226 citations · 590 across the 36 of their papers we have counts for
8 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…
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network
Yuanzhi Wang, Tao Lu, Yanduo Zhang +4
Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e.g., facial parsing maps, faci…
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…