Search Results for author:
Found 53 papers, 17 papers with code
Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI
Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relationships with WM microstructure.
PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment
Furthermore, in the model fine-tuning stage, the learned content-aware features serve as a guide to fuse the point cloud quality features extracted from different perspectives.
Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment
Furthermore, in the model fine-tuning stage, we propose a semantic-guided multi-view fusion module to effectively integrate the features of projected images from multiple perspectives.
Super-Resolution Multi-Contrast Unbiased Eye Atlases With Deep Probabilistic Refinement
These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.
Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation
For the first time, our framework explores three types of bilateral entanglements within AVS: pixel entanglement, modality entanglement, and temporal entanglement.
Uncertainty-aware Language Modeling for Selective Question Answering
We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs capable of estimating uncertainty with every prediction.
Predicting Age from White Matter Diffusivity with Residual Learning
We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution
We hypothesize that deformable convolution can be an exploratory alternative to combine all advantages from the previous operators, providing long-range dependency, adaptive spatial aggregation and computational efficiency as a foundation backbone.
SJTU-TMQA: A quality assessment database for static mesh with texture map
However, little research has been done on the quality assessment of textured meshes, which hinders the development of quality-oriented applications, such as mesh compression and enhancement.
Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization
We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area.
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models
Experiments in various situations demonstrate the advantages of our method compared to strong baselines on this task.
Ranked #10 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration
Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available.
GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity
A two-step patch cropping algorithm and a patch texture mapping module refine the size of 1-hop geodesic patches and build the relationship between the mesh geometry and color information, resulting in the generation of 1-hop textured geodesic patches.
TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations
Dynamic colored meshes (DCM) are widely used in various applications; however, these meshes may undergo different processes, such as compression or transmission, which can distort them and degrade their quality.
TSMD: A Database for Static Color Mesh Quality Assessment Study
Static meshes with texture map are widely used in modern industrial and manufacturing sectors, attracting considerable attention in the mesh compression community due to its huge amount of data.
Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-relevance Degradation Description
Second, to reduce the significant domain discrepancy, we establish an intermediate domain, the description domain, based on insights from subjective experiments, by considering the domain relevance among samples located in the perception domain and learning a structured latent space.
Cascade Subspace Clustering for Outlier Detection
A suitable Markov Chain can be defined on the self-representation and it allows us to recognize the difference between inliers and outliers.
Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template.
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
We hypothesize that convolution with LK sizes is limited to maintain an optimal convergence for locality learning.
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
Reduced Reference Quality Assessment for Point Cloud Compression
Specifically, we use the attribute and geometry quantization steps of different compression methods (i. e., V-PCC, G-PCC and AVS) to infer the point cloud quality, assuming that the point clouds have no other distortions before compression.
Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training
The proposed pipeline is effective and robust in extracting muscle groups on 2D single slice CT thigh images. The container is available for public use at https://github. com/MASILab/DA_CT_muscle_seg
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
To extract effective features for PCQA, we propose a new graph convolution kernel, i. e., GPAConv, which attentively captures the perturbation of structure and texture.
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation
We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.
TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment
The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner.
Point Cloud Quality Assessment using 3D Saliency Maps
Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, called point cloud quality assessment using 3D saliency maps (PQSM).
Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation
Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships.
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.
Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models
External experiments on 20 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset that contains longitudinal single abdominal slices validate that our method can harmonize the slice positional variance in terms of muscle and visceral fat area.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling
Objective quality assessment of 3D point clouds is essential for the development of immersive multimedia systems in real-world applications.
Personality-Driven Social Multimedia Content Recommendation
In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale.
Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation
In this paper, we propose a novel unsupervised approach that leverages pairwise contrast-enhanced CT (CECT) context to compute non-contrast segmentation without ground-truth label.
Characterizing Renal Structures with 3D Block Aggregate Transformers
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.
No-Reference Point Cloud Quality Assessment via Domain Adaptation
We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds.
Ranked #4 on
Point Cloud Quality Assessment
on WPC
Sampling Before Training: Rethinking the Effect of Edges in the Process of Training Graph Neural Networks
Graph neural networks (GNN) demonstrate excellent performance on many graph-based tasks; however, they also impose a heavy computational burden when trained on a large-scale graph.
Which One is Better: Assessing Objective Metrics for Point Cloud Compression
Point cloud compression (PCC) has made remarkable achievement in recent years.
Active Reinforcement Learning over MDPs
This paper proposes a framework of Active Reinforcement Learning (ARL) over MDPs to improve generalization efficiency in a limited resource by instance selection.
Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling
We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues.
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask.
MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy
In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).
Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric
Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years.
Ranked #5 on
Point Cloud Quality Assessment
on WPC
Blind Image Quality Assessment
Point Cloud Quality Assessment
SoMin.ai: Personality-Driven Content Generation Platform
Guided by the MBTI personality type, automatically derived from a user social network content, SoMin. ai generates new social media content based on the preferences of other users with a similar personality type aiming at enhancing the user experience on social networking venues as well diversifying the efforts of marketers when crafting new content for digital marketing campaigns.
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding
Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference.
Inferring Point Cloud Quality via Graph Similarity
We propose the GraphSIM -- an objective metric to accurately predict the subjective quality of point cloud with superimposed geometry and color impairments.
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).
I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance
Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks.
Parallel Exploration via Negatively Correlated Search
Empirical results show that the significant advantages of NCS over the compared state-of-the-art methods can be highly owed to the effective parallel exploration ability.
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection
In the first prediction part, we focus on predicting the downward trend, which is an earlier stage of the customer lifecycle compared to churn.
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction
Image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction aims to recover detailed information from low-resolution images and reconstruct them into high-resolution images.
Fractional Calculus In Image Processing: A Review
Over the last decade, it has been demonstrated that many systems in science and engineering can be modeled more accurately by fractional-order than integer-order derivatives, and many methods are developed to solve the problem of fractional systems.
Practical Algorithms for Learning Near-Isometric Linear Embeddings
Motivated by non-negative matrix factorization, we reformulate our problem into a Frobenius norm minimization problem, which is solved by the Alternating Direction Method of Multipliers (ADMM) and develop an algorithm, FroMax.
