Xin Li Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Professor 10 Months

University of Arkansas at Fayetteville

faculty

37 h-index 363 pubs 9,580 cited

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Biography and Research Information

OverviewAI-generated summary

Xin Li's research group at the University of Arkansas at Fayetteville focuses on advancements in artificial intelligence, particularly in the areas of deep learning, computer vision, and neural networks. Their work has produced publications on model-guided deep hyperspectral image super-resolution and deep hyperspectral image fusion networks, indicating a focus on enhancing image quality and information extraction from hyperspectral data.

The group also investigates self-supervised learning techniques for point cloud analysis, exploring rotation invariance and the integration of local geometry with global topology. Furthermore, their research extends to robust action recognition using directed attention in Transformer models. Recent publications also highlight applications of AI in emerging fields, including the use of ChatGPT to assist beginners in bioinformatics and self-supervised kernel estimation for blind image deblurring.

With a high-impact researcher designation, Xin Li has accumulated a significant body of work, evidenced by an h-index of 37 and over 9,580 citations across 363 publications. The active research lab website and the leadership of a research group suggest a dynamic and ongoing contribution to the field.

Metrics

  • h-index: 37
  • Publications: 363
  • Citations: 9,580

Selected Publications

  • A neuronal code for object representation and memory in the human amygdala and hippocampus (2025) DOI
  • Insect-Foundation: A Foundation Model and Large-Scale 1M Dataset for Visual Insect Understanding (2024) DOI
  • MMGInpainting: Multi-Modality Guided Image Inpainting Based on Diffusion Models (2024) DOI
  • GAFlow: Incorporating Gaussian Attention into Optical Flow (2023) DOI
  • Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus (2023) DOI
  • Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing (2023) DOI
  • GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph (2023) DOI
  • Comprehensive Social Trait Judgments From Faces in Autism Spectrum Disorder (2023) DOI
  • Self-supervised Non-uniform Kernel Estimation with Flow-based Motion Prior for Blind Image Deblurring (2023) DOI
  • Robust Geometry-Dependent Attack for 3D Point Clouds (2023) DOI
  • Spatially Varying Prior Learning for Blind Hyperspectral Image Fusion (2023) DOI
  • Uncertainty-Driven Knowledge Distillation for Language Model Compression (2023) DOI
  • Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis (2023) DOI
  • Empowering beginners in bioinformatics with ChatGPT (2023) DOI
  • Empowering Beginners in Bioinformatics with ChatGPT (2023) DOI

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