firstname.lastname@example.org | MRC Innovation Fellow
- Ziyun Cai, Yang Long, Ling Shao (2018), “Adaptive RGB Image Recognition by Visual-Depth Embedding”, IEEE Transactions on Image Processing (TIP), DOI: 10.1109/TIP.2018.2806839. (SJR Q1, Impact Factor: 4.828)
- Yang Long, Li Liu, Fumin Shen, Ling Shao, and Xuelong Li (2017), “Zero-shot Learning Using Synthesised
Unseen Visual Data with Diffusion Regularisation”, IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI), DOI: 10.1109/TPAMI.2017.2762295. (SJR Q1, Impact Factor: 8.329)
- Yang Long, Fan Zhu, Ling Shao (2017), “Face Recognition with A Small Occluded Training Set Using Spatial
and Statistical Pooling”, Information Sciences, Elsevier, DOI: 10.1016/j.ins.2017.10.042. (SJR Q1, Impact
- Haofeng Zhang, Li Liu, Yang Long, Ling Shao (2017), “Unsupervised Deep Hashing with Pseudo Labels for
Scalable Image Retrieval”, IEEE Transactions on Image Processing (TIP), DOI: 10.1109/TIP.2017.2781422.
(SJR Q1, Impact Factor: 4.828)
- Yang Long, Fan Zhu and Ling Shao (2016), “Recognising Occluded Multi-view Actions Using Local Nearest
Neighbour Embedding”, Computer Vision and Image Understanding (CVIU), DOI: 10.1016/j.cviu.2015.06.003.
(SJR Q1, Impact Factor: 2.498)
- Yi Zhu, Yang Long, Yu Guan, Shawn Newsam, Ling Shao (2018), “Towards Universal Representation for Unseen Action Recognition”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE (Core A*, h5-index: 158).
- Yang Long, Li, Liu, Yumin Shen, and Ling Shao (2018), “Towards Affordable Semantic Searching: Zero-shot
Retrieval via Dominant Attributes”, AAAI Conference on Arti1cial Intelligence (AAAI), New Orleans,
USA, 2018. DOI: (Core A*, h5-index: 56)
- Yang Long, and Ling Shao (2017), “Learning to Recognise Unseen Classes by A Few Similes”, In Proceedings
of the ACM International Conference on Multimedia (ACM-MM), pp. 636-644. ACM. DOI: 10.1145/3123266.
3123323 (Core A*, h5-index: 44)
- Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding and Jungong Han (2017), “From Zero-Shot
Learning to Conventional Supervised Classi1cation: Unseen Visual Data Synthesis”, In Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. DOI: 10.1109/CVPR.2017.653
(Core A*, h5-index: 158).
- Yang Long and Ling Shao (2017), “Describing Unseen Classes by Exemplars: Zero-shot Learning Using
Grouped Simile Ensemble”, In Proceedings of the IEEE Winter Conference on Applications of Computer
Vision (WACV), IEEE. DOI: 10.1109/WACV.2017.106 (Core A, h5-index: 31)
- Yang Long, Li Liu and Ling Shao (2017), “Towards Fine-grained Open Zero-shot Learning: Inferring Unseen
Visual Features from Attributes”, In Proceedings of the IEEE Winter Conference on Applications of Computer
Vision (WACV), pp. 907-915, IEEE. DOI: 10.1109/WACV.2017.106 (Core A, h5-index: 31)
- Yang Long, Li Liu and Ling. Shao (2016), “Attribute Embedding with Visual-Semantic Ambiguity Removal for
Zero-shot Learning”, In Proceedings of the British Machine Vision Conference (BMVC). DOI: 10.5244/C.30.40
Yang Long is currently a Research Fellow with OpenLab, School of Computing, Newcastle University. He received his Ph. D. degree in Computer Vision and Machine Learning from the Department of Electronic and Electrical Engineering, the University of Sheffield, UK, in 2017. He received the M.Sc. degree from the same institution, in 2014. His research interests include Artificial Intelligence, Machine Learning, Computer Vision, Deep Learning, Zero-shot Learning, with focus on Transparent AI for Healthcare Data Science. He has authored/co-authored papers in refereed journals/conferences such as IEEE TPAMI, TIP, CVPR, AAAI and ACM MM, and holds 1 Chinese patent. He is also a regular reviewer for leading journals and conferences. He is a member of the British Computer Society, ACM, and IEEE. Full CV can be found HERE.
Invited Key Note Talk for BMVC 2018 workshop: Cross-Domain Sketch Analysis Using Deep Learning Methods (CDSA 2018)
Another paper accepted to IEEE TIP 2018 (IF 5.071), 1 Elsevier Information Sciences (IF 4.305), 1 IET Electronics Letters (IF 1.232).
1 paper accepted to IEEE TIP 2018.
1 paper accepted by CVPR 2018.
MRC Fellowship granted (343,713.51 GBP).
1 paper accepted to AAAI 2018.
1 paper accepted to IEEE TPAMI 2017.
Full Publication List
Journal Papers All of the papers below are published in leading journals, including TPAMI (IEEE Transactions
on Pattern Analysis and Machine Intelligence) that is widely acknowledged as the flagship journal in the f1eld of
Arti1cial Intelligence (Google Scholar top-100 for h5-index across all academic disciplines).
Conference Papers As a discipline computer science is distinctive in its conferences require submission
of full papers which are all double-blind peer-reviewed by three or more reviewers. The acceptance rates for
the venues below are all less than 25%. Note: “Core” rankings relate to the “CORE Conference Ranking” of the
Computing Research and Education Association of Australasia. A* (“2agship conference, a leading venue in a
discipline area”); A (“excellent conference, and highly respected in a discipline area”); B (“good conference, and
well regarded in a discipline area”).