167 citations · 424 across the 41 of their papers we have counts for
14 papers · 1 filter
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation
Juntong Fan, Debesh Jha, Tieyong Zeng +1
Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues. This similarity gives ri…
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
Jing-Xiao Liao, Sheng-Lai Wei, Chen-Long Xie +5
Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be…
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance
Yiming Cui, Lecheng Ruan, Hang-Cheng Dong +4
The networks for point cloud tasks are expected to be invariant when the point clouds are affinely transformed such as rotation and reflection. So far, relative to the rotational i…
Deep ReLU Networks Have Surprisingly Simple Polytopes
Feng-Lei Fan, Wei Huang, Xiangru Zhong +4
A ReLU network is a piecewise linear function over polytopes. Figuring out the properties of such polytopes is of fundamental importance for the research and development of neural…
Recognizable Information Bottleneck
Yilin Lyu, Xin Liu, Mingyang Song +4
Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalizat…
Fast MRI Reconstruction via Edge Attention
Hanhui Yang, Juncheng Li, Lok Ming Lui +3
Fast and accurate MRI reconstruction is a key concern in modern clinical practice. Recently, numerous Deep-Learning methods have been proposed for MRI reconstruction, however, they…
EWT: Efficient Wavelet-Transformer for Single Image Denoising
Juncheng Li, Bodong Cheng, Ying Chen +2
Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatl…
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees
Ziwen Wang, Yancheng Yuan, Jiaming Ma +2
In this paper, we propose a randomly projected convex clustering model for clustering a collection of high dimensional data points in with hidden clusters. C…
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
Hansheng Guo, Juncheng Li, Guangwei Gao +2
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous m…
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
Feng-Lei Fan, Hang-Cheng Dong, Zhongming Wu +4
Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into art…
Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm
Dong Li, Shuisheng Zhou, Tieyong Zeng +1
K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters k has to be given…
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
Zhifan Gao, Yifeng Guo, Jiajing Zhang +2
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techni…
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
Ao Chen, Jinghua Zhang, Md Mamunur Rahaman +6
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and reso…
Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks
Feng-Lei Fan, Yingxin Li, Hanchuan Peng +2
Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened under…