Search Results for author:
Found 15 papers, 5 papers with code
D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field
In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface.
For One-Shot Decoding: Self-supervised Deep Learning-Based Polar Decoder
We propose a self-supervised deep learning-based decoding scheme that enables one-shot decoding of polar codes.
SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces
To enhance the visual accuracy of generated lip movement while reducing the dependence on labeled data, we propose a novel framework SelfTalk, by involving self-supervision in a cross-modals network system to learn 3D talking faces.
Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretching
However, the unconventional visual signals of these cameras pose a great challenge to the robustness of spiking neural networks.
Frequency and Scale Perspectives of Feature Extraction
Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction.
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments
Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model.
Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons
Consistent with the ALTP phenomenon, the AIA neuron model is adaptive to input stimuli, and internal associative learning occurs only when both dendrites are stimulated at the same time.
Efficient CNN Architecture Design Guided by Visualization
Our VGNetG-1. 0MP achieves 67. 7% top-1 accuracy with 0. 99M parameters and 69. 2% top-1 accuracy with 1. 14M parameters on ImageNet classification dataset.
A Novel Heart Disease Classification Algorithm based on Fourier Transform and Persistent Homology
Classification and prediction of heart disease is a significant problem to realize medical treatment and life protection.
New Solution based on Hodge Decomposition for Abstract Games
This paper proposes Hodge Potential Choice (HPC), a new solution for abstract games with irreflexive dominance relations.
Dynamic Multi-Scale Loss Optimization for Object Detection
With the continuous improvement of the performance of object detectors via advanced model architectures, imbalance problems in the training process have received more attention.
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature
In this paper, we introduce the adaptive Wasserstein curvature denoising (AWCD), an original processing approach for point cloud data.
CE-FPN: Enhancing Channel Information for Object Detection
Instead of the original 1x1 convolution and linear upsampling, it mitigates the information loss due to channel reduction.
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate.
Ranked #1 on
Tumor Segmentation
on DigestPath
SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking
Recently spiking neural networks (SNNs), the third-generation of neural networks has shown remarkable capabilities of energy-efficient computing, which is a promising alternative for deep neural networks (DNNs) with high energy consumption.
