Staff profile

Yang Long is an Associate Professor in the Department of Computer Science, Durham University. He is also an IEEE Senior Member (SMIEEE) and 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 100+ top-tier papers in refereed journals/conferences such as IEEE TPAMI, TIP, CVPR, AAAI, and ACM MM.

Conference Paper

• SID-NERF: Few-Shot Nerf Based on Scene Information Distribution
  
  Li, Y., Wan, F., & Long, Y. (2024). SID-NERF: Few-Shot Nerf Based on Scene Information Distribution. In 2024 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE. https://doi.org/10.1109/icme57554.2024.10687533
• Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance
  
  Aduragba, T. O., Yu, J., Cristea, A. I., & Long, Y. (2023). Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance. In WWW ’23: Proceedings of the ACM Web Conference 2023 (pp. 3928-3936). ACM. https://doi.org/10.1145/3543507.3583877
• Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding
  
  Huang, Y., Long, Y., & Wang, L. (2019). Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding. In Thirty-Second AAAI Conference on Artificial Intelligence ; proceedings. (pp. 5342-5349). Thirty-Second AAAI Conference on Artificial Intelligence.
• Order Matters: Shuffling Sequence Generation for Video Prediction
  
  Wang, J., Hu, B., Long, Y., & Guan, Y. (2019). Order Matters: Shuffling Sequence Generation for Video Prediction. Presented at BMVC.
• A General Transductive Regularizer for Zero-Shot Learning
  
  Mao, H., Zhang, H., Long, Y., Wang, S., & Yang, L. (2019). A General Transductive Regularizer for Zero-Shot Learning. Presented at BMVC.
• Towards affordable semantic searching: Zero-shot retrieval via dominant attributes
  
  Long, Y., Liu, L., Shen, Y., & Shao, L. (2018). Towards affordable semantic searching: Zero-shot retrieval via dominant attributes. In Thirty-Second AAAI Conference on Artificial Intelligence ; proceedings. (pp. 7210-7217). Thirty-Second AAAI Conference on Artificial Intelligence.
• Adaptive Visual-Depth Fusion Transfer
  
  Cai, Z., Long, Y., & Shao, L. (2018). Adaptive Visual-Depth Fusion Transfer. Presented at ACCV.
• Enhancing apparel data based on fashion theory for developing a novel apparel style recommendation system
  
  Guan, C., Qin, S., Ling, W., & Long, Y. (2018). Enhancing apparel data based on fashion theory for developing a novel apparel style recommendation system. Presented at World Conference on Information Systems and Technologies Springer, Cham.
• Towards Universal Representation for Unseen Action Recognition
  
  Zhu, Y., Long, Y., Guan, Y., Newsam, S., & Shao, L. (2018). Towards Universal Representation for Unseen Action Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 9436-9445). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr.2018.00983
• Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification
  
  Long, Y., Tan, Y., Organisciak, D., Yang, L., & Shao, L. (2018). Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification. Presented at BMVC.
• From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis
  
  Long, Y., Liu, L., Shao, L., Shen, F., Ding, G., & Han, J. (2017). From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis. Presented at Computer Vision and Pattern Recognition IEEE.
• Learning to recognise unseen classes by a few similes
  
  Long, Y., & Shao, L. (2017). Learning to recognise unseen classes by a few similes. Presented at Proceedings of the 25th ACM international conference on Multimedia ACM.
• Towards fine-grained open zero-shot learning: Inferring unseen visual features from attributes
  
  Long, Y., Liu, L., & Shao, L. (2017). Towards fine-grained open zero-shot learning: Inferring unseen visual features from attributes. Presented at 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) IEEE.
• Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble
  
  Long, Y., & Shao, L. (2017). Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble. Presented at 2017 IEEE winter conference on applications of computer vision (WACV) IEEE.
• Attribute embedding with visual-semantic ambiguity removal for zero-shot learning
  
  Long, Y., Liu, L., & Shao, L. (2016). Attribute embedding with visual-semantic ambiguity removal for zero-shot learning. Presented at BMVC.

Doctoral Thesis

• Zero-shot Image Classification
  
  Long, Y. (2017). Zero-shot Image Classification [Thesis]. University of Sheffield.

