neural Information Processing Systems. Granada, Spain: ACM, 2011. 118-126 |
[123] |
Malisiewicz T, Gupta A, Efros A A. Ensemble of exemplar-SVMs for object detection and beyond. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 89-96 |
[124] |
Aytar Y, Zisserman A. Enhancing exemplar SVMs using part level transfer regularization. In: Proceedings of the 2012 British Machine Vision Conference. Guildford, UK: BMVA, 2012. 1-11 |
[125] |
Aytar Y, Zisserman A. Part level transfer regularization for enhancing exemplar SVMs. Computer Vision and Image Understanding, 2015, 138:114-123 doi: 10.1016/j.cviu.2015.04.004 |
[126] |
Oquab M, Bottou L, Laptev I, Sivic J. Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014. 1717-1724 |
[127] |
Hoffman J, Gupta S, Leong J, Guadarrama S, Darrell T. Cross-modal adaptation for RGB-D detection. In: Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE, 2016. 5032-5039 |
[128] |
Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? In: Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Quebec, Canada: ACM, 2014. 3320-3328 |
[129] |
Tang Y J, Wu B, Peng L R, Liu C S. Semi-supervised transfer learning for convolutional neural network based Chinese character recognition. In: Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Kyoto, Japan: IEEE, 2017. 441-447 |
[130] |
Christodoulidis S, Anthimopoulos M, Ebner L, Christe A, Mougiakakou S. Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE Journal of Biomedical and Health Informatics, 2017, 21(1):76-84 doi: 10.1109/JBHI.2016.2636929 |
[131] |
Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422 doi: 10.1109/TPAMI.2011.239 |
[132] |
Hare S, Saffari A, Torr P H S. Struck: structured output tracking with kernels. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 263-270 |
[133] |
Milan A, Schindler K, Roth S. Multi-target tracking by discrete-continuous energy minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10):2054-2068 doi: 10.1109/TPAMI.2015.2505309 |
[134] |
Bolme D S, Beveridge J R, Draper B A, Lui Y M. Visual object tracking using adaptive correlation filters. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 2544-2550 |
[135] |
Henriques J F, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European Conference on Computer Vision. Florence, Italy: Springer, 2012. 702-715 |
[136] |
Henriques J F, Caseiro R, Martins P, Batista J. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596 doi: 10.1109/TPAMI.2014.2345390 |
[137] |
Danelljan M, Khan F S, Felsberg M, Van De Weijer J. Adaptive color attributes for real-time visual tracking. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014. 1090-1097 |
[138] |
Wang N Y, Yeung D Y. Learning a deep compact image representation for visual tracking. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, USA: ACM, 2013. 809-817 |
[139] |
Wang N Y, Li S Y, Gupta A, Yeung D Y. Transferring rich feature hierarchies for robust visual tracking. arXiv: 1501.04587v2, 2015. |
[140] |
Wang L J, Ouyang W L, Wang X G, Lu H C. Visual tracking with fully convolutional networks. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 3119-3127 |
[141] |
Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 4293-4302 |
[142] |
Cui Z, Xiao S T, Feng J S, Yan S C. Recurrently target-attending tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016. 1449-1458 |