Search Results for author: Andrew Feng

Found 20 papers, 6 papers with code

Empowering Federated Learning for Massive Models with NVIDIA FLARE

no code implementations12 Feb 2024 Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh, Adithya Renduchintala, Isaac Yang, Zhihong Zhang, Yuhong Wen, Sean Yang, Kevin Lu, Kristopher Kersten, Camir Ricketts, Daguang Xu, Chester Chen, Yan Cheng, Andrew Feng

In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge.

Federated Learning

Instant Photorealistic Style Transfer: A Lightweight and Adaptive Approach

no code implementations18 Sep 2023 Rong Liu, Enyu Zhao, Zhiyuan Liu, Andrew Feng, Scott John Easley

In this paper, we propose an Instant Photorealistic Style Transfer (IPST) approach, designed to achieve instant photorealistic style transfer on super-resolution inputs without the need for pre-training on pair-wise datasets or imposing extra constraints.

Style Transfer Super-Resolution

Generative AI for Medical Imaging: extending the MONAI Framework

2 code implementations27 Jul 2023 Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.

Anomaly Detection Denoising +2

Co-Speech Gesture Synthesis using Discrete Gesture Token Learning

no code implementations4 Mar 2023 Shuhong Lu, Youngwoo Yoon, Andrew Feng

Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users.

STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset

4 code implementations17 Mar 2022 Meida Chen, Qingyong Hu, Zifan Yu, Hugues Thomas, Andrew Feng, Yu Hou, Kyle McCullough, Fengbo Ren, Lucio Soibelman

Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages.

3D Instance Segmentation 3D Semantic Segmentation

Ground material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach

no code implementations24 Sep 2021 Meida Chen, Andrew Feng, Yu Hou, Kyle McCullough, Pratusha Bhuvana Prasad, Lucio Soibelman

For ground material segmentation, we utilized an existing convolutional neural network architecture (i. e., 3DMV) which was originally designed for segmenting RGB-D sensed indoor data.

Material Classification object-detection +1

3D photogrammetry point cloud segmentation using a model ensembling framework

no code implementations Journal of Computing in Civil Engineering 2020 Meida Chen, Andrew Feng, Kyle McCullough, Pratusha Bhuvana Prasad, Ryan McAlinden, Lucio Soibelman

In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i. e., ground, human-made objects, and vegetation).

3D Reconstruction Point Cloud Segmentation

Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions

no code implementations1 Sep 2020 Kyle McCullough, Andrew Feng, Meida Chen, Ryan McAlinden

A goal of this research is to allow automated monitoring for largescale infrastructure projects, such as railways, to determine reliable metrics that define and predict the direction construction initiatives could take, allowing for a directed monitoring via narrowed and targeted satellite imagery requests.

BIG-bench Machine Learning

Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models

no code implementations21 Aug 2020 Meida Chen, Andrew Feng, Kyle McCullough, Pratusha Bhuvana Prasad, Ryan McAlinden, Lucio Soibelman

At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locations and ground materials (Chen et al., 2019).

Point Cloud Segmentation

Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale

no code implementations NeurIPS 2016 Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar

Deep distributed decision trees and tree ensembles have grown in importance due to the need to model increasingly large datasets.

Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

no code implementations7 Jul 2016 Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza-Yates, Andrew Feng, Erik Ordentlich, Lee Yang, Gavin Owens

For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on.

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