All Outputs (8)

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning (2023)
Journal Article
Yang, F., Li, X., Duan, H., Xu, F., Huang, Y., Zhang, X., …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

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations acro... Read More about MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning.

Rules for Expectation: Learning to Generate Rules via Social Environment Modeling (2023)
Journal Article
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

Deconfounding Causal Inference for Zero-shot Action Recognition (2023)
Journal Article
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

Zero-shot action recognition (ZSAR) aims to recognize unseen action categories in the test set without corresponding training examples. Most existing zero-shot methods follow the feature generation framework to transfer knowledge from seen action cat... Read More about Deconfounding Causal Inference for Zero-shot Action Recognition.

Feature fine-tuning and attribute representation transformation for zero-shot learning (2023)
Journal Article
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

Zero-Shot Learning (ZSL) aims to generalize a pretrained classification model to unseen classes with the help of auxiliary semantic information. Recent generative methods are based on the paradigm of synthesizing unseen visual data from class attribu... Read More about Feature fine-tuning and attribute representation transformation for zero-shot learning.

DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume (2023)
Journal Article
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

Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. However, this assumption does not hold in dynamic objects in scenarios, l... Read More about DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume.

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition (2023)
Journal Article
Li, T., Wen, Z., Long, Y., Hong, Z., Zheng, S., Yu, L., …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

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio... Read More about The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition.

Improving Health Mention Classification through Emphasising Literal Meanings: a Study Towards Diversity and Generalisation for Public Health Surveillance (2023)
Presentation / Conference Contribution
Aduragba, T. O., Yu, J., Cristea, A. I., & Long, Y. (in press). Improving Health Mention Classification through Emphasising Literal Meanings: a Study Towards Diversity and Generalisation for Public Health Surveillance.

Dynamic Unary Convolution in Transformers (2023)
Journal Article
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

It is uncertain whether the power of transformer architectures can complement existing convolutional neural networks. A few recent attempts have combined convolution with transformer design through a range of structures in series, where the main cont... Read More about Dynamic Unary Convolution in Transformers.