Chase Rainwater Source Confirmed
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor / Department Chairperson
University of Arkansas at Fayetteville
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Biography and Research Information
OverviewAI-generated summary
Chase Rainwater's research focuses on advanced computer vision and machine learning techniques, particularly for applications in aerial imagery and domain adaptation. His work investigates novel neural network architectures, such as transformers, to improve the accuracy and efficiency of image segmentation and geolocalization tasks. Recent publications include "AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation" (2024, 2023) and "Direct Aerial Visual Geolocalization Using Deep Neural Networks" (2021).
Rainwater also explores methods for domain adaptation, which allows models trained on one dataset to perform well on data from a different distribution. This is evident in his publications "BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation" (2021) and "EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring" (2022). His research extends to vision-language tasks, as seen in "VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning" (2022). He leads a research group and has a significant publication record, with an h-index of 15 and over 900 citations across 54 publications.
His federally funded work includes two NSF Convergence Accelerator awards totaling over $5.7 million. These grants support projects aimed at empowering regional food systems and bridging small farms to regional food supply chains through data-driven agriculture. Rainwater collaborates with several researchers at the University of Arkansas at Fayetteville, including Taisei Hanyu and Jackson Cothren.
Metrics
- h-index: 15
- Publications: 54
- Citations: 908
Selected Publications
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking (2025) DOI
- RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection (2025) DOI
- A Bi-Modular Auto Encoder-Based Unsupervised Degradation Detection Methodology for Remaining Useful Life Prediction (2024) DOI
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation (2024) DOI
- A Bi-Modular Auto Encoder-Based Unsupervised Degradation Detection Methodology for Remaining Useful Life Prediction (2024) DOI
- VLCAP: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning (2022) DOI
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022) DOI
- Direct Aerial Visual Geolocalization Using Deep Neural Networks (2021) DOI
- BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation (2021) DOI
Federal Grants 2 $5,742,469 total
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