Search Results for author:
Found 36 papers, 15 papers with code
Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion
The extraction of geoelectric structural information from airborne transient electromagnetic(ATEM)data primarily involves data processing and inversion.
DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal
The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas.
FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation
Vision Foundation Models (VFMs) excel in generalization due to large-scale pretraining, but fine-tuning them for Domain Generalized Semantic Segmentation (DGSS) while maintaining this ability remains challenging.
SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction
We propose the Representation Diffusion Transformer architecture, and a relative positional bias is added when calculating attention, enabling the diffusion model to achieve global modeling capability for seismic data.
ChangeDiff: A Multi-Temporal Change Detection Data Generator with Flexible Text Prompts via Diffusion Model
Specifically, we propose multi-class distribution-guided text prompts (MCDG-TP), allowing for layouts to be generated flexibly through controllable classes and their corresponding ratios.
SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote Sensing
However, the application of ST to RS image segmentation remains underexplored.
3-D Magnetotelluric Deep Learning Inversion Guided by Pseudo-Physical Information
To efficiently achieve data-physical dual-driven MT deep learning inversion for large-scale 3-D MT data, we propose using DL forward modeling networks to compute this portion of the loss.
Applying the Lower-Biased Teacher Model in Semi-Supervised Object Detection
I present the Lower Biased Teacher model, an enhancement of the Unbiased Teacher model, specifically tailored for semi-supervised object detection tasks.
RepAct: The Re-parameterizable Adaptive Activation Function
Addressing the imperative need for efficient artificial intelligence in IoT and edge computing, this study presents RepAct, a re-parameterizable adaptive activation function tailored for optimizing lightweight neural networks within the computational limitations of edge devices.
DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image Clarity
For the task of low-light image enhancement, deep learning-based algorithms have demonstrated superiority and effectiveness compared to traditional methods.
Ranked #1 on
Low-Light Image Enhancement
on LOLv2-synthetic
Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation
Specifically, we introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples by nearest neighbor retrieval and guides the reliable optimization of unstable samples by bi-level learning.
Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval
Our method comprises three key innovations: (1) Multi-scale Cross-Modal Alignment Transformer (MSCMAT), which computes cross-attention between single-scale image features and localized text features, integrating global textual context to derive a matching score matrix within a mini-batch, (2) a multi-scale cross-modal semantic alignment loss that enforces semantic alignment across scales, and (3) a cross-scale multi-modal semantic consistency loss that uses the matching matrix from the largest scale to guide alignment at smaller scales.
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion
Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy.
SeisFusion: Constrained Diffusion Model with Input Guidance for 3D Seismic Data Interpolation and Reconstruction
In response to this challenge, we propose a novel diffusion model reconstruction framework tailored for 3D seismic data.
Disentangle Estimation of Causal Effects from Cross-Silo Data
Estimating causal effects among different events is of great importance to critical fields such as drug development.
Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification
Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities.
Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation
This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics.
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection can significantly benefit patients.
Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation
Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of efficiency and accuracy for large and deep GNNs.
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction
And, a novel multiscale STFT-based feature extraction method combined with 3D-CNN architecture is proposed to accurately capture predictive probabilities of samples.
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain.
Learning Pseudo-Relations for Cross-domain Semantic Segmentation
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to the unlabeled target domain.
PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition
Sleep stage recognition is crucial for assessing sleep and diagnosing chronic diseases.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
FastATDC: Fast Anomalous Trajectory Detection and Classification
Here, we present a careful theoretical and empirical analysis of the ATDC algorithm, showing that the calculation of anomaly scores in both stages can be simplified, and that the second stage of the algorithm is much more important than the first stage.
A comment-driven evidence appraisal approach for decision-making when only uncertain evidence available
Conclusions: Comment-derived evidence assertions have the potential as an evidence appraisal tool for heuristic decisions based on the accuracy, sensitivity, and efficiency of evidence-comment networks.
More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation
Feature alignment between domains is one of the mainstream methods for Unsupervised Domain Adaptation (UDA) semantic segmentation.
Phase Shift of Planetary Waves and Wave--Jet Resonance on Tidally Locked Planets
This study improves the understanding of wave--mean flow interactions on tidally locked planets.
Atmospheric and Oceanic Physics Earth and Planetary Astrophysics Fluid Dynamics
A universal simulating framework for quantum key distribution systems
Our framework focuses on realistic characters of optical devices and system structures.
Quantum Physics Optics
Drug–target affinity prediction using graph neural network and contact maps
Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area.
Ranked #3 on
Drug Discovery
on LIT-PCBA(ESR1_ant)
Better and Faster: Exponential Loss for Image Patch Matching
Recent studies on image patch matching are paying more attention on hard sample learning, because easy samples do not contribute much to the network optimization.
Molecule Property Prediction Based on Spatial Graph Embedding
And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning.
Ranked #10 on
Graph Regression
on Lipophilicity
Real-time 'Actor-Critic' Tracking
In this work, we propose a novel tracking algorithm with real-time performance based on the âActor-Criticâ framework.
Skeleton-based Action Recognition Using LSTM and CNN
Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation.
SAFETY: Secure gwAs in Federated Environment Through a hYbrid solution with Intel SGX and Homomorphic Encryption
In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure high efficiency and privacy at the same time.
Cryptography and Security
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem.
