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Found 25 papers, 12 papers with code
P²Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
The task is extremely challenging because of the vast areas of non-texture regions in these scenes.
Gaussian Opacity Fields: Efficient and Compact Surface Reconstruction in Unbounded Scenes
Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time.
2D Gaussian Splatting for Geometrically Accurate Radiance Fields
3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking.
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning
The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives.
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.
Mip-Splatting: Alias-free 3D Gaussian Splatting
Recently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency.
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.
DebSDF: Delving into the Details and Bias of Neural Indoor Scene Reconstruction
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.
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing
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.
Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods
The ultrasound characteristics of thyroid nodules guide the evaluation of thyroid cancer in patients with thyroid nodules.
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
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.
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.
PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes
PlaneDepth estimates the depth distribution using a Laplacian Mixture Model based on orthogonal planes for an input image.
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
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.
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
Ranked #6 on
Autonomous Driving
on CARLA Leaderboard
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).
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.
AS-MLP: An Axial Shifted MLP Architecture for Vision
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.
Ranked #13 on
Semantic Segmentation
on DensePASS
P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
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.
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement
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.
Believe It or Not, We Know What You Are Looking at!
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.
Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
In the first stage, we train a CNN to map each pixel to an embedding space where pixels from the same plane instance have similar embeddings.
Ranked #1 on
Plane Instance Segmentation
on NYU Depth v2
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States
To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW).
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
We propose a novel investment decision strategy (IDS) based on deep learning.
Computational Finance
