13 citations · 14 across the 23 of their papers we have counts for
6 papers · 1 filter
SCOPE-VSR: A Broad-Coverage Training Dataset for Video Super-Resolution
Gengyu Liao, Yechun Yong, Zheng Chen +4
Open-domain video super-resolution (VSR) requires temporally coherent details across widely varying scenes and subjects. However, existing training datasets are often semantically…
SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models
Kewei Zhang, Zheng Chen, Haotong Qin +1
Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation models. However, their large la…
BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement
Yi Ai, Zheng Chen, Yuanhao Cai +2
Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport can model exposure ambiguity;…
LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution
Tianxing Wu, Zheng Chen, Cirou Xu +5
One-Step Diffusion Models have demonstrated promising capability and fast inference in video super-resolution (VSR) for real-world. Nevertheless, the substantial model size and hig…
BinaryDemoire: Moiré-Aware Binarization for Image Demoiréing
Zheng Chen, Zhi Yang, Xiaoyang Liu +5
Image demoiréing aims to remove structured moiré artifacts in recaptured imagery, where degradations are highly frequency-dependent and vary across scales and directions. While rec…
VEQ: Modality-Adaptive Quantization for MoE Vision-Language Models
Guangshuo Qin, Zhiteng Li, Zheng Chen +3
Mixture-of-Experts(MoE) Vision-Language Models (VLMs) offer remarkable performance but incur prohibitive memory and computational costs, making compression essential. Post-Training…