57 citations · 86 across the 7 of their papers we have counts for
7 papers
ERVD: An Efficient and Robust ViT-Based Distillation Framework for Remote Sensing Image Retrieval
Le Dong, Qixuan Cao, Lei Pu +4
ERVD: An Efficient and Robust ViT-Based Distillation Framework for Remote Sensing Image Retrieval
Robust Depth Completion with Uncertainty-Driven Loss Functions
Yufan Zhu, Weisheng Dong, Leida Li +3
Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite the popularity of color-guided methods for sparse-to-dense depth completion, they treated pixe…
Searching Efficient Model-guided Deep Network for Image Denoising
Qian Ning, Weisheng Dong, Xin Li +3
Neural architecture search (NAS) has recently reshaped our understanding on various vision tasks. Similar to the success of NAS in high-level vision tasks, it is possible to find a…
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution
Xuan Xu, Xin Xiong, Jinge Wang +1
Video super-resolution, which attempts to reconstruct high-resolution video frames from their corresponding low-resolution versions, has received increasingly more attention in rec…
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network
Qian Ning, Weisheng Dong, Guangming Shi +2
Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed like…
Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding
Fangfang Wu, Weisheng Dong, Guangming Shi +1
State-of-the-art approaches toward image restoration can be classified into model-based and learning-based. The former - best represented by sparse coding techniques - strive to ex…
The Shape of Sight: A Homological Framework for Unifying Visual Perception
Xin Li
Visual perception, the brain's construction of a stable world from sensory data, faces several long-standing, fundamental challenges. While often studied separately, these problems…