Search Results for author: Zehao Yu

Found 26 papers, 14 papers with code

3D Neural Edge Reconstruction

no code implementations CVPR 2024 Lei LI, Songyou Peng, Zehao Yu, Shaohui Liu, Rémi Pautrat, Xiaochuan Yin, Marc Pollefeys

Real-world objects and environments are predominantly composed of edge features, including straight lines and curves.

Surface Reconstruction

Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes

1 code implementation16 Apr 2024 Zehao Yu, Torsten Sattler, Andreas Geiger

Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time.

Novel View Synthesis Surface Reconstruction

2D Gaussian Splatting for Geometrically Accurate Radiance Fields

1 code implementation26 Mar 2024 Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, Shenghua Gao

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking.

Novel View Synthesis

Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning

no code implementations19 Mar 2024 Cheng Peng, Zehao Yu, Kaleb E Smith, Wei-Hsuan Lo-Ciganic, Jiang Bian, Yonghui Wu

The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives.

Decoder Transfer Learning

Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need

no code implementations11 Dec 2023 Cheng Peng, Xi Yang, Aokun Chen, Zehao Yu, Kaleb E Smith, Anthony B Costa, Mona G Flores, Jiang Bian, Yonghui Wu

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.

Language Modelling Large Language Model +3

Mip-Splatting: Alias-free 3D Gaussian Splatting

1 code implementation CVPR 2024 Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, Andreas Geiger

Recently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency.

Novel View Synthesis

DebSDF: Delving into the Details and Bias of Neural Indoor Scene Reconstruction

no code implementations29 Aug 2023 Yuting Xiao, Jingwei Xu, Zehao Yu, Shenghua Gao

This paper presents \textbf{DebSDF} to address these challenges, focusing on the utilization of uncertainty in monocular priors and the bias in SDF-based volume rendering.

Indoor Scene Reconstruction Surface Reconstruction

Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing

no code implementations31 Mar 2023 Aokun Chen, Daniel Paredes, Zehao Yu, Xiwei Lou, Roberta Brunson, Jamie N. Thomas, Kimberly A. Martinez, Robert J. Lucero, Tanja Magoc, Laurence M. Solberg, Urszula A. Snigurska, Sarah E. Ser, Mattia Prosperi, Jiang Bian, Ragnhildur I. Bjarnadottir, Yonghui Wu

To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes.

Language Modelling Large Language Model

Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures

no code implementations14 Mar 2023 Aokun Chen, Zehao Yu, Xi Yang, Yi Guo, Jiang Bian, Yonghui Wu

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.

Classification Deep Learning +2

Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension

no code implementations14 Mar 2023 Cheng Peng, Xi Yang, Zehao Yu, Jiang Bian, William R. Hogan, Yonghui Wu

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

SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies

no code implementations6 Dec 2022 Zehao Yu, Xi Yang, Chong Dang, Prakash Adekkanattu, Braja Gopal Patra, Yifan Peng, Jyotishman Pathak, Debbie L. Wilson, Ching-Yuan Chang, Wei-Hsuan Lo-Ciganic, Thomas J. George, William R. Hogan, Yi Guo, Jiang Bian, Yonghui Wu

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.

MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction

1 code implementation1 Jun 2022 Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, Andreas Geiger

Motivated by recent advances in the area of monocular geometry prediction, we systematically explore the utility these cues provide for improving neural implicit surface reconstruction.

3D Reconstruction Multi-View 3D Reconstruction +1

A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models

no code implementations10 Aug 2021 Zehao Yu, Xi Yang, Chong Dang, Songzi Wu, Prakash Adekkanattu, Jyotishman Pathak, Thomas J. George, William R. Hogan, Yi Guo, Jiang Bian, Yonghui Wu

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).

Clinical Relation Extraction Using Transformer-based Models

1 code implementation19 Jul 2021 Xi Yang, Zehao Yu, Yi Guo, Jiang Bian, Yonghui Wu

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.

Binary Classification Classification +3

AS-MLP: An Axial Shifted MLP Architecture for Vision

2 code implementations ICLR 2022 Dongze Lian, Zehao Yu, Xing Sun, Shenghua Gao

Our proposed AS-MLP obtains 51. 5 mAP on the COCO validation set and 49. 5 MS mIoU on the ADE20K dataset, which is competitive compared to the transformer-based architectures.

object-detection Object Detection +1

P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation

1 code implementation15 Jul 2020 Zehao Yu, Lei Jin, Shenghua Gao

Furthermore, because those textureless regions in indoor scenes (e. g., wall, floor, roof, \etc) usually correspond to planar regions, we propose to leverage superpixels as a plane prior.

Monocular Depth Estimation Superpixels

Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement

1 code implementation CVPR 2020 Zehao Yu, Shenghua Gao

On one hand, the high-resolution depth map, the data-adaptive propagation method and the Gauss-Newton layer jointly guarantee the effectiveness of our method.

Depth Estimation

Believe It or Not, We Know What You Are Looking at!

1 code implementation4 Jul 2019 Dongze Lian, Zehao Yu, Shenghua Gao

There are two merits for our two-stage solution based gaze following: i) our solution mimics the behavior of human in gaze following, therefore it is more psychological plausible; ii) besides using heatmap to supervise the output of our network, we can also leverage gaze direction to facilitate the training of gaze direction pathway, therefore our network can be more robustly trained.

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