Apparel-based deep learning system design for apparel style recommendation
(2019)
Journal Article
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
Dr Yang Long's Outputs (13)
Adversarial unseen visual feature synthesis for Zero-shot Learning (2019)
Journal Article
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
Generic compact representation through visual-semantic ambiguity removal (2019)
Journal Article
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
Zero-shot Hashing with orthogonal projection for image retrieval (2019)
Journal Article
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
A General Transductive Regularizer for Zero-Shot Learning (2019)
Presentation / Conference Contribution
Mao, H., Zhang, H., Long, Y., Wang, S., & Yang, L. (2019, December). A General Transductive Regularizer for Zero-Shot Learning. Presented at BMVC
Order Matters: Shuffling Sequence Generation for Video Prediction (2019)
Presentation / Conference Contribution
Wang, J., Hu, B., Long, Y., & Guan, Y. (2019, December). Order Matters: Shuffling Sequence Generation for Video Prediction. Presented at BMVC
A Probabilistic Zero-Shot Learning Method via Latent Nonnegative Prototype Synthesis of Unseen Classes (2019)
Journal Article
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.2019.2955157Zero-shot learning (ZSL), a type of structured multioutput learning, has attracted much attention due to its requirement of no training data for target classes. Conventional ZSL methods usually project visual features into semantic space and assign l... Read More about A Probabilistic Zero-Shot Learning Method via Latent Nonnegative Prototype Synthesis of Unseen Classes.
Semantic combined network for zero-shot scene parsing (2019)
Journal Article
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.2019.0870Recently, image-based scene parsing has attracted increasing attention due to its wide application. However, conventional models can only be valid on images with the same domain of the training set and are typically trained using discrete and meaning... Read More about Semantic combined network for zero-shot scene parsing.
2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling (2019)
Journal Article
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.2019.2944745Human Action Recognition (HAR) for CCTV-oriented applications is still a challenging problem. Real-world scenarios HAR implementations is difficult because of the gap between Deep Learning data requirements and what the CCTV-based frameworks can offe... Read More about 2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling.
Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding (2019)
Presentation / Conference Contribution
Huang, Y., Long, Y., & Wang, L. (2019, December). Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding. Presented at Thirty-Second AAAI Conference on Artificial IntelligenceWord similarity and word relatedness are fundamental to natural language processing and more generally, understanding how humans relate concepts in semantic memory. A growing number of datasets are being proposed as evaluation benchmarks,however, the... Read More about Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding.
Depth Embedded Recurrent Predictive Parsing Network for Video Scenes (2019)
Journal Article
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.2019.2909053Semantic segmentation-based scene parsing plays an important role in automatic driving and autonomous navigation. However, most of the previous models only consider static images, and fail to parse sequential images because they do not take the spati... Read More about Depth Embedded Recurrent Predictive Parsing Network for Video Scenes.
Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers (2019)
Journal Article
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/10.1145/3314399Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify inf... Read More about Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers.
Dual-verification network for zero-shot learning (2019)
Journal Article
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