3 citations · 3 across the 12 of their papers we have counts for
14 papers
UnfoldCRF: Structured Mask Refinement with Image-Conditioned Latent Regions
Chunming He, Rihan Zhang, Lei Xu +5
Learned mask refiners improve segmentation accuracy, but it is hard to tell how much of the improvement comes from explicit structure rather than from extra capacity, and whether i…
Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?
Xinge Guo, Fengyang Xiao, Dingming Zhang +6
Foundation segmenters such as SAM return several plausible masks for an unlabeled image, and a student trained on the wrong one inherits its errors. Choosing among them means query…
RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects
Chunming He, Rihan Zhang, Dingming Zhang +5
Concealed Object Segmentation (COS) encompasses a family of dense-prediction tasks, including camouflaged object detection, polyp segmentation, transparent object detection, and in…
Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration
Fengyang Xiao, Peng Hu, Lei Xu +7
Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depen…
QualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration
Fengyang Xiao, Jingjia Feng, Peng Hu +6
Real-world image restoration (RWIR) is a highly challenging task due to the absence of clean ground-truth images. Many recent methods resort to pseudo-label (PL) supervision, often…
Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
Chunming He, Rihan Zhang, Fengyang Xiao +3
Biological learning proceeds from easy to difficult tasks, gradually reinforcing perception and robustness. Inspired by this principle, we address Context-Entangled Content Segment…
Nested Unfolding Network for Real-World Concealed Object Segmentation
Chunming He, Rihan Zhang, Dingming Zhang +3
Deep unfolding networks (DUNs) have recently advanced concealed object segmentation (COS) by modeling segmentation as iterative foreground-background separation. However, existing…
SCALER: SAM-Enhanced Collaborative Learning for Label-Deficient Concealed Object Segmentation
Chunming He, Rihan Zhang, Longxiang Tang +4
Existing methods for label-deficient concealed object segmentation (LDCOS) either rely on consistency constraints or Segment Anything Model (SAM)-based pseudo-labeling. However, th…
UnfoldLDM: Degradation-Aware Unfolding with Iterative Latent Diffusion Priors for Blind Image Restoration
Chunming He, Rihan Zhang, Zheng Chen +6
Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited for blind image restoration (BIR).…
Reversible Unfolding Network for Concealed Visual Perception with Generative Refinement
Chunming He, Fengyang Xiao, Rihan Zhang +3
Existing methods for concealed visual perception (CVP) often leverage reversible strategies to decrease uncertainty, yet these are typically confined to the mask domain, leaving th…
Segment Concealed Objects with Incomplete Supervision
Chunming He, Kai Li, Yachao Zhang +8
Incompletely-Supervised Concealed Object Segmentation (ISCOS) involves segmenting objects that seamlessly blend into their surrounding environments, utilizing incompletely annotate…
UnfoldIR: Rethinking Deep Unfolding Network in Illumination Degradation Image Restoration
Chunming He, Rihan Zhang, Fengyang Xiao +4
Deep unfolding networks (DUNs) are widely employed in illumination degradation image restoration (IDIR) to merge the interpretability of model-based approaches with the generalizat…
IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond
Peng Hu, Chunming He, Lei Xu +5
Blind Face Restoration (BFR) addresses the challenge of reconstructing degraded low-quality (LQ) facial images into high-quality (HQ) outputs. Conventional approaches predominantly…
RUN: Reversible Unfolding Network for Concealed Object Segmentation
Chunming He, Rihan Zhang, Fengyang Xiao +7
Existing concealed object segmentation (COS) methods frequently utilize reversible strategies to address uncertain regions. However, these approaches are typically restricted to th…