Search Results for author:
Found 13 papers, 4 papers with code
Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach
Timely and precise classification and segmentation of gastric bleeding in endoscopic imagery are pivotal for the rapid diagnosis and intervention of gastric complications, which is critical in life-saving medical procedures.
Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features
Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital.
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition
First, we define a supernet and propose a global and local alternate Neural Architecture Search method to search the optimal architecture alternately with a differentiable neural architecture search.
Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition
Eye movement biometrics is a secure and innovative identification method.
A Survey on Mixup Augmentations and Beyond
As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable.
MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification
At the testing stage, given an adversarial sample, the MsMemoryGAN retrieves its most relevant normal patterns in memory for the reconstruction.
Neural Architecture Search based Global-local Vision Mamba for Palm-Vein Recognition
In this paper, first, we propose a hybrid network structure named Global-local Vision Mamba (GLVM), to learn the local correlations in images explicitly and global dependencies among tokens for vein feature representation.
SUMix: Mixup with Semantic and Uncertain Information
Mixup data augmentation approaches have been applied for various tasks of deep learning to improve the generalization ability of deep neural networks.
StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification
To enable CNNs to capture comprehensive feature representations from palm-vein images, we explored the effect of convolutional kernel size on the performance of palm-vein identification networks and designed LaKNet, a network leveraging large kernel convolution and gating mechanism.
Adversarial Masking Contrastive Learning for vein recognition
To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive learning model for the downstream palm-vein recognition task, by alternatively optimizing the encoder in the contrastive learning model and a set of latent variables.
EmMixformer: Mix transformer for eye movement recognition
To this end, we propose a mixed block consisting of three modules, transformer, attention Long short-term memory (attention LSTM), and Fourier transformer.
Adversarial AutoMixup
In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator.
Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-V ein Identification
Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples.
