4 citations · 7 across the 7 of their papers we have counts for
7 papers
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)
Ya-nan Guan, Shaonan Zhang, Hang Guo +55
In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait. Despite sign…
DBINDS -- Can Initial Noise from Diffusion Model Inversion Help Reveal AI-Generated Videos?
Yanlin Wu, Xiaogang Yuan, Dezhi An
AI-generated video has advanced rapidly and poses serious challenges to content security and forensic analysis. Existing detectors rely mainly on pixel-level visual cues and genera…
Adaptive Stain Normalization for Cross-Domain Medical Histology
Tianyue Xu, Yanlin Wu, Abhai K. Tripathi +2
Deep learning advances have revolutionized automated digital pathology analysis. However, differences in staining protocols and imaging conditions can introduce significant color v…
Med-URWKV†: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation
Zhenhuan Zhou, Yining Li, Yanlin Wu +3
Medical image segmentation is a fundamental task in computer-aided diagnosis and treatment. Existing approaches based on CNNs, ViTs, Mamba, and hybrid models still suffer from limi…
TransUKAN:Computing-Efficient Hybrid KAN-Transformer for Enhanced Medical Image Segmentation
Yanlin Wu, Tao Li, Zhihong Wang +2
U-Net is currently the most widely used architecture for medical image segmentation. Benefiting from its unique encoder-decoder architecture and skip connections, it can effectivel…
Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation
Zhenhuan Zhou, Along He, Yanlin Wu +3
In medical images, various types of lesions often manifest significant differences in their shape and texture. Accurate medical image segmentation demands deep learning models with…
FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation
Along He, Tao Li, Yanlin Wu +2
Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-su…