10 citations · 14 across the 7 of their papers we have counts for
7 papers
Co-Evidential Fusion with Information Volume for Medical Image Segmentation
Yuanpeng He, Lijian Li, Tianxiang Zhan +3
Although existing semi-supervised image segmentation methods have achieved good performance, they cannot effectively utilize multiple sources of voxel-level uncertainty for targete…
Mutual Evidential Deep Learning for Medical Image Segmentation
Yuanpeng He, Yali Bi, Lijian Li +3
Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality…
CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement
Wei Wang, Zhi Jin
Low-Light Image Enhancement (LLIE) has advanced with the surge in phone photography demand, yet many existing methods neglect compression, a crucial concern for resource-constraine…
Latent Modulated Function for Computational Optimal Continuous Image Representation
Zongyao He, Zhi Jin
The recent work Local Implicit Image Function (LIIF) and subsequent Implicit Neural Representation (INR) based works have achieved remarkable success in Arbitrary-Scale Super-Resol…
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Ao Li +109
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective netw…
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing
Yuwei Qiu, Kaihao Zhang, Chenxi Wang +3
In recent years, Transformer networks are beginning to replace pure convolutional neural networks (CNNs) in the field of computer vision due to their global receptive field and ada…