298 citations · 1.5k across the 72 of their papers we have counts for
12 papers · 1 filter
Towards End-to-End Image Compression and Analysis with Transformers
Yuanchao Bai, Xu Yang, Xianming Liu +4
We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. Instead of placing an existing Trans…
Deep Attentional Guided Image Filtering
Zhiwei Zhong, Xianming Liu, Junjun Jiang +2
Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods…
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution
Qing Ma, Junjun Jiang, Xianming Liu +1
To solve the ill-posed problem of hyperspectral image super-resolution (HSISR), an usually method is to use the prior information of the hyperspectral images (HSIs) as a regulariza…
A Practical Contrastive Learning Framework for Single-Image Super-Resolution
Gang Wu, Junjun Jiang, Xianming Liu
Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks. It is challen…
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
Jialei Xu, Yuanchao Bai, Xianming Liu +2
Depth estimation from a single image is an active research topic in computer vision. The most accurate approaches are based on fully supervised learning models, which rely on a lar…
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
Junjun Jiang, Chenyang Wang, Xianming Liu +2
High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions, such as low-light environment and spoofin…
Learning with Noisy Labels via Sparse Regularization
Xiong Zhou, Xianming Liu, Chenyang Wang +3
Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer fr…
Asymmetric Loss Functions for Learning with Noisy Labels
Xiong Zhou, Xianming Liu, Junjun Jiang +2
Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be…
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
Pengwei Liang, Junjun Jiang, Xianming Liu +1
The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the computational photography. It is very challenging because the blur kern…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Zhiwei Zhong, Xianming Liu, Junjun Jiang +3
Depth map records distance between the viewpoint and objects in the scene, which plays a critical role in many real-world applications. However, depth map captured by consumer-grad…
Deep Learning-based Face Super-Resolution: A Survey
Junjun Jiang, Chenyang Wang, Xianming Liu +1
Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face i…