10 citations · 21 across the 17 of their papers we have counts for
17 papers
D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field
Xueting Yang, Yihao Luo, Yuliang Xiu +3
Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with…
For One-Shot Decoding: Self-supervised Deep Learning-Based Polar Decoder
Huiying Song, Yihao Luo, Yuma Fukuzawa
We propose a self-supervised deep learning-based decoding scheme that enables one-shot decoding of polar codes. In the proposed scheme, rather than using the information bit vector…
SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces
Ziqiao Peng, Yihao Luo, Yue Shi +5
Speech-driven 3D face animation technique, extending its applications to various multimedia fields. Previous research has generated promising realistic lip movements and facial exp…
Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretching
Haibo Shen, Juyu Xiao, Yihao Luo +3
Neuromorphic vision sensors (event cameras) simulate biological visual perception systems and have the advantages of high temporal resolution, less data redundancy, low power consu…
Frequency and Scale Perspectives of Feature Extraction
Liangqi Zhang, Yihao Luo, Xiang Cao +2
Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction. In this paper, by analyzing th…
Efficient CNN Architecture Design Guided by Visualization
Liangqi Zhang, Haibo Shen, Yihao Luo +4
Modern efficient Convolutional Neural Networks(CNNs) always use Depthwise Separable Convolutions(DSCs) and Neural Architecture Search(NAS) to reduce the number of parameters and th…
Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons
Haibo Shen, Yihao Luo, Xiang Cao +3
As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Int…
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments
Haibo Shen, Yihao Luo, Xiang Cao +3
Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model. Due to…
ECG Classification based on Wasserstein Scalar Curvature
Fupeng Sun, Yin Ni, Yihao Luo +1
Electrocardiograms (ECG) analysis is one of the most important ways to diagnose heart disease. This paper proposes an efficient ECG classification method based on Wasserstein scala…
A Novel Heart Disease Classification Algorithm based on Fourier Transform and Persistent Homology
Yin Ni, Fupeng Sun, Yihao Luo +2
Classification and prediction of heart disease is a significant problem to realize medical treatment and life protection. In this paper, persistent homology is involved to analyze…
New Solution based on Hodge Decomposition for Abstract Games
Yihao Luo, Jinhui Pang, Weibin Han +1
This paper proposes Hodge Potential Choice (HPC), a new solution for abstract games with irreflexive dominance relations. This solution is formulated by involving geometric tools l…
Dynamic Multi-Scale Loss Optimization for Object Detection
Yihao Luo, Xiang Cao, Juntao Zhang +3
With the continuous improvement of the performance of object detectors via advanced model architectures, imbalance problems in the training process have received more attention. It…
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature
Yihao Luo, Ailing Yang, Fupeng Sun +1
In this paper, we introduce the adaptive Wasserstein curvature denoising (AWCD), an original processing approach for point cloud data. By collecting curvatures information from Was…
CE-FPN: Enhancing Channel Information for Object Detection
Yihao Luo, Xiang Cao, Juntao Zhang +4
Feature pyramid network (FPN) has been an effective framework to extract multi-scale features in object detection. However, current FPN-based methods mostly suffer from the intrins…
Geometric Characteristics of Wasserstein Metric on SPD(n)
Yihao Luo, Shiqiang Zhang, Yueqi Cao +1
Wasserstein distance, especially among symmetric positive-definite matrices, has broad and deep influences on development of artificial intelligence (AI) and other branches of comp…
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
Chuang Zhu, Ke Mei, Ting Peng +4
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly eff…
SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking
Yihao Luo, Min Xu, Caihong Yuan +5
Recently spiking neural networks (SNNs), the third-generation of neural networks has shown remarkable capabilities of energy-efficient computing, which is a promising alternative f…