Journal Article

• FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off
  
  Zhang, T., Wan, F., Duan, H., Tong, K. W., Deng, J., & Long, Y. (2025). FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off. Knowledge-Based Systems, 317, Article 113393. https://doi.org/10.1016/j.knosys.2025.113393
• Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields
  
  Miao, X., Duan, H., Bai, Y., Shah, T., Song, J., Long, Y., Ranjan, R., & Shao, L. (2025). Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence. Advance online publication. https://doi.org/10.1109/TPAMI.2025.3535916
• The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces
  
  Ma, W., Ma, T., Organisciak, D., Waide, J. E. T., Meng, X., & Long, Y. (2025). The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces. Electronics, 14(3), Article 508. https://doi.org/10.3390/electronics14030508
• CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video
  
  Miao, X., Bai, Y., Duan, H., Wan, F., Huang, Y., Long, Y., & Zheng, Y. (2024). CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video. Pattern Recognition, 156, Article 110729. https://doi.org/10.1016/j.patcog.2024.110729
• Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination
  
  Jiang, C., Wang, S., Long, Y., Li, Z., Zhang, H., & Shao, L. (2024). Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence. Advance online publication. https://doi.org/10.1109/tpami.2024.3487631
• Rules for Expectation: Learning to Generate Rules via Social Environment Modeling
  
  Pu, J., Duan, H., Zhao, J., & Long, Y. (2024). Rules for Expectation: Learning to Generate Rules via Social Environment Modeling. IEEE Transactions on Circuits and Systems for Video Technology, 34(8), 6874-6887. https://doi.org/10.1109/tcsvt.2023.3334526
• Leveraging Self-Distillation and Disentanglement Network to Enhance Visual–Semantic Feature Consistency in Generalized Zero-Shot Learning
  
  Liu, X., Wang, C., Yang, G., Wang, C., Long, Y., Liu, J., & Zhang, Z. (2024). Leveraging Self-Distillation and Disentanglement Network to Enhance Visual–Semantic Feature Consistency in Generalized Zero-Shot Learning. Electronics, 13(10), Article 1977. https://doi.org/10.3390/electronics13101977
• Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval
  
  Ma, T., Organisciak, D., Ma, W., & Long, Y. (2024). Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval. Electronics, 13(9), Article 1660. https://doi.org/10.3390/electronics13091660
• Wearable-based behaviour interpolation for semi-supervised human activity recognition
  
  Duan, H., Wang, S., Ojha, V., Wang, S., Huang, Y., Long, Y., Ranjan, R., & Zheng, Y. (2024). Wearable-based behaviour interpolation for semi-supervised human activity recognition. Information Sciences, 665, Article 120393. https://doi.org/10.1016/j.ins.2024.120393
• DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
  
  Miao, X., Bai, Y., Duan, H., Huang, Y., Wan, F., Xu, X., Long, Y., & Zheng, Y. (2024). DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume. IEEE Transactions on Circuits and Systems for Video Technology, 34(4), 2564-2576. https://doi.org/10.1109/tcsvt.2023.3305776
• MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning
  
  Yang, F., Li, X., Duan, H., Xu, F., Huang, Y., Zhang, X., Long, Y., & Zheng, Y. (2024). MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning. IEEE Journal of Biomedical and Health Informatics, 28(2), 858-869. https://doi.org/10.1109/jbhi.2023.3336726
• Feature fine-tuning and attribute representation transformation for zero-shot learning
  
  Pang, S., He, X., Hao, W., & Long, Y. (2023). Feature fine-tuning and attribute representation transformation for zero-shot learning. Computer Vision and Image Understanding, 236, Article 103811. https://doi.org/10.1016/j.cviu.2023.103811
• The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition
  
  Li, T., Wen, Z., Long, Y., Hong, Z., Zheng, S., Yu, L., Chen, B., Yang, X., & Shao, L. (2023). The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 13730-13748. https://doi.org/10.1109/tpami.2023.3294505
• Dynamic Unary Convolution in Transformers
  
  Duan, H., Long, Y., Wang, S., Zhang, H., Willcocks, C. G., & Shao, L. (2023). Dynamic Unary Convolution in Transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 12747-12759. https://doi.org/10.1109/tpami.2022.3233482
• Deconfounding Causal Inference for Zero-shot Action Recognition
  
