35 citations · 51 across the 32 of their papers we have counts for
24 papers · 1 filter
Predicting Functions, Not Features: KANs with Function-Space Joint-Embedding Predictive Learning for Medical Image Segmentation
Yungeng Liu, Xuanzi Fang, Yuge Zhang +3
Kolmogorov--Arnold Networks (KANs) introduce explicit functional representations by parameterizing each network edge as a learnable univariate function. However, existing KAN-based…
SegDem: Segmentation helps Demosaicing
Ping Chen, Xiangming Wang, Yongyong Chen +5
Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter array (CFA). Most existing methods formulate d…
The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +89
This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-wo…
Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation
Yungeng Liu, Xuanzi Fang, Haijin Zeng +2
Text-guided medical image segmentation leverages clinical semantics to improve lesion delineation, yet many existing models bind cross-modal fusion, supervision, and decoder design…
Computer-Aided Design Generation by Cascaded Discrete Diffusion Model
Honghu Pan, Xiaoling Luo, Yongyong Chen +2
Recent deep learning approaches seek to automate CAD creation by representing a model as a sequence of discrete commands and parameters, and then generating them using autoregressi…
Deep LoRA-Unfolding Networks for Image Restoration
Xiangming Wang, Haijin Zeng, Benteng Sun +4
Deep unfolding networks (DUNs), combining conventional iterative optimization algorithms and deep neural networks into a multi-stage framework, have achieved remarkable accomplishm…
HSI-VAR: Rethinking Hyperspectral Restoration through Spatial-Spectral Visual Autoregression
Xiangming Wang, Benteng Sun, Yungeng Liu +4
Hyperspectral images (HSIs) capture richer spatial-spectral information beyond RGB, yet real-world HSIs often suffer from a composite mix of degradations, such as noise, blur, and…
SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging
Haijin Zeng, Xuan Lu, Jiezhang Cao +8
Humans learn in two complementary ways: a slow, cumulative process that builds broad, general knowledge, and a fast, on-the-fly process that captures specific experiences. Existing…
Association and Consolidation: Evolutionary Memory-Enhanced Incremental Multi-View Clustering
Zisen Kong, Bo Zhong, Pengyuan Li +3
Incremental multi-view clustering aims to achieve stable clustering results while addressing the stability-plasticity dilemma (SPD) in view-incremental scenarios. The core challeng…
Lightweight Quad Bayer HybridEVS Demosaicing via State Space Augmented Cross-Attention
Shiyang Zhou, Haijin Zeng, Yunfan Lu +3
Event cameras like the Hybrid Event-based Vision Sensor (HybridEVS) camera capture brightness changes as asynchronous "events" instead of frames, offering advanced application on m…
Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks
Haijin Zeng, Xiangming Wang, Yongyong Chen +2
Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environ…
Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing
Shiyang Zhou, Haijin Zeng, Yunfan Lu +5
Quad Bayer demosaicing is the central challenge for enabling the widespread application of Hybrid Event-based Vision Sensors (HybridEVS). Although existing learning-based methods t…
OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
Xiangming Wang, Haijin Zeng, Jiaoyang Chen +3
In real-world scenarios, complex data such as multispectral images and multi-frame videos inherently exhibit robust low-rank property. This property is vital for multi-dimensional…
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging
Zhenghao Pan, Haijin Zeng, Jiezhang Cao +3
Color video snapshot compressive imaging (SCI) employs computational imaging techniques to capture multiple sequential video frames in a single Bayer-patterned measurement. With th…
Cross-Scan Mamba with Masked Training for Robust Spectral Imaging
Wenzhe Tian, Haijin Zeng, Yin-Ping Zhao +3
Snapshot Compressive Imaging (SCI) enables fast spectral imaging but requires effective decoding algorithms for hyperspectral image (HSI) reconstruction from compressed measurement…
LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network
Xuanqi Zhang, Haijin Zeng, Jinwang Pan +2
Transformer-based low-light enhancement methods have yielded promising performance by effectively capturing long-range dependencies in a global context. However, their elevated com…
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model
Zhenghao Pan, Haijin Zeng, Jiezhang Cao +2
This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this, we integrate the advantageous att…
How Image Generation Helps Visible-to-Infrared Person Re-Identification?
Honghu Pan, Yongyong Chen, Yunqi He +2
Compared to visible-to-visible (V2V) person re-identification (ReID), the visible-to-infrared (V2I) person ReID task is more challenging due to the lack of sufficient training samp…
Multi-Granularity Graph Pooling for Video-based Person Re-Identification
Honghu Pan, Yongyong Chen, Zhenyu He
The video-based person re-identification (ReID) aims to identify the given pedestrian video sequence across multiple non-overlapping cameras. To aggregate the temporal and spatial…
Pose-Aided Video-based Person Re-Identification via Recurrent Graph Convolutional Network
Honghu Pan, Qiao Liu, Yongyong Chen +4
Existing methods for video-based person re-identification (ReID) mainly learn the appearance feature of a given pedestrian via a feature extractor and a feature aggregator. However…
Towards Complete-View and High-Level Pose-based Gait Recognition
Honghu Pan, Yongyong Chen, Tingyang Xu +2
The model-based gait recognition methods usually adopt the pedestrian walking postures to identify human beings. However, existing methods did not explicitly resolve the large intr…
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising
Haijin Zeng, Shaoguang Huang, Yongyong Chen +2
Spatial-Spectral Total Variation (SSTV) can quantify local smoothness of image structures, so it is widely used in hyperspectral image (HSI) processing tasks. Essentially, SSTV ass…
Hyperspectral Image Denoising with Log-Based Robust PCA
Yang Liu, Qian Zhang, Yongyong Chen +2
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex approach to RPCA for HSI denoising,…
TCDesc: Learning Topology Consistent Descriptors for Image Matching
Honghu Pan, Fanyang Meng, Nana Fan +1
The constraint of neighborhood consistency or local consistency is widely used for robust image matching. In this paper, we focus on learning neighborhood topology consistent descr…