Search Results for author:
Found 57 papers, 15 papers with code
SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts
Based on the confidence map, we select the top-k pixels as the positive points set and choose the negative points set using a Gaussian distribution, followed by independent K-means clustering for both sets.
Weak but influential: Nonlinear contributions of structural connectivity to human cognitive abilities and brain functions
Using the Human Connectome Project dataset (n=999) and multiple tractography algorithms, we constructed the whole-brain structural connectivity with heterogeneous weights of streamline numbers.
Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy
Goal-conditioned dynamic manipulation is inherently challenging due to complex system dynamics and stringent task constraints, particularly in deformable object scenarios characterized by high degrees of freedom and underactuation.
Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach
We developed cardiac output analysis and virtual angiography systems, implemented guidewire 3D reconstruction using binocular stereo vision, and evaluated the system through angiography validation and CABG training applications.
Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning
Objective: To develop and evaluate a dynamic cardiovascular holographic visualization tool for preoperative CABG planning.
MFH: A Multi-faceted Heuristic Algorithm Selection Approach for Software Verification
Our approach leverages the heuristics that verifiers producing correct results typically implement certain appropriate algorithms, and the supported algorithms by these verifiers indirectly reflect which ones are potentially applicable.
RSRWKV: A Linear-Complexity 2D Attention Mechanism for Efficient Remote Sensing Vision Task
High-resolution remote sensing analysis faces challenges in global context modeling due to scene complexity and scale diversity.
FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from Few Images
We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images.
Time-Varying Coronary Artery Deformation: A Dynamic Skinning Framework for Surgical Training
Purpose: This study proposes a novel anatomically-driven dynamic modeling framework for coronary arteries using skeletal skinning weights computation, aiming to achieve precise control over vessel deformation while maintaining real-time performance for surgical simulation applications.
Mono3DVLT: Monocular-Video-Based 3D Visual Language Tracking
Visual-Language Tracking (VLT) is emerging as a promising paradigm to bridge the human-machine performance gap.
FRESA: Feedforward Reconstruction of Personalized Skinned Avatars from Few Images
We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images.
JADE: Joint-aware Latent Diffusion for 3D Human Generative Modeling
Our key insight is a joint-aware latent representation that decomposes human bodies into skeleton structures, modeled by joint positions, and local surface geometries, characterized by features attached to each joint.
A Greedy Strategy for Graph Cut
To reduce the computational complexity of GGC, only mergers between clusters and their neighbors are considered.
Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often struggle with spatial reasoning.
Clustering Based on Density Propagation and Subcluster Merging
We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space.
Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method
In the graph-based semi-supervised learning, the Green-function method is a classical method that works by computing the Green's function in the graph space.
The Roles of Contextual Semantic Relevance Metrics in Human Visual Processing
This study investigates human visual perception and processing by introducing the metrics of contextual semantic relevance.
FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry
The fusion of both visual and LiDAR measurements is based on a single unified voxel map where the LiDAR module constructs the geometric structure for registering new LiDAR scans and the visual module attaches image patches to the LiDAR points.
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance
Despite the strides, most graph contrastive learning models face challenges: 1) graph augmentation is used to improve learning diversity, but commonly used random augmentation methods may destroy inherent semantics and cause noise; 2) the fixed positive and negative sample selection strategy is limited to deal with complex real data, thereby impeding the model's capability to capture fine-grained patterns and relationships.
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
We prove that the Marcus mapping solves a specific type of optimal transport problem and demonstrate that solving this problem through Marcus mapping is more efficient than directly applying optimal transport methods.
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
Ensemble learning is a method that leverages weak learners to produce a strong learner.
A Novel Dependency Framework for Enhancing Discourse Data Analysis
By converting both PDTB and RST annotations for the same texts into dependencies, this study also applies ``dependency distance'' metrics to examine the correlation between RST dependencies and PDTB dependencies in English.
Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation
In contrast, we present a novel method aiming for high-quality motion transfer with realistic apparel animation.
Continuous Output Personality Detection Models via Mixed Strategy Training
The traditional personality models only yield binary results.
Textual Similarity as a Key Metric in Machine Translation Quality Estimation
We also found that "hter" actually failed to predict human scores in QE.
Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression
Automated essay scoring (AES) involves predicting a score that reflects the writing quality of an essay.
