Pierce Helton Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
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Research Areas
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Biography and Research Information
OverviewAI-generated summary
Pierce Helton's research focuses on the application of artificial intelligence and machine learning techniques to visual perception problems, particularly in the context of domain adaptation. His work addresses challenges in adapting AI models trained on one dataset or domain to perform effectively on different, yet related, datasets or domains, often with limited or no labeled data in the target domain. Recent publications explore methods for equipollent domain adaptation in image deblurring and continual unsupervised domain adaptation for self-driving car perception systems.
Helton has also investigated AI applications in biological contexts, including the development of systems for the automatic imaging, quantification, and identification of arthropods. His research network includes collaborations with Thanh-Dat Truong, Ashley P. G. Dowling, Khoa Luu, and Chase Rainwater at the University of Arkansas at Fayetteville and the Arkansas Agricultural Experiment Station. Helton's scholarly contributions are reflected in an h-index of 3 and 19 citations across 5 publications.
Metrics
- h-index: 3
- Publications: 5
- Citations: 19
Selected Publications
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CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars (2024)
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EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022)
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Artificial Intelligence System for Automatic Imaging, Quantification, and Identification of Arthropods in Leaf Litter and Pitfall Samples (2022)
Collaboration Network
Top Collaborators
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- Artificial Intelligence System for Automatic Imaging, Quantification, and Identification of Arthropods in Leaf Litter and Pitfall Samples
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
- Artificial Intelligence System for Automatic Imaging, Quantification, and Identification of Arthropods in Leaf Litter and Pitfall Samples
- CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
- CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
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