Chong Luo
Chong Luo is a computer-vision researcher at Microsoft Research Asia whose work ranges from visual tracking to data-efficient vision models, multimodal learning and computer-use agents. She earned degrees at Fudan University, the National University of Singapore and Shanghai Jiao Tong University, and joined MSRA in 2003. Across computer vision and multimodal learning, she coauthored SA-Siam, Visual Token Matching, Chameleon, Phi-Ground and LLM2CLIP; Visual Token Matching received an ICLR Outstanding Paper Award in 2023, and LLM2CLIP received an AAAI Outstanding Paper Award in 2026. As of October 2026, she is General Manager of Research Sciences at Microsoft Research Asia and Director of the Shanghai Lab.
- Now at
- Microsoft Research Asia
- Works
- 104
- Citations
- 5,498
- h-index
- 29
Publication numbers from OpenAlex, as of 22 October 2026.
Timeline
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2000–2012
Degrees at Fudan, NUS and SJTU
She earns a B.Sc. in computer science at Fudan University in 2000 and an M.Sc. in computer science at the National University of Singapore in 2002. She completes a Ph.D. in electrical engineering at Shanghai Jiao Tong University in 2012.
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Since 2003
Joining Microsoft Research Asia
In 2003, she joins Microsoft Research Asia (MSRA). Her research interests span computer vision, multimodal AI, image and video understanding and generation, large language models, AI agents and human-AI collaboration.
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2018
A real-time Siamese tracker
In 2018, she coauthors A Twofold Siamese Network for Real-Time Object Tracking. Its SA-Siam model combines a semantic branch and an appearance branch, which the authors train separately to keep their features distinct.
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2023
Visual Token Matching wins at ICLR
With Donggyun Kim, Jinwoo Kim, Seongwoong Cho and Seunghoon Hong, she coauthors Visual Token Matching, a few-shot method for dense prediction tasks including semantic segmentation, depth estimation, edge detection and keypoint detection. The paper receives an ICLR Outstanding Paper Award in 2023.
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2024
Chameleon for dense prediction
In 2024, she coauthors Chameleon, a data-efficient generalist for dense visual prediction. It adapts to unseen tasks across video, 3D, medical, biological and user-interactive settings with at most 50 labeled images.
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July 2025
Phi-Ground for GUI grounding
In July 2025, she coauthors Phi-Ground Tech Report, which presents the Phi-Ground model family for GUI grounding in computer-use agents. The model family achieves state-of-the-art results across all five grounding benchmarks for models under 10B parameters in agent settings.
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2026
LLM2CLIP wins an AAAI award
In 2024, she coauthors LLM2CLIP, an efficient fine-tuning framework that embeds an LLM into pretrained CLIP at nearly standard CLIP fine-tuning cost. The paper receives an AAAI-26 Outstanding Paper Award in 2026.
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As of October 2026
General manager and lab director
As of October 2026, she is General Manager of Research Sciences at Microsoft Research Asia and Director of the Shanghai Lab.
Career
- Since 2003Microsoft Research AsiaComputer vision researcher
- 2006–2026Microsoft
Education
- 2012Shanghai Jiao Tong UniversityPh.D.
- 2002National University of SingaporeM.Sc.
- 2000Fudan UniversityB.Sc.
Sources and credits
Photo: We have no published photo of Chong Luo yet. Send us a free-licensed photo.
- Uncoded Multimedia Transmission - 1st Edition - Feng Wu - Chong Luo -
- Chong Luo at Microsoft Research
- A Twofold Siamese Network for Real-Time Object Tracking
- Announcing the ICLR 2023 Outstanding Paper Award Recipients – ICLR Blog
- Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
- Phi-Ground Tech Report: Advancing Perception in GUI Grounding
- LLM2CLIP: Powerful Language Model Unlocks Richer Cross-Modality Representation
- AAAI Conference Paper Awards and Recognition - AAAI
- LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation

