collaborators

8 papers

cs.HC2026

Automated Gaze-based Behavioral Segmentation and Temporal Representation for Bridge Inspection in Unconstrained 3D Environments

Daniel Jimenez Gil, Haosen Zhang, Zixin Wang +1

Visual bridge inspection is a knowledge-intensive task in which inspectors coordinate visual search, spatial navigation, structural reasoning, and defect identification and documen…

cs.IR2026

From Trajectories to Phenotypes: Disease Progression as Structural Priors for Multi-organ Imaging Representation Learning

Zian Wang, Lizhen Lan, Guangming Wang +9

Imaging-derived phenotypes (IDPs) summarize multi-organ physiology but provide only static snapshots of diseases that evolve over time. In contrast, longitudinal electronic health…

cs.SD2026

Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores

Congren Dai, Yue Yang, Krinos Li +12

Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision--Lang…

eess.IV2026

Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Database

Zi Wang, Mingkai Huang, Zhang Shi +61

Multimodal cardiovascular magnetic resonance (CMR) imaging provides comprehensive and non-invasive insights into cardiovascular disease (CVD) diagnosis and underlying mechanisms. D…

eess.IV2026

From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction

Zhenxuan Zhang, Lipei Zhang, Yanqi Cheng +10

In motion-robust magnetic resonance imaging (MRI), slice-to-volume reconstruction is critical for recovering anatomically consistent 3D brain volumes from 2D slices, especially und…

cs.CV2026

Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis

Yinzhe Wu, Hongyu Rui, Fanwen Wang +5

Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, paired low- and high-resolution (LR…