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Found 15 papers, 3 papers with code
Toward Tiny and High-quality Facial Makeup with Data Amplify Learning
The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations.
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e
We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab).
SieveNet: Selecting Point-Based Features for Mesh Networks
Meshes are widely used in 3D computer vision and graphics, but their irregular topology poses challenges in applying them to existing neural network architectures.
SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging Garments
Our work presents a novel spectrum-inspired learning-based approach for generating clothing deformations with dynamic effects and personalized details.
Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis
Besides, we develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR, where each case consists of a 3D shape of adrenal gland and its diagnosis label (normal vs. abnormal) assigned by experts.
Detail-aware Deep Clothing Animations Infused with Multi-source Attributes
This paper presents a novel learning-based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations.
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
Fair AutoML Through Multi-objective Optimization
There has been a recent surge of interest in fairness measurement and bias mitigation in machine learning, given the identification of significant disparities in predictions from models in many domains.
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
We present MedMNIST, a collection of 10 pre-processed medical open datasets.
Research on Fitness Function of Two Evolution Algorithms Used for Neutron Spectrum Unfolding
In this work, we investigated the performance of eight fitness functions attached to the genetic algorithm (GA) and the differential evolution algorithm (DEA) used for unfolding four neutron spectra selected from the IAEA 403 report.
A Stochastic Trust Region Algorithm Based on Careful Step Normalization
An algorithm is proposed for solving stochastic and finite sum minimization problems.
Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification
The class-identity term keeps the intra-class samples within each camera view gathering together, the relative distance term maximizes the distance between the intra-class class set and inter-class set across different camera views, and the regularization term smoothness the parameters of deep convolutional neural network (CNN).
Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration
This work conquer this problem by changing the Riemannian metric on the target surface to a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints.
Area Preserving Brain Mapping
Experimental results on caudate nucleus surface mapping and cortical surface mapping demonstrate the efficacy and efficiency of the proposed method.
