35 citations · 51 across the 32 of their papers we have counts for
9 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…
Agentic Flow Steering and Parallel Rollout Search for Spatially Grounded Text-to-Image Generation
Ping Chen, Daoxuan Zhang, Xiangming Wang +3
Precise Text-to-Image (T2I) generation has achieved great success but is hindered by the limited relational reasoning of static text encoders and the error accumulation in open-loo…
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…
Vision-Language Controlled Deep Unfolding for Joint Medical Image Restoration and Segmentation
Ping Chen, Zicheng Huang, Xiangming Wang +4
We propose VL-DUN, a principled framework for joint All-in-One Medical Image Restoration and Segmentation (AiOMIRS) that bridges the gap between low-level signal recovery and high-…