Search Results for author:
Found 77 papers, 31 papers with code
Saliency deep embedding for aurora image search
In this paper, we aim to explore a new search method for images captured with circular fisheye lens, especially the aurora images.
G-SMOTE: A GMM-based synthetic minority oversampling technique for imbalanced learning
In this paper, the focus is to develop a robust synthetic minority oversampling technique which falls the umbrella of data level approaches.
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping
Deep neural networks (DNNs) have a high accuracy on image classification tasks.
Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods
Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients's quality of life.
G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data
The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image.
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning
In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation models available.
Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems
These enhancements are tasked with the precise retrieval and fusion of shared and individual data.
Information Theory Signal Processing Information Theory
Underwater image enhancement with Image Colorfulness Measure
To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model.
A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms
In this study, we offer a two-step surface-based deep learning pipeline that achieves significantly higher performance.
Improved Preterm Prediction Based on Optimized Synthetic Sampling of EHG Signal
Preterm labor is the leading cause of neonatal morbidity and mortality and has attracted research efforts from many scientific areas.
Towards Dynamic Urban Bike Usage Prediction for Station Network Reconfiguration
To fill this gap, in this work we propose a novel and efficient bike station-level prediction algorithm called AtCoR, which can predict the bike usage at both existing and new stations (candidate locations during reconfiguration).
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction
We propose a continuous neural network architecture, termed Explainable Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step time series prediction at arbitrary time points.
Sparse Array of Sub-surface Aided Anti-blockage mmWave Communication Systems
An approximated ergodic spectral efficiency of the SAoS aided system is derived and the performance impact of the SAoS design is evaluated.
Information Theory Information Theory
Syncretic Modality Collaborative Learning for Visible Infrared Person Re-Identification
Visible infrared person re-identification (VI-REID) aims to match pedestrian images between the daytime visible and nighttime infrared camera views.
Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning Scheme
Existing VSR methods are mostly trained and evaluated on synthetic datasets, where the LR videos are uniformly downsampled from their high-resolution (HR) counterparts by some simple operators (e. g., bicubic downsampling).
Measure of Strength of Evidence for Visually Observed Differences between Subpopulations
For measuring the strength of visually-observed subpopulation differences, the Population Difference Criterion is proposed to assess the statistical significance of visually observed subpopulation differences.
Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps
Whole-brain surface extraction is an essential topic in medical imaging systems as it provides neurosurgeons with a broader view of surgical planning and abnormality detection.
Sketch-based Normal Map Generation with Geometric Sampling
This paper proposes a deep generative model for generating normal maps from users sketch with geometric sampling.
Imaging dynamics beneath turbid media via parallelized single-photon detection
Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task.
Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words
Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information.
Clinical Relation Extraction Using Transformer-based Models
The goal of this study is to systematically explore three widely used transformer-based models (i. e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain.
A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models
In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes).
Tracing Text Provenance via Context-Aware Lexical Substitution
Tracing text provenance can help claim the ownership of text content or identify the malicious users who distribute misleading content like machine-generated fake news.
Generalised Image Outpainting with U-Transformer
In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalised image outpainting problem.
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift
Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data.
Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
In this paper, we learn an Adaptive Confidence Margin (Ada-CM) to fully leverage all unlabeled data for semi-supervised deep facial expression recognition.
Facial Expression Recognition
Facial Expression Recognition (FER)
Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding
We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5mm tissue-like phantom made with rapidly decorrelating dynamic scattering media.
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively fuse multiple image information to reconstruct high-precision 3D models.
Tensorial tomographic differential phase-contrast microscopy
We report Tensorial Tomographic Differential Phase-Contrast microscopy (T2DPC), a quantitative label-free tomographic imaging method for simultaneous measurement of phase and anisotropy.
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency.
Efficient Human-in-the-loop System for Guiding DNNs Attention
Attention guidance is an approach to addressing dataset bias in deep learning, where the model relies on incorrect features to make decisions.
Outpainting by Queries
Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision.
Normalized Feature Distillation for Semantic Segmentation
As a promising approach in model compression, knowledge distillation improves the performance of a compact model by transferring the knowledge from a cumbersome one.
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization
Due to the uneven distribution of offset points, these existing methods can hardly cluster all instance points.
Ranked #3 on
3D Instance Segmentation
on S3DIS
3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization
Although deep-learning based methods for monocular pedestrian detection have made great progress, they are still vulnerable to heavy occlusions.
Ranked #2 on
Multiview Detection
on Wildtrack
(using extra training data)
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables
While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods.
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation
Generating consistent and high-quality images from given texts is essential for visual-language understanding.
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation
Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets.
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
However, it is difficult to draw a proper scene graph for image retrieval, image generation, and multi-modal applications.
Imaging across multiple spatial scales with the multi-camera array microscope
This article experimentally examines different configurations of a novel multi-camera array microscope (MCAM) imaging technology.
