1 citations · 1 across the 6 of their papers we have counts for
7 papers
Reliability-Aware Checkpoint Selection for Domain Generalization
Jinshi Liu, Jiahao Li, Pan Liu +5
Checkpoint selection in domain generalization often relies on source-validation accuracy, yet the selected checkpoint need not provide reliable probabilities on unseen target domai…
HiFi-Mesh: High-Fidelity Efficient 3D Mesh Generation via Compact Autoregressive Dependence
Yanfeng Li, Tao Tan, Qingquan Gao +3
High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods su…
MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment
Siyi Xun, Yue Sun, Jingkun Chen +5
Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical I…
MoEdit: On Learning Quantity Perception for Multi-object Image Editing
Yanfeng Li, Kahou Chan, Yue Sun +6
Multi-object images are prevalent in various real-world scenarios, including augmented reality, advertisement design, and medical imaging. Efficient and precise editing of these im…
Charting the Path Forward: CT Image Quality Assessment -- An In-Depth Review
Siyi Xun, Qiaoyu Li, Xiaohong Liu +3
Computed Tomography (CT) is a frequently utilized imaging technology that is employed in the clinical diagnosis of many disorders. However, clinical diagnosis, data storage, and ma…
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Xiangyu Xiong, Yue Sun, Xiaohong Liu +10
Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On t…
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Xiangyu Xiong, Yue Sun, Xiaohong Liu +6
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative advers…