Robust Capped lp-Norm Support Vector Ordinal Regression
Adhering to this concept, we introduce a new model, Capped $\ell_{p}$-Norm Support Vector Ordinal Regression(CSVOR), that is robust to outliers.
Differential contributions of machine learning and statistical analysis to language and cognitive sciences
This study employs the Buckeye Speech Corpus to illustrate how machine learning and statistical analysis are applied in data-driven research to obtain distinct insights on language production.
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning
Considering the characteristics of trigger generation, data poisoning and model training in backdoor attack, this paper designs a backdoor attack method based on federated learning.
TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches
This study employs deep learning techniques to explore four speaker profiling tasks on the TIMIT dataset, namely gender classification, accent classification, age estimation, and speaker identification, highlighting the potential and challenges of multi-task learning versus single-task models.
Computational Sentence-level Metrics Predicting Human Sentence Comprehension
Our results indicate that these computational sentence-level metrics are exceptionally effective at predicting and elucidating the processing difficulties encountered by readers in comprehending sentences as a whole across a variety of languages.
Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach
Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs.
Multi-class Support Vector Machine with Maximizing Minimum Margin
Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks.
A Novel Normalized-Cut Solver with Nearest Neighbor Hierarchical Initialization
To address the problems, we propose a novel N-Cut solver designed based on the famous coordinate descent method.
DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation
Specifically, for an initial hand-object pose estimated by a base network, we forward it to a physics simulator to evaluate its stability.
OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation
The incorporation of these modules showed superior performance for oral cancer segmentation for two datasets used in this research.
Covert Communication Based on the Poisoning Attack in Federated Learning
Covert communication has become an important area of research in computer security.
Exploring the cloud of feature interaction scores in a Rashomon set
Thus, we recommend exploring feature interaction strengths in a model class of approximately equally accurate predictive models.
Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan
Conclusions: The proposed deep learning algorithms can be an effective tool for early identification of hemorrhage etiologies based on NCCT scans.
On the Global Solution of Soft k-Means
This paper presents an algorithm to solve the Soft k-Means problem globally.
Interacting Hand-Object Pose Estimation via Dense Mutual Attention
In contrast, we propose a novel dense mutual attention mechanism that is able to model fine-grained dependencies between the hand and the object.
Ranked #2 on
hand-object pose
on HO-3D v2
(using extra training data)
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism
The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings.
Deep Reinforcement Learning
Graph Representation Learning
+3
A Compact Neural Network-based Algorithm for Robust Image Watermarking
Digital image watermarking seeks to protect the digital media information from unauthorized access, where the message is embedded into the digital image and extracted from it, even some noises or distortions are applied under various data processing including lossy image compression and interactive content editing.
Transcribing Natural Languages for The Deaf via Neural Editing Programs
This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign language glosses.
New Tight Relaxations of Rank Minimization for Multi-Task Learning
Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace.
Lifespan associations of resting-state brain functional networks with ADHD symptoms
Our findings reveal a lifespan association of brain networks with ADHD symptoms and provide potential shared neural bases of distinct ADHD symptoms in children and adults.
Large-Capacity Image Steganography Based on Invertible Neural Networks
Many attempts have been made to hide information in images, where the main challenge is how to increase the payload capacity without the container image being detected as containing a message.
Local $K$-means: An Efficient Optimization Algorithm And Its Generalization
We also developed a general model that unified LKM, KSUMS, and SC, and discussed the connection among them.
AttentionFlow: Visualising Influence in Networks of Time Series
The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest.
Extraction of the proton mass radius from the vector meson photoproductions near thresholds
We present an analysis of the proton mass radius by studying the $t$-dependence of the differential cross sections of the vector meson photoproductions near the thresholds.
High Energy Physics - Phenomenology
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery
To alleviate this problem, we proposed a novel ensemble and random collaborative representation-based detector (ERCRD) for HAD, which comprises two closely related stages.
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut
In particular, over 15x and 7x speed-up can be obtained with respect to $k$-means on the synthetic dataset of 1 million samples and the benchmark dataset (CelebA) of 200k samples, respectively [GitHub].
Learning Feature Sparse Principal Subspace
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on.
Feature Learning Viewpoint of AdaBoost and a New Algorithm
We explain the rationality of this and illustrate the theorem that when the dimension of these features increases, the performance of SVM would not be worse, which can explain the resistant to overfitting of AdaBoost.
Low Rank Regularization: A Review
Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version.
