activity
20242026
most citedHigh-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

3 citations · 3 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models

Jingxuan Zhang, Yunta Hsieh, Zhongwei Wan +5

Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing co…

cs.CV2026

ProMist-5K: A Comprehensive Dataset for Digital Emulation of Cinematic Pro-Mist Filter Effects

Yingtie Lei, Zimeng Li, Chi-Man Pun +3

Pro-Mist filters are widely used in cinematography for their ability to create soft halation, lower contrast, and produce a distinctive, atmospheric style. These effects are diffic…

eess.IV2025

FS-RWKV: Leveraging Frequency Spatial-Aware RWKV for 3T-to-7T MRI Translation

Yingtie Lei, Zimeng Li, Chi-Man Pun +2

Ultra-high-field 7T MRI offers enhanced spatial resolution and tissue contrast that enables the detection of subtle pathological changes in neurological disorders. However, the lim…

cs.CV2025

SFormer: SNR-guided Transformer for Underwater Image Enhancement from the Frequency Domain

Xin Tian, Yingtie Lei, Xiujun Zhang +3

Recent learning-based underwater image enhancement (UIE) methods have advanced by incorporating physical priors into deep neural networks, particularly using the signal-to-noise ra…

cs.CV2025

CMAMRNet: A Contextual Mask-Aware Network Enhancing Mural Restoration Through Comprehensive Mask Guidance

Yingtie Lei, Fanghai Yi, Yihang Dong +5

Murals, as invaluable cultural artifacts, face continuous deterioration from environmental factors and human activities. Digital restoration of murals faces unique challenges due t…

cs.CV20243 cited

High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

Mingxian Li, Hao Sun, Yingtie Lei +5

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. Th…