6 papers
SEGA: A Transferable Signed Ensemble Gaussian Black-Box Attack against No-Reference Image Quality Assessment Models
Yujia Liu, Dingquan Li, Zhixuan Li +1
No-Reference Image Quality Assessment (NR-IQA) models play an important role in various real-world applications. Recently, adversarial attacks against NR-IQA models have attracted…
Shape Distribution Matters: Shape-specific Mixture-of-Experts for Amodal Segmentation under Diverse Occlusions
Zhixuan Li, Yujia Liu, Chen Hui +3
Amodal segmentation targets to predict complete object masks, covering both visible and occluded regions. This task poses significant challenges due to complex occlusions and extre…
Single Point, Full Mask: Velocity-Guided Level Set Evolution for End-to-End Amodal Segmentation
Zhixuan Li, Yujia Liu, Chen Hui +1
Amodal segmentation aims to recover complete object shapes, including occluded regions with no visual appearance, whereas conventional segmentation focuses solely on visible areas.…
MS-IQA: A Multi-Scale Feature Fusion Network for PET/CT Image Quality Assessment
Siqiao Li, Chen Hui, Wei Zhang +7
Positron Emission Tomography / Computed Tomography (PET/CT) plays a critical role in medical imaging, combining functional and anatomical information to aid in accurate diagnosis.…
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Chenyue Song, Chen Hui, Qing Lin +8
Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variable modeling, demonstrating its advantages in lossless medical image compression…
Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA
Zhixuan Li, Hyunse Yoon, Sanghoon Lee +1
Amodal segmentation aims to infer the complete shape of occluded objects, even when the occluded region's appearance is unavailable. However, current amodal segmentation methods la…