PeopleofSuperintelligence

Researcher

Hui Li

Also known as 李辉, hli1221

Hui Li is a computer vision researcher whose deep-learning work fuses infrared and visible images. They studied at Jiangnan University, coauthored DenseFuse and RFN-Nest, then coauthored LRRNet and CrossFuse. They appeared in Stanford University's World’s Top 2% Scientists ranking from 2022 to 2025. Since July 2025, they have been an associate professor at Jiangnan University's School of Artificial Intelligence and Computer Science.

Badges

Timeline

  1. 2011–2018

    Degrees at Jiangnan University

    They study computer science and technology at Jiangnan University's School of Internet of Things Engineering from 2011 to 2015 and earn a B.A. in 2015. From 2016 to 2018, they pursue a master's degree at the Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence.

  2. 2018–2021

    A PhD under Xiao-Jun Wu

    They complete a PhD in control science and engineering at Jiangnan University in December 2021 under Prof. Xiao-Jun Wu, at the Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence.

  3. 2018–2021

    DenseFuse and RFN-Nest

    In 2018, they coauthor DenseFuse, a deep-learning architecture for infrared and visible image fusion. With Xiao-Jun Wu and Josef Kittler, they publish RFN-Nest in 2021, an end-to-end residual fusion network trained in two stages.

  4. January 2022–July 2025

    Lecturer at Jiangnan

    From January 2022 to July 2025, they serve as a lecturer at Jiangnan University's School of Artificial Intelligence and Computer Science.

  5. 2022–2025

    World’s Top 2% Scientists

    They appear in Stanford University's World’s Top 2% Scientists ranking in 2022, 2023, 2024, and 2025.

  6. 2023

    LRRNet for image fusion

    In 2023, they coauthor LRRNet with Tianyang Xu, Xiao-Jun Wu, Jiwen Lu, and Josef Kittler, a learnable-representation network for infrared and visible image fusion.

  7. 2024

    CrossFuse for image fusion

    In 2024, with Xiao-Jun Wu, they develop CrossFuse, which uses cross-attention to enhance complementary information in infrared and visible image fusion.

  8. Since July 2025

    Associate professor at Jiangnan

    Since July 2025, they are an associate professor in Jiangnan University's School of Artificial Intelligence and Computer Science. Their work includes image fusion and RGBT tracking.

Career

  1. Since 2022Jiangnan UniversityLecturer, Associate Professor

Education

  1. 2021Jiangnan UniversityPhD
  2. 2018Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational IntelligenceMaster's degree
  3. 2015Jiangnan UniversityB.A.

From the biography.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Jiangnan University. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  3. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. 43% of living people shaping AI work outside their country of birthWe estimate that 43% of living people shaping AI work outside their country of birth (520 of 1,198 people with known birth and employment countries).

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Hui Li yet. Send us a free-licensed photo.

  1. Hui Li – International Joint Laboratory on AI, Jiangnan University. hli1221.github.io. Checked 21 October 2026.
  2. [1804.08361] DenseFuse: A Fusion Approach to Infrared and Visible Images arxiv.org. Checked 21 October 2026.
  3. [2103.04286] RFN-Nest: An end-to-end residual fusion network for infrared and visible images arxiv.org. Checked 21 October 2026.
  4. [2304.05172] LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images arxiv.org. Checked 21 October 2026.
  5. [2406.10581] CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approach arxiv.org. Checked 21 October 2026.
  6. Hui Li(李辉) (hli1221) - Gitee.com gitee.com. Checked 21 October 2026.