Key Research Areas
- Graph clustering and representation learning
- Subspace learning and low‑rank representation models
- Machine learning and deep learning methods
- Computer vision and image processing
- Light‑field reconstruction and view synthesis
- Image restoration (e.g. low‑light and underwater imaging)
- Diffusion and generative models for visual understanding
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Core Areas of Expertise
Dr. Liu expertise lies in machine learning and computer vision, with a focus on graph‑based representation learning, subspace and low‑rank modeling, and deep neural networks for visual data analysis. Her work advances clustering, image restoration, and view synthesis through robust, structure‑aware learning frameworks.
Recent Publications
2025
- Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering
- Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence
- Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation
2024
- Superpixel graph contrastive clustering with semantic-invariant augmentations for hyperspectral images
- Deep diversity-enhanced feature representation of hyperspectral images
2023
- Deep attention-guided graph clustering with dual self-supervision
- Light field reconstruction via deep adaptive fusion of hybrid lenses
- Semi-supervised subspace clustering via tensor low-rank representation
- EGRC-Net: Embedding-induced graph refinement clustering network
- Content-aware warping for view synthesis
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