64 citations · 87 across the 20 of their papers we have counts for
20 papers
PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing
Chengyu Fang, Chunming He, Yuelin Zhang +6
Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying colo…
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…
A Vision-Language-Action Model for Adaptive Ultrasound-Guided Needle Insertion and Needle Tracking
Yuelin Zhang, Qingpeng Ding, Longxiang Tang +2
Ultrasound (US)-guided needle insertion is a critical yet challenging procedure due to dynamic imaging conditions and difficulties in needle visualization. Many methods have been p…
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…
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…
Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts
Hantao Zhou, Rui Yang, Longxiang Tang +3
Image assessment aims to evaluate the quality and aesthetics of images and has been applied across various scenarios, such as natural and AIGC scenes. Existing methods mostly addre…
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…
Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
Chengyu Fang, Chunming He, Longxiang Tang +6
Camouflaged Object Segmentation (COS) remains challenging because camouflaged objects exhibit only subtle visual differences from their backgrounds and single-modality RGB methods…
InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences
Chenyang Zhu, Kai Li, Yue Ma +5
Recent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing meth…
A Survey of Camouflaged Object Detection and Beyond
Fengyang Xiao, Sujie Hu, Yuqi Shen +6
Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for comput…
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models
Longxiang Tang, Zhuotao Tian, Kai Li +5
This study addresses the Domain-Class Incremental Learning problem, a realistic but challenging continual learning scenario where both the domain distribution and target classes va…
Diffusion Models in Low-Level Vision: A Survey
Chunming He, Yuqi Shen, Chengyu Fang +6
Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities. Among them, diffusion model-based solutions, characterize…
Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network
Chengyu Fang, Chunming He, Fengyang Xiao +5
Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real…
UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment
Hantao Zhou, Longxiang Tang, Rui Yang +6
Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) aim to simulate human subjective perception of image visual quality and aesthetic appeal. Despite distinct learn…
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
Chunming He, Chengyu Fang, Yulun Zhang +6
Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among t…
Consistency Regularization for Generalizable Source-free Domain Adaptation
Longxiang Tang, Kai Li, Chunming He +2
Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variet…
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Chunming He, Kai Li, Guoxia Xu +5
Unpaired Medical Image Enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most…
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher
Longxiang Tang, Kai Li, Chunming He +2
This paper studies source-free domain adaptive fundus image segmentation which aims to adapt a pretrained fundus segmentation model to a target domain using unlabeled images. This…
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
Chunming He, Kai Li, Yachao Zhang +5
Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment objects well blended with surrounding environments using sparsely-annotated data for model training. It rema…