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Assistant Professor in the Department of Computer ScienceMCS 1026+44 (0) 191 33 48133

Biography

Yang Long is an Assistant Professor in the Department of Computer Science, Durham University. He is also an MRC Innovation Fellow aiming to design scalable AI solutions for large-scale healthcare applications. His research background is in the highly interdisciplinary field of Computer Vision and Machine Learning. While he is passionate about unveiling the black-box of AI brain and transferring the knowledge to seek Scalable, Interactable, Interpretable, and sustainable solutions for other disciplinary researches, e.g. physical activity, mental health, design, education, security, and geoengineering. He has authored/co-authored 20+ top-tier papers in refereed journals/conferences such as IEEE TPAMI, TIP, CVPR, AAAI, and ACM MM, and holds a patent and a Chinese National Grant.

Research groups

  • Innovative Computing

Publications

Conference Paper

  • Wang, Junyan, Hu, Bingzhang, Long, Yang & Guan, Yu (2019), Order Matters: Shuffling Sequence Generation for Video Prediction, BMVC.
  • Huang, Yan, Long, Yang & Wang, Liang (2019), Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding, Thirty-Second AAAI Conference on Artificial Intelligence. Thirty-Second AAAI Conference on Artificial Intelligence, 5342-5349.
  • Mao, Huaqi, Zhang, Haofeng, Long, Yang, Wang, Shidong & Yang, Longzhi (2019), A General Transductive Regularizer for Zero-Shot Learning, BMVC.
  • Guan, Congying, Qin, Shengfeng, Ling, Wessie & Long, Yang (2018), Enhancing apparel data based on fashion theory for developing a novel apparel style recommendation system, World Conference on Information Systems and Technologies Springer, Cham. 31-40.
  • Long, Yang, Tan, Yao, Organisciak, Daniel, Yang, Longzhi & Shao, Ling (2018), Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification, BMVC.
  • Cai, Ziyuni, Long, Yang & Shao, Ling (2018), Adaptive Visual-Depth Fusion Transfer, ACCV.
  • Zhu, Yi, Long, Yang, Guan, Yu, Newsam, Shawn & Shao, Ling (2018), Towards Universal Representation for Unseen Action Recognition, IEEE Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) IEEE; CVF; IEEE Comp Soc. 345 E 47TH ST, NEW YORK, NY 10017 USA, IEEE, 9436-9445.
  • Long, Yang, Liu, Li, Shen, Yuming & Shao, Ling (2018), Towards affordable semantic searching: Zero-shot retrieval via dominant attributes, Thirty-Second AAAI Conference on Artificial Intelligence. Thirty-Second AAAI Conference on Artificial Intelligence, 7210-7217.
  • Long, Yang & Shao, Ling (2017), Learning to recognise unseen classes by a few similes, Proceedings of the 25th ACM international conference on Multimedia ACM. 636-644.
  • Long, Yang, Liu, Li & Shao, Ling (2017), Towards fine-grained open zero-shot learning: Inferring unseen visual features from attributes, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) IEEE. 944-952.
  • Long, Yang & Shao, Ling (2017), Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble, 2017 IEEE winter conference on applications of computer vision (WACV) IEEE. 907-915.
  • Long, Yang, Liu, Li, Shao, Ling, Shen, Fumin, Ding, Guiguang & Han, Jungong (2017), From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis, Computer Vision and Pattern Recognition IEEE.
  • Long, Yang, Liu, Li & Shao, Ling (2016), Attribute embedding with visual-semantic ambiguity removal for zero-shot learning, BMVC.

Doctoral Thesis

  • Long, Yang (2017). Zero-shot Image Classification. University of Sheffield. PhD.

Journal Article

Supervision students