T
Ting Liu Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Researcher
faculty
27 h-index
204 pubs
3,101 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Ting Liu's research focuses on the application of advanced machine learning techniques, particularly deep learning, to address complex problems in computer vision and medical imaging. Their work has explored areas such as image segmentation, object detection, and representation learning for tasks like person re-identification and medical image analysis.
Recent publications demonstrate a focus on developing novel deep learning architectures and training methodologies. This includes work on multi-attention mechanisms for segmentation tasks (MALUNet), unsupervised learning approaches for depth estimation, and meta-transfer learning for video object segmentation. Several publications also investigate improving the robustness and generalizability of models, particularly in person re-identification, by addressing issues like illumination variations and synthetic data utilization.
Liu's scholarship metrics indicate a significant body of work, with 204 total publications and an h-index of 27, reflecting a highly cited researcher. Key collaborators include Rebecca Logsdon Muenich and Arghajeet Saha, both from the University of Arkansas at Fayetteville, with whom shared publications have been produced.
Metrics
- h-index: 27
- Publications: 204
- Citations: 3,101
Selected Publications
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Creation of a Landslide Susceptibility Map Using Short‐Term Data From the July 2018 Heavy Rainfall in Southern Hiroshima Prefecture (2026)
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Machine learning-based identification of animal feeding operations in the United States on a parcel-scale (2025)
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Modeling green infrastructure as a flood mitigation strategy in an urban coastal area (2024)
Collaboration Network
Top Collaborators
Yuzhuo Fu
Shanghai Jiao Tong University (CN)
23 shared publications
- MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
- Less Is More: Learning from Synthetic Data with Fine-Grained Attributes for Person Re-Identification
- Deep multimodal representation learning for generalizable person re-identification
- Rethinking Illumination for Person Re-Identification: A Unified View
- LAMM: Label Alignment for Multi-Modal Prompt Learning
Showing 5 of 23 shared publications
Suncheng Xiang
Shanghai Jiao Tong University (CN)
18 shared publications
- MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
- Less Is More: Learning from Synthetic Data with Fine-Grained Attributes for Person Re-Identification
- Deep multimodal representation learning for generalizable person re-identification
- Rethinking Illumination for Person Re-Identification: A Unified View
- LAMM: Label Alignment for Multi-Modal Prompt Learning
Showing 5 of 18 shared publications
Mingye Xie
Shanghai Jiao Tong University (CN)
9 shared publications
- MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
- LAMM: Label Alignment for Multi-Modal Prompt Learning
- MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation
- GIST: Improving Parameter Efficient Fine-Tuning via Knowledge Interaction
- Spatial Attention Guided Local Facial Attribute Editing
Showing 5 of 9 shared publications
Jingsheng Gao
Shanghai Jiao Tong University (CN)
7 shared publications
- LAMM: Label Alignment for Multi-Modal Prompt Learning
- Rethinking Person Re-Identification via Semantic-based Pretraining
- Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
- GIST: Improving Parameter Efficient Fine-Tuning via Knowledge Interaction
- GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction
Showing 5 of 7 shared publications
Mengyuan Guan
6 shared publications
- Less Is More: Learning from Synthetic Data with Fine-Grained Attributes for Person Re-Identification
- Rethinking Illumination for Person Re-Identification: A Unified View
- Rethinking Person Re-Identification via Semantic-Based Pretraining
- Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
- Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
Showing 5 of 6 shared publications
Jiacheng Ruan
Shanghai Jiao Tong University (CN)
5 shared publications
- MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
- LAMM: Label Alignment for Multi-Modal Prompt Learning
- MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation
- Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
- GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction
Guanjie You
National University of Defense Technology (CN)
4 shared publications
- Less Is More: Learning from Synthetic Data with Fine-Grained Attributes for Person Re-Identification
- Rethinking Illumination for Person Re-Identification: A Unified View
- Taking A Closer Look at Synthesis: Fine-Grained Attribute Analysis for Person Re-Identification
- Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
Zefang Yu
Shanghai Jiao Tong University (CN)
4 shared publications
- Deep multimodal representation learning for generalizable person re-identification
- LAMM: Label Alignment for Multi-Modal Prompt Learning
- GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction
- Learning to Floorplan like Human Experts via Reinforcement Learning
Hao Chen
Shanghai Jiao Tong University (CN)
3 shared publications
- Deep multimodal representation learning for generalizable person re-identification
- Rethinking Person Re-Identification via Semantic-Based Pretraining
- Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
Hong Zhu
Shanghai Stomatological Hospital (CN)
3 shared publications
- An IL-6/STAT3/MR/FGF21 axis mediates heart-liver cross-talk after myocardial infarction
- NCOR1 maintains the homeostasis of vascular smooth muscle cells and protects against aortic aneurysm
- Osteoblast MR deficiency protects against adverse ventricular remodeling after myocardial infarction
Lin‐Juan Du
Shanghai Jiao Tong University (CN)
3 shared publications
- An IL-6/STAT3/MR/FGF21 axis mediates heart-liver cross-talk after myocardial infarction
- NCOR1 maintains the homeostasis of vascular smooth muscle cells and protects against aortic aneurysm
- Osteoblast MR deficiency protects against adverse ventricular remodeling after myocardial infarction
Jianyong Sun
Shanghai Ninth People's Hospital (CN)
3 shared publications
- An IL-6/STAT3/MR/FGF21 axis mediates heart-liver cross-talk after myocardial infarction
- NCOR1 maintains the homeostasis of vascular smooth muscle cells and protects against aortic aneurysm
- Osteoblast MR deficiency protects against adverse ventricular remodeling after myocardial infarction
Bo‐Yan Chen
Shanghai Stomatological Hospital (CN)
3 shared publications
- An IL-6/STAT3/MR/FGF21 axis mediates heart-liver cross-talk after myocardial infarction
- NCOR1 maintains the homeostasis of vascular smooth muscle cells and protects against aortic aneurysm
- Osteoblast MR deficiency protects against adverse ventricular remodeling after myocardial infarction
Ruogu Li
Shanghai Chest Hospital (CN)
3 shared publications
- An IL-6/STAT3/MR/FGF21 axis mediates heart-liver cross-talk after myocardial infarction
- NCOR1 maintains the homeostasis of vascular smooth muscle cells and protects against aortic aneurysm
- Osteoblast MR deficiency protects against adverse ventricular remodeling after myocardial infarction
Lijun Wang
Dalian University of Technology (CN)
3 shared publications
- Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection
- You Only Infer Once: Cross-Modal Meta-Transfer for Referring Video Object Segmentation
- Adaptive Co-teaching for Unsupervised Monocular Depth Estimation
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