- 🌱 A lecturer at the School of Computer Science, Guangdong Polytechnic Normal University.
- ⭐️ Current research focuses on multimodal data fusion for autonomous driving, remote sensing, and vehicle–infrastructure cooperation.
- 🏫 Received the Ph. D. degree in Electronics Information from Sun Yat-sen University.
- 📈 I maintain active and long‑term scientific collaborations with Sun Yat-sen University, University of Cambridge, Peng Cheng Laboratory, and Robert Gordon University.
- 💬 Ask me about my projects and scientific cooperation on [email protected](personal) and [email protected](institutional).
- 📝 Published List: Google Scholar
- InScope Dataset [Information Fusion'2026] Released
- A large-scale dataset (InScope) featuring multi-position LiDARs in a real-world setting is presented in this paper, specifically developed to address the current research gap concerning occlusion challenges within the I2I perception systems in open traffic scenarios.
- Confidence‑V2X [ADVEI'2026] Released
- This paper presents Confidence‑V2X, a confidence‑aware cooperative perception framework designed to jointly optimize object detection performance and communication efficiency in V2X systems.
- CaPaT [IEEE GRSL'2025] Holding
- Unlike existing networks that employ weight-sharing or indirect interaction strategies, the CaPaT introduces a direct feature interaction paradigm that significantly improves the transfer efficiency of feature fusion while reducing the number of model parameters.
- FusDreamer [IEEE TGRS'2025] Released
- World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the RS field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer).
- MBFormer [IEEE TCSVT'2023] Holding
- This paper is the first time that dynamic region-aware convolution is introduced for spatial guide mask generation, and the acquired mask is used as an elevation salience agent to guide another branch for spatial contextual-aware information exploration.
- AM3Net_Multimodal_Data_Fusion [IEEE TCSVT'2022] Released
- Proposing multimodal data fusion methods that pay more attention to the specificity of HSI spectral channels and the complementarity of HSI and LiDAR spatial information and consider more feature transmission processes among different modalities collaboratively.
- ASPCNet_HSIC [Neurocomuting'2022] Released
- This paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels based on an enlarged receptive field. It could adaptively change according to the inconsistent semantic information of HSIs.
- TPS-HSIC [IEEE JSTARS'2019] Released
- This paper introduces a new feature extraction method called TPS into HSI to address the layer-separation problem.
- KNNRS-HSIC [IEEE JSTARS'2018] Released
- To further explore the optimal representations of superpixels, the KNNRS method based on two k selection rules is proposed to find the most representative training and test samples.
- CCJSR [IEEE GRSL'2018] Released
- A hyperspectral image classification method via fusing correlation coefficient and joint sparse representation.
- Cimy_PPtools
- This is a Python toolbox that supports data preprocessing for tasks such as hyperspectral classification and fusion, as well as model saving and result visualization.
| 🐍 Python | 📐 MATLAB | 📜 Shell |
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