  Wang, J., Jiang, Y., Long, Y., Sun, X., Pagnucco, M., & Song, Y. (2023). Deconfounding Causal Inference for Zero-shot Action Recognition. IEEE Transactions on Multimedia, 26, 3976-3986. https://doi.org/10.1109/tmm.2023.3318300
• EfficientTDNN: Efficient Architecture Search for Speaker Recognition
  
  Wang, R., Wei, Z., Duan, H., Ji, S., Long, Y., & Hong, Z. (2022). EfficientTDNN: Efficient Architecture Search for Speaker Recognition. IEEE/ACM/Transactions/on/Audio,/Speech,/and/Language/Processing, 30, 2267-2279. https://doi.org/10.1109/taslp.2022.3182856
• Kernelized distance learning for zero-shot recognition
  
  Zarei, M. R., Taheri, M., & Long, Y. (2021). Kernelized distance learning for zero-shot recognition. Information Sciences, 580, 801-818. https://doi.org/10.1016/j.ins.2021.09.032
• Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
  
  Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2021). Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7327-7347. https://doi.org/10.1109/tpami.2021.3116668
• A plug-in attribute correction module for generalized zero-shot learning
  
  Zhang, H., Bai, H., Long, Y., Liu, L., & Shao, L. (2021). A plug-in attribute correction module for generalized zero-shot learning. Pattern Recognition, 112, Article 107767. https://doi.org/10.1016/j.patcog.2020.107767
• Modality independent adversarial network for generalized zero shot image classification
  
  Zhang, H., Wang, Y., Long, Y., Yang, L., & Shao, L. (2021). Modality independent adversarial network for generalized zero shot image classification. Neural Networks, 134, 11-22. https://doi.org/10.1016/j.neunet.2020.11.007
• Deep transductive network for generalized zero shot learning
  
  Zhang, H., Liu, L., Long, Y., Zhang, Z., & Shao, L. (2020). Deep transductive network for generalized zero shot learning. Pattern Recognition, 105, Article 107370. https://doi.org/10.1016/j.patcog.2020.107370
• Pseudo Distribution on Unseen Classes for Generalized Zero Shot Learning
  
  Zhang, H., Liu, J., Yao, Y., & Long, Y. (2020). Pseudo Distribution on Unseen Classes for Generalized Zero Shot Learning. Pattern Recognition Letters, 135, 451-458. https://doi.org/10.1016/j.patrec.2020.05.021
• A Probabilistic Zero-Shot Learning Method via Latent Nonnegative Prototype Synthesis of Unseen Classes
  
  Zhang, H., Mao, H., Long, Y., Yang, W., & Shao, L. (2020). A Probabilistic Zero-Shot Learning Method via Latent Nonnegative Prototype Synthesis of Unseen Classes. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 31(7), 2361-2375. https://doi.org/10.1109/tnnls.2019.2955157
• 2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling
  
  Angelini, F., Fu, Z., Long, Y., Shao, L., & Naqvi, S. M. (2020). 2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling. IEEE Transactions on Multimedia, 22(6), 1433-1446. https://doi.org/10.1109/tmm.2019.2944745
• Learning discriminative domain-invariant prototypes for generalized zero shot learning
  
  Wang, Y., Zhang, H., Zhang, Z., Long, Y., & Shao, L. (2020). Learning discriminative domain-invariant prototypes for generalized zero shot learning. Knowledge-Based Systems, 196, Article 105796. https://doi.org/10.1016/j.knosys.2020.105796
• Semantic combined network for zero-shot scene parsing
  
  Wang, Y., Zhang, H., Wang, S., Long, Y., & Yang, L. (2020). Semantic combined network for zero-shot scene parsing. IET Image Processing, 14(4), 757-765. https://doi.org/10.1049/iet-ipr.2019.0870
• A Joint Label Space for Generalized Zero-Shot Classification
  
  Li, J., Lan, X., Long, Y., Liu, Y., Chen, X., Shao, L., & Zheng, N. (2020). A Joint Label Space for Generalized Zero-Shot Classification. IEEE Transactions on Image Processing, 29, 5817-5831. https://doi.org/10.1109/tip.2020.2986892
• Depth Embedded Recurrent Predictive Parsing Network for Video Scenes
  
