activity
20192022
most citedHierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

58 citations · 84 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2022

Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation

Juncheng Li, Hanhui Yang, Qiaosi Yi +4

Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of paramete…

cs.CV20224 cited

Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer

Guangwei Gao, Zhengxue Wang, Juncheng Li +3

Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world…

cs.CV202158 cited

Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

Guangwei Gao, Yi Yu, Jian Yang +2

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face…

cs.CV2021

MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation

Guangwei Gao, Guoan Xu, Yi Yu +3

In recent years, how to strike a good trade-off between accuracy and inference speed has become the core issue for real-time semantic segmentation applications, which plays a vital…

eess.IV2021

Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network

Zhengxue Wang, Guangwei Gao, Juncheng Li +2

Recently, the single image super-resolution (SISR) approaches with deep and complex convolutional neural network structures have achieved promising performance. However, those meth…

cs.CV20201 cited

Cross-View Image Synthesis with Deformable Convolution and Attention Mechanism

Hao Ding, Songsong Wu, Hao Tang +3

Learning to generate natural scenes has always been a daunting task in computer vision. This is even more laborious when generating images with very different views. When the views…