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i. e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i. e., opioid use), and evaluate the extraction rate of SDoH using cancer populations.
SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing
We propose an interactive editing method that allows humans to help deep neural networks (DNNs) learn a latent space more consistent with human knowledge, thereby improving classification accuracy on indistinguishable ambiguous data.
Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation
On the one hand, the unidirectional projection enforces our model focused more on the core task, i. e., 3D segmentation; on the other hand, unlocking the bidirectional to unidirectional projection enables a deeper cross-domain semantic alignment and enjoys the flexibility to fuse better and complicated features from very different spaces.
Large-scale single-photon imaging
Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing.
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots
To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period.
OSIS: Efficient One-stage Network for 3D Instance Segmentation
Current 3D instance segmentation models generally use multi-stage methods to extract instance objects, including clustering, feature extraction, and post-processing processes.
Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures
Materials and methods: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes.
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the two datasets by 1%~3% and 0. 7%~1. 3%, respectively.
Clinical Concept Extraction
Machine Reading Comprehension
+3
Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer
In this work, students were trained on a logic tutor that supports a default forward-chaining (FC) and a backward-chaining (BC) strategy.
Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph
The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history.
Watermarking Text Generated by Black-Box Language Models
To allow third-parties to autonomously inject watermarks into generated text, we develop a watermarking framework for black-box language model usage scenarios.
An Offline Time-aware Apprenticeship Learning Framework for Evolving Reward Functions
Apprenticeship learning (AL) is a process of inducing effective decision-making policies via observing and imitating experts' demonstrations.
Sensing Aided Uplink Transmission in OTFS ISAC with Joint Parameter Association, Channel Estimation and Signal Detection
In this work, we study sensing-aided uplink transmission in an integrated sensing and communication (ISAC) vehicular network with the use of orthogonal time frequency space (OTFS) modulation.
LLM Paternity Test: Generated Text Detection with LLM Genetic Inheritance
Large language models (LLMs) can generate texts that carry the risk of various misuses, including plagiarism, planting fake reviews on e-commerce platforms, or creating inflammatory false tweets.
A Study of Generative Large Language Model for Medical Research and Healthcare
This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection
We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample, facilitating the learning of discriminative representations of anomaly instances.
A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task.
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.
A Benchmark for Chinese-English Scene Text Image Super-resolution
Scene Text Image Super-resolution (STISR) aims to recover high-resolution (HR) scene text images with visually pleasant and readable text content from the given low-resolution (LR) input.
Gradient constrained sharpness-aware prompt learning for vision-language models
This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i. e., improving the performance on unseen classes while maintaining the performance on seen classes.
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers
Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting.
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
We evaluated the transfer learning ability of the prompt-based learning algorithms in a cross-institution setting.
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution
To ensure the content consistency among adjacent frames, we exploit the temporal dynamics in LR videos to guide the diffusion process by optimizing the latent sampling path with a motion-guided loss, ensuring that the generated HR video maintains a coherent and continuous visual flow.
A Dynamic Network for Efficient Point Cloud Registration
For the point cloud registration task, a significant challenge arises from non-overlapping points that consume extensive computational resources while negatively affecting registration accuracy.
A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems
Accurate channel state information (CSI) is essential for downlink precoding at the base station (BS), especially for frequency FDD wideband massive MIMO systems with OFDM.
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.
Semantic-aware Data Augmentation for Text-to-image Synthesis
In particular, we propose to augment texts in the semantic space via an Implicit Textual Semantic Preserving Augmentation ($ITA$), in conjunction with a specifically designed Image Semantic Regularization Loss ($L_r$) as Generated Image Semantic Conservation, to cope well with semantic mismatch and collapse.
PairingNet: A Learning-based Pair-searching and -matching Network for Image Fragments
In this paper, we propose a learning-based image fragment pair-searching and -matching approach to solve the challenging restoration problem.
Unraveling Batch Normalization for Realistic Test-Time Adaptation
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to inaccurate target estimation.
Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysis
Additional offsets and modulation scalars are learned on the whole point features, which shift the deformable reference points to the regions of interest.
Toward Accurate and Temporally Consistent Video Restoration from Raw Data
Extensive experiments demonstrate the leading VJDD performance of our method in term of restoration accuracy, perceptual quality and temporal consistency.
Data-driven Option Pricing
We propose an innovative data-driven option pricing methodology that relies exclusively on the dataset of historical underlying asset prices.
CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning
Current multi-modal benchmarks for domain-specific knowledge concentrate on multiple-choice questions and are predominantly available in English, which imposes limitations on the comprehensiveness of the evaluation.
Rethinking Multi-domain Generalization with A General Learning Objective
Multi-domain generalization (mDG) is universally aimed to minimize the discrepancy between training and testing distributions to enhance marginal-to-label distribution mapping.
Towards Understanding the Influence of Reward Margin on Preference Model Performance
Reinforcement Learning from Human Feedback (RLHF) is a widely used framework for the training of language models.
