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Gao, J. and Wang, Q. and Xing, J. and Ling, H. and Hu, W. and Maybank, Stephen J. (2020) Tracking-by-fusion via Gaussian Process Regression extended to transfer learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (4), pp. 939-955. ISSN 0162-8828.
Wang, Q. and Teng, Z. and Xing, J. and Gao, J. and Hu, W. and Maybank, Stephen J. (2018) Learning attentions: residual attentional Siamese Network for high performance online visual tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition , ISSN 1063-6919.
Hu, W. and Gao, J. and Wu, O. and Du, J. and Maybank, Stephen J. (2018) Anomaly detection using local kernel density estimation and context-based regression. IEEE Transactions on Knowledge and Data Engineering 32 (2), pp. 218-233. ISSN 1041-4347.
Zhang, M. and Wang, Q. and Xing, J. and Gao, J. and Peng, P. and Hu, W. and Maybank, Stephen J. (2018) Visual tracking via spatially aligned correlation filters network. In: Ferrari, V. and Hebert, M. and Sminchisescu, C. and Weiss, Y. (eds.) Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part III. Lecture Notes in Computer Science 11207. Springer, pp. 484-500. ISBN 9783030012182.
Wang, Q. and Zhang, M. and Xing, J. and Gao, J. and Hu, W. and Maybank, Stephen J. (2018) Do not lose the details: reinforced representation learning for high performance visual tracking. In: Lang, J. (ed.) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence. ISBN 9780999241127.
Hu, W. and Gao, J. and Xing, J. and Zhang, C. and Maybank, Stephen J. (2017) Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (1), pp. 172-188. ISSN 0162-8828.
Chang, R.S. and Gao, J. and Gruhn, V. and He, J. and Roussos, George and Tsai, W.-T. (2013) Mobile cloud computing research: issues, challenges and needs. In: UNSPECIFIED (ed.) IEEE Seventh International Symposium on Service-Oriented System Engineering. Washington, USA: IEEE Computer Society, pp. 442-453. ISBN 9781467356596.
Tsetsos, Konstantinos and Gao, J. and McClelland, J.L. and Usher, Marius (2012) Using time-varying evidence to test models of decision dynamics: bounded diffusion vs. the leaky competing accumulator model. Frontiers in Neuroscience 6 (79), pp. 6-79. ISSN 1662-453X.