Search Results for author:
Found 19 papers, 12 papers with code
P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching
Biological processes, functions, and properties are intricately linked to the ensemble of protein conformations, rather than being solely determined by a single stable conformation.
MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling
Specifically, MixEHR-Nest detects multiple subtopics from each phenotype topic, whose prior is guided by the expert-curated phenotype concepts such as Phenotype Codes (PheCodes) or Clinical Classification Software (CCS) codes.
TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
In many domains, such as healthcare, time-series data is often irregularly sampled with varying intervals between observations.
Plane2Depth: Hierarchical Adaptive Plane Guidance for Monocular Depth Estimation
Specifically, in the proposed plane guided depth generator (PGDG), we design a set of plane queries as prototypes to softly model planes in the scene and predict per-pixel plane coefficients.
ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation
Our method decomposes metric depth into scene scale and relative depth, and predicts them through a semantic-aware scale prediction (SASP) module and an adaptive relative depth estimation (ARDE) module, respectively.
Ranked #1 on
Monocular Depth Estimation
on DIODE Outdoor
OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
It has long been challenging to recover the underlying dynamic 3D scene representations from a monocular RGB video.
UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models
In order to answer this question, we propose the UNO Arena based on the card game UNO to evaluate the sequential decision-making capability of LLMs and explain in detail why we choose UNO.
Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning
Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in the healthcare domain.
TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces
This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop.
NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
In this paper, we aim to model 3D scene dynamics from multi-view videos.
TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare
However, the development of PTMs on healthcare time-series data is lagging behind. This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale time series and ability to capture long-term temporal dependencies.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.
Ranked #2 on
Unsupervised Monocular Depth Estimation
on KITTI-C
(using extra training data)
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
In this paper, we study the problem of 3D object segmentation from raw point clouds.
ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation
We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions.
Multi-scale Matching Networks for Semantic Correspondence
We follow the coarse-to-fine matching strategy and build a top-down feature and matching enhancement scheme that is coupled with the multi-scale hierarchy of deep convolutional neural networks.
Ranked #11 on
Semantic correspondence
on SPair-71k
Supervised multi-specialist topic model with applications on large-scale electronic health record data
Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine.
Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation
In recognition-based action interaction, robots' responses to human actions are often pre-designed according to recognized categories and thus stiff.
Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction
Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction.
