Search Results for author:
Found 25 papers, 10 papers with code
Towards Large-scale Generative Ranking
Generative recommendation has recently emerged as a promising paradigm in information retrieval.
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion
Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics.
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github. com/uni-medical/IMIS-Bench.
Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration
In addition, the generalization and extrapolation ability of nested Fourier-DeepONet beyond the training range has been thoroughly evaluated.
Beyond First-Order: A Multi-Scale Approach to Finger Knuckle Print Biometrics
Recently, finger knuckle prints (FKPs) have gained attention due to their rich textural patterns, positioning them as a promising biometric for identity recognition.
Cephalometric Landmark Detection across Ages with Prototypical Network
To our knowledge, this is the first effort toward developing a unified solution and dataset for cephalometric landmark detection across age groups.
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes.
Scale-aware competition network for palmprint recognition
This paper proposes a scale-aware competitive network (SAC-Net), which includes the Inner-Scale Competition Module (ISCM) and the Across-Scale Competition Module (ASCM) to capture texture characteristics related to orientation and scale.
VideoPro: A Visual Analytics Approach for Interactive Video Programming
Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection.
Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning
This study presents a novel framework for accurate prediction of missing teeth in different patterns, facilitating digital implant planning.
SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks
Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure.
$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Here, we develop a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI with the generalization of seismic sources, including the frequencies and locations of sources.
Construction of unbiased dental template and parametric dental model for precision digital dentistry
In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration
Multiphase flow in porous media is essential to understand CO$_2$ migration and pressure fields in the subsurface associated with GCS.
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks.
Time-constrained Dynamic Mechanisms for College Admissions
Recent literature shows that dynamic matching mechanisms may outperform the standard mechanisms to deliver desirable results.
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling.
Real-time Dual-channel 2 * 2 MIMO Fiber-THz-Fiber Seamless Integration System at 385 GHz and 435 GHz
We demonstrate the first practical real-time dual-channel fiber-THz-fiber 2 * 2 MIMO seamless integration system with a record net data rate of 2 * 103. 125 Gb/s at 385 GHz and 435 GHz over two spans of 20 km SSMF and 3 m wireless link.
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes.
PL-Net: Progressive Learning Network for Medical Image Segmentation
In recent years, segmentation methods based on deep convolutional neural networks (CNNs) have made state-of-the-art achievements for many medical analysis tasks.
Deep learning-based person re-identification methods: A survey and outlook of recent works
In recent years, with the increasing demand for public safety and the rapid development of intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot research topics in the computer vision field.
Global-Local Dynamic Feature Alignment Network for Person Re-Identification
To solve these problems, we propose a simple and efficient Local Sliding Alignment (LSA) strategy to dynamically align the local features of two images by setting a sliding window on the local stripes of the pedestrian.
Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients
Given the nonlinear kinetics of tacrolimus and large variability, population pharmacokinetic model should be combined with therapeutic drug monitoring to optimize individualized therapy.
