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20112026
most citedOReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery

11 citations · 27 across the 16 of their papers we have counts for

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cs.CV2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

Aayush Dhakal, Subash Khanal, Srikumar Sastry +4

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state…

cs.CV2024★ 11 cited

OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery

Philipe Dias, Aristeidis Tsaris, Jordan Bowman +4

While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to bil…

cs.CV2020

Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation

Abhishek K Dubey, Michael T Young, Christopher Stanley +2

Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a larg…

cs.CV2019

Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics

Dalton Lunga, Jonathan Gerrand, Hsiuhan Lexie Yang +2

The shear volumes of data generated from earth observation and remote sensing technologies continue to make major impact; leaping key geospatial applications into the dual data and…

cs.CV2018★ 1 cited

Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States

Hsiuhan Lexie Yang, Jiangye Yuan, Dalton Lunga +3

Establishing up-to-date large scale building maps is essential to understand urban dynamics, such as estimating population, urban planning and many other applications. Although man…

cs.CV2017

Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches

Dalton Lunga, Lexie Yang, Budhendra Bhaduri

Very large overhead imagery associated with ground truth maps has the potential to generate billions of training image patches for machine learning algorithms. However, random samp…