Tianhe Ren

I am currently working at The International Digital Economy Academy (IDEA) as Computer Vision Engineer, advised by Prof. Lei Zhang. In 2021, I got my bachelor's degree from MAC Lab in Xiamen University advised by Associate Prof. Yiyi Zhou and Prof. Rongrong Ji.

I'm primarily interested in researching vision foundation models, object detection and segmentation, and multi-modal learning. I'm also passionate about open-source projects in AI community. The research work and open-source projects I'm involved in have garnered more than 30.0K stars on Github.

✨ My personal avatar is my beloved daughter named Toffee, she is a Minuet (also known as the Napoleon) cat ✨

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Toffee

News

2025

We have released the DINO-XSeeK model, aimed at more accurately detecting objects based more complex text descriptions.
One paper is accepted to TPAMI 2025.

2024

We have released DINO-X, the world's top-performing vision model for open-world detection and understanding.
Grounding DINO is selected as The 10th Most Influential Paper among the 2023 Computer Vision and Pattern Recognition ArXiv papers by PaperDigest.
Grounded SAM is selected as The 7th Most Influential Paper among the 2024 Computer Vision and Pattern Recognition ArXiv papers by PaperDigest.
Grounding DINO is selected as The 1st Most Influential Paper in ECCV 2024 by PaperDigest.
Grounded SAM 2 is released towards tracking anything in video.
One paper is accepted to NeurIPS 2024.
Grounding DINO 1.6 is released with stronger open-world detection performance.
One paper is accepted to TPAMI 2024.
Six papers are accepted to ECCV 2024.
We have released Grounding DINO 1.5, the most capable open-set detection model to date.

2023

Grounded SAM is accepted to ICCV 2023 Demo Track.
Four papers are accepted to CVPR/NeurIPS/ICCV 2023.

2022

One paper is accepted to NeurIPS 2022.

2021

One paper is accepted to ICCV 2021.

Selected Research

See full list at Google Scholar. (* indicates equal contribution, † indicates corresponding author).

	DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding IDEA Research Team Tech report, Nov, 2024. Paper / Code / Website / DINO-XSeeK (preview)
	Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang† European Conference on Computer Vision (ECCV), 2024. The 1st Most Influential Paper by PaperDigest. Paper / Code / Website / Video
	T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy Qing Jiang, Feng Li, Zhaoyang Zeng, Tianhe Ren, Shilong Liu, Lei Zhang† European Conference on Computer Vision (ECCV), 2024 Paper / Code / Website / Demo / Video
	Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection Tianhe Ren, Qing Jiang, Shilong Liu, Zhaoyang Zeng, Wenlong Liu, Han Gao, Hongjie Huang, Zhengyu Ma, Xiaoke Jiang, Yihao Chen, Yuda Xiong, Hao Zhang, Feng Li, Peijun Tang, Kent Yu, Lei Zhang† Tech report, May. 2024 Paper / Code / Website / Demo / Grounding DINO 1.6 Tech Blog
	Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, Lei Zhang† International Conference on Computer Vision (ICCV) Demo Track, 2023 The 7th Most Influential Paper in the 2024 Computer Vision and Pattern Recognition ArXiv papers by PaperDigest. Paper / Code / Video / Grounded SAM 2
	Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models Gen Luo, Yiyi Zhou, Tianhe Ren, Shengxin Chen, Xiaoshuai Sun, Rongrong Ji† Conference on Neural Information Processing Systems (NeurIPS), 2023 Paper / Code / Website
	Detection Transformer with Stable Matching Shilong Liu, Tianhe Ren, Jiayu Chen, Zhaoyang Zeng, Hao Zhang, Feng Li, Hongyang Li, Jun Huang, Hang Su, Jun Zhu, Lei Zhang† International Conference on Computer Vision (ICCV*), 2023 Paper / Code
	detrex: Benchmarking Detection Transformers Tianhe Ren, Shilong Liu, Feng Li, Hao Zhang, Ailing Zeng, Jie Yang, Xingyu Liao, Ding Jia, Hongyang Li, He Cao, Jianan Wang, Zhaoyang Zeng, Xianbiao Qi, Yuhui Yuan, Jianwei Yang, Lei Zhang† Tech report, May. 2023 Paper / Code / Website
	You Only Segment Once: Towards Real-Time Panoptic Segmentation Jie Hu, Linyan Huang, Tianhe Ren, Shengchuan Zhang, Rongrong Ji, Liujuan Cao†, Computer Vision and Pattern Recognition (CVPR), 2023 Paper / Code
	TRAR: Routing the Attention Spans in Transformers for Visual Question Answering Yiyi Zhou, Tianhe Ren, Chaoyang Zhu , Xiaoshuai Sun, Jianzhuang Liu, Xinghao Ding, Mingliang Xu, Rongrong Ji† International Conference on Computer Vision (ICCV), 2021 Paper / Code

Work Experiences

	International Digital Economy Academy (IDEA) Senior Computer Vision Engineer and Researcher 2022 - Present Advisor: Lei Zhang • Leading research and development of vision foundation models • Developed high-impact open-source projects
	OneFlow (SiliconFlow Now) Computer Vision Engineer 2021 - 2022 Advisor: Jinhui Yuan • Develop deep learning frameworks and computer vision models • Contribute to OneFlow's computer vision ecosystem
	MAC Lab, Xiamen University Research Assistant 2019 - 2021 Advisors: Yiyi Zhou and Rongrong Ji • Conducted research on multi-modal learning

Professional Services

Conference Reviewer

2025

• International Conference on Learning Representations (ICLR)
• Artificial Intelligence and Statistics (AISTATS)
• Computer Vision and Pattern Recognition (CVPR)
• International Conference on Machine Learning (ICML)
• International Conference on Computer Vision (ICCV)
• Conference on Neural Information Processing Systems (NeurIPS)

2024

• Computer Vision in the Wild (CVinW) Workshop at CVPR
• European Conference on Computer Vision (ECCV)
• Conference on Neural Information Processing Systems (NeurIPS)

Workshop Organizer

• Computer Vision in the Wild (CVinW) Workshop at CVPR 2025
• Computer Vision in the Wild (CVinW) Workshop at CVPR 2023

Amazing template by Jon Barron. Big thanks!