27 citations · 82 across the 13 of their papers we have counts for
11 papers · 1 filter
Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement
Kun Zhou, Xinyu Lin, Wenbo Li +5
Previous low-light image enhancement (LLIE) approaches, while employing frequency decomposition techniques to address the intertwined challenges of low frequency (e.g., illuminatio…
From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm
Kun Zhou, Wenbo Li, Nianjuan Jiang +2
Neural radiance fields (NeRF) have shown great success in novel view synthesis. However, recovering high-quality details from real-world scenes is still challenging for the existin…
NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer
Kun Zhou, Wenbo Li, Yi Wang +4
Neural radiance fields (NeRF) show great success in novel view synthesis. However, in real-world scenes, recovering high-quality details from the source images is still challenging…
Video-P2P: Video Editing with Cross-attention Control
Shaoteng Liu, Yuechen Zhang, Wenbo Li +2
This paper presents Video-P2P, a novel framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-…
What Makes for Good Tokenizers in Vision Transformer?
Shengju Qian, Yi Zhu, Wenbo Li +2
The architecture of transformers, which recently witness booming applications in vision tasks, has pivoted against the widespread convolutional paradigm. Relying on the tokenizatio…
Image Inpainting via Iteratively Decoupled Probabilistic Modeling
Wenbo Li, Xin Yu, Kun Zhou +3
Generative adversarial networks (GANs) have made great success in image inpainting yet still have difficulties tackling large missing regions. In contrast, iterative probabilistic…
Mutual Guidance and Residual Integration for Image Enhancement
Kun Zhou, KenKun Liu, Wenbo Li +2
Previous studies show the necessity of global and local adjustment for image enhancement. However, existing convolutional neural networks (CNNs) and transformer-based models face g…
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation
Kun Zhou, Wenbo Li, Xiaoguang Han +1
For video frame interpolation (VFI), existing deep-learning-based approaches strongly rely on the ground-truth (GT) intermediate frames, which sometimes ignore the non-unique natur…
MAT: Mask-Aware Transformer for Large Hole Image Inpainting
Wenbo Li, Zhe Lin, Kun Zhou +3
Recent studies have shown the importance of modeling long-range interactions in the inpainting problem. To achieve this goal, existing approaches exploit either standalone attentio…
On Efficient Transformer-Based Image Pre-training for Low-Level Vision
Wenbo Li, Xin Lu, Shengju Qian +3
Pre-training has marked numerous state of the arts in high-level computer vision, while few attempts have ever been made to investigate how pre-training acts in image processing sy…
Unsupervised data augmentation for object detection
Yichen Zhang, Zeyang Song, Wenbo Li
Data augmentation has always been an effective way to overcome overfitting issue when the dataset is small. There are already lots of augmentation operations such as horizontal fli…