  Zhou, L., Zhang, H., Long, Y., Shao, L., & Yang, J. (2019). Depth Embedded Recurrent Predictive Parsing Network for Video Scenes. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4643-4654. https://doi.org/10.1109/tits.2019.2909053
• Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers
  
  Gao, Y., Long, Y., Guan, Y., Basu, A., Baggaley, J., & Ploetz, T. (2019). Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(1), Article 12. https://doi.org/10.1145/3314399
• Dual-verification network for zero-shot learning
  
  Zhang, H., Long, Y., Yang, W., & Shao, L. (2019). Dual-verification network for zero-shot learning. Information Sciences, 470, 43-57. https://doi.org/10.1016/j.ins.2018.08.048
• Zero-shot Hashing with orthogonal projection for image retrieval
  
  Zhang, H., Long, Y., & Shao, L. (2019). Zero-shot Hashing with orthogonal projection for image retrieval. Pattern Recognition Letters, 117, 201-209. https://doi.org/10.1016/j.patrec.2018.04.011
• Generic compact representation through visual-semantic ambiguity removal
  
  Long, Y., Guan, Y., & Shao, L. (2019). Generic compact representation through visual-semantic ambiguity removal. Pattern Recognition Letters, 117, 186-192. https://doi.org/10.1016/j.patrec.2018.04.024
• Adversarial unseen visual feature synthesis for Zero-shot Learning
  
  Zhang, H., Long, Y., Liu, L., & Shao, L. (2019). Adversarial unseen visual feature synthesis for Zero-shot Learning. Neurocomputing, 329, 12-20. https://doi.org/10.1016/j.neucom.2018.10.043
• Triple Verification Network for Generalized Zero-Shot Learning
  
  Zhang, H., Long, Y., Guan, Y., & Shao, L. (2019). Triple Verification Network for Generalized Zero-Shot Learning. IEEE Transactions on Image Processing, 28(1), 506-517. https://doi.org/10.1109/tip.2018.2869696
• Apparel-based deep learning system design for apparel style recommendation
  
  Guan, C., Qin, S., & Long, Y. (2019). Apparel-based deep learning system design for apparel style recommendation. International Journal of Clothing Science and Technology, 31(3), 376-389. https://doi.org/10.1108/ijcst-02-2018-0019
• Attribute relaxation from class level to instance level for zero-shot learning
  
  Zhang, H., Long, Y., & Zhao, C. (2018). Attribute relaxation from class level to instance level for zero-shot learning. Electronics Letters, 54(20), 1170-1172. https://doi.org/10.1049/el.2018.5027
• Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation
  
  Long, Y., Liu, L., Shen, F., Shao, L., & Li, X. (2018). Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2498-2512. https://doi.org/10.1109/tpami.2017.2762295
• Adaptive RGB Image Recognition by Visual-Depth Embedding
  
  Cai, Z., Long, Y., & Shao, L. (2018). Adaptive RGB Image Recognition by Visual-Depth Embedding. IEEE Transactions on Image Processing, 27(5), 2471-2483. https://doi.org/10.1109/tip.2018.2806839
• Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval
  
  Zhang, H., Liu, L., Long, Y., & Shao, L. (2018). Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval. IEEE Transactions on Image Processing, 27(4), 1626-1638. https://doi.org/10.1109/tip.2017.2781422
• Face recognition with a small occluded training set using spatial and statistical pooling
  
  Long, Y., Zhu, F., Shao, L., & Han, J. (2018). Face recognition with a small occluded training set using spatial and statistical pooling. Information Sciences, 430, 634-644. https://doi.org/10.1016/j.ins.2017.10.042
• Generic compact representation through visual-semantic ambiguity removal
  
  Long, Y., Guan, Y., & Shao, L. (2018). Generic compact representation through visual-semantic ambiguity removal. Pattern Recognition Letters, 186.
• Recognising occluded multi-view actions using local nearest neighbour embedding
  
  Long, Y., Zhu, F., & Shao, L. (2016). Recognising occluded multi-view actions using local nearest neighbour embedding. Computer Vision and Image Understanding, 144, 36-45.
• Zero-shot leaning and hashing with binary visual similes
  
  Zhang, H., Long, Y., & Shao, L. (n.d.). Zero-shot leaning and hashing with binary visual similes. Multimedia Tools and Applications, 1-19.