Academic Impacts

My research has received recognition from over 40 Academicians and Fellows, including a Turing Award winner. To date, my work has accumulated 10,000+ citations on Google Scholar and has been featured by authoritative organizations such as IEEE and CSIG. My open-source projects have garnered 4,000+ Stars on GitHub. Representative endorsements include:

  1. Cited by Turing Award Winner: Prof. Yoshua Bengio (Turing Award Winner) cited my work multiple times in a NeurIPS’25 paper, noting that his research was “building upon insights from” my work and adopting it as a key research foundation to achieve significant improvements in his domain.

  2. Recognized by Meta FAIR: The Meta FAIR Team (including Computer Vision pioneer/Distinguished Scientist Ross Girshick, Citations: 580k+) highlighted my work as one of only two representative methods in Remote Sensing within their SAM 2 technical report.

  3. Included in Stanford CS231n Curriculum: My survey on object detection, a Most Popular Article in Proceedings of the IEEE, was selected as reference material for the world-renowned course “CS231n” (2023-2025) taught by Prof. Li Fei-Fei.

  4. Praised by IEEE/AAIA Fellow: Prof. Jocelyn Chanussot (Director at INRIA, France) highly commended the RSPrompter method, stating it “successfully” and “autonomously” adapts large models for instance segmentation tasks, fully affirming the algorithm’s automation capabilities.

  5. Recommended by IEEE/ACM/AAAS Fellow: Academician Dacheng Tao and Prof. Xudong Jiang (IEEE Fellow) introduced my work as a representative method in the top-tier journal TPAMI (2024). They assessed that the method “is capable of simultaneously detecting objects and their attributes”, validating the complementarity of fine-grained attributes for open-world detection.

  6. Affirmed by Fellow of RSC/CAE/EIC: Academician Jonathan Li pointed out in his paper (JAG, 2023) that my algorithm designed a “compact” dual-channel architecture to achieve “efficiency maximization”, successfully establishing spatiotemporal long-range dependencies to enhance feature extraction.

  7. Highlighted by Domain Experts: Prof. Fahad Shahbaz Khan dedicated a separate section to introduce my work in a Remote Sensing survey. Prof. Liangpei Zhang (IEEE Fellow) evaluated the adaptive prompting method in a NeurIPS paper as being “significantly different from other manual prompting methods”, possessing unique advantages.


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