I received my Ph.D. from the College of Computer Science and Engineering, Nanjing University of Science and Technology, under the supervision of Prof. Jian Yang, and co-supervised by Xiang Li from Nankai University.
My research interests include multimodal perception and prompt engineering. I have published 5+ papers 
I am currently seeking postdoctoral positions in areas including embodied AI, multimodal perception, and prompt engineering.
🔥 News
- 2024.11: 🎉 One first-author paper has been accepted to TPAMI 2025.
- 2023.09: 🎉 One first-author paper has been accepted to NeurIPS 2023.
- 2022.09: 🎉 One first-author paper has been selected for an Spotlight!
- 2022.09: 🎉 One first-author paper has been accepted to NeurIPS 2022.
- 2022.03: 🎉 One first-author paper has been selected for an ORAL presentation!
- 2022.03: 🎉 One first-author paper has been accepted to CVPR 2022.
📝 Publications
TPAMI 2025 Fine-Grained Visual Text Prompting
Lingfeng Yang, Xiang Li, Yueze Wang, Xinlong Wang, Jian Yang
- Proposes fine-grained multimodal prompting to enhance large multimodal models’ localization and grounding capability, thereby boosting referring comprehension performance.
- Our work has been adopted by the research group of Prof. Philip H. S. Torr (Oxford University, Marr Prize laureate), who employed the proposed Fine-Grained Visual Prompting (FGVTP) as the core target extractor in their weakly supervised referring segmentation framework.
- Our work has inspired subsequent studies and has been applied to multiple domains, including Egocentric Action Recognition and Compositional Action Recognition for embodied intelligence perception.
NeurIPS 2023 Fine-Grained Visual Prompting
Lingfeng Yang, Yueze Wang, Xiang Li, Xinlong Wang, Jian Yang
- Propose a specific visual prompting technique that enhances referring expression comprehension by highlighting regions of interest through background blurring based on fine-grained segmentation.
- Maintains faster inference speed in the trade-off while achieving more than a 5-point improvement over state-of-the-art methods.
NeurIPS 2022 RecursiveMix: Mixed Learning with History (Spotlight, Top 12.8%)
Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang
- Propose a simple yet effective mixed-data augmentation technique for image classification.
- Enhance model pretraining performance for object detection and semantic segmentation tasks.
CVPR 2022 Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information (Oral, Top 3.3%)
Lingfeng Yang, Xiang Li, Renjie Song, Borui Zhao, Juntian Tao, Shihao Zhou, Jiajun Liang, Jian Yang
- Proposed a dynamic MLP fusion framework for fine-grained image classification by incorporating geo-temporal information.
- Improved classification accuracy on multiple fine-grained datasets.
🎖 Honors and Awards
- 2025.06 Outstanding Graduate (Top 1%)
- 2025.03 Dean’s Medal (Top 1%)
- 2024.12 National Scholarship (Top 5%)
Competition
- 2022.10 First Prize (Team), 5th Open Source Innovation Competition
- 2022.07 1st Place, 2nd Jittor AI Challenge (1st out of 414 teams, ¥50,000 prize)
- 2022.06 2nd Place, SnakeCLEF 2022 (CVPR 2022 Workshop)
- 2021.06 3rd Place, iNaturalist Challenge 2021 (CVPR 2021 Workshop)
- 2020.11 2nd Place, 1st ZhengTu Cup Campus Machine Vision AI Competition (2nd out of 943 teams, ¥150,000 prize)
📖 Educations
- 2020.09 - present, Phd student, College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
- 2016.09 - 2020.06, Undergraduate, College of Science, Nanjing University of Science and Technology, Nanjing, China.
💻 Internships
- 2024.04 - present, Insta360 (Shenzhen Arashi Vision Co., Ltd.), Shenzhen, China.
- 2023.09 - 2024.04, Visual Technology Department, Baidu, Beijing, China.
- 2022.09 - 2023.08, Beijing Academy of Artificial Intelligence (BAAI), Beijing, China.
- 2021.01 - 2022.08, MEGVII Research Institute, Nanjing, China.
💬 Services
Reviewers
- Outstanding Reviewer of CVPR 2025
- TPAMI, NeurIPS, CVPR, ICCV, ECCV