Tianren Ma  

马 天任  

Ph.D. Candidate


Learning and Machine Perception Lab (LAMP)
School of Electronic, Electrical and Communication Engineering
University of Chinese Academy of Sciences (UCAS)
Beijing, China, 100083.

博士研究生


机器学习与感知实验室 (LAMP)
电子电气与通信工程学院
中国科学院大学 (UCAS)
中国,北京,100083


Email: matianren18##mails.ucas.ac.cn
Github: https://github.com/martian422

My Biography

I am a Ph.D. candidate of LAMP at UCAS, advised by Prof. Qixiang Ye.

I got my B.E. degree in UCAS in 2022.

Currently, I'm exploring multimodal model's generation and reinforcement learning methods with discrete structures.

I'm also interested in photography, graphic design, and musicals.

个人简介

我是中国科学院大学LAMP实验室的博士研究生, 导师是叶齐祥教授

2022年,我在中国科学院大学获得工学学士学位。

目前,我正在研究基于离散变量的多模态生成和强化学习办法。

我对摄影,平面设计以及音乐剧都很感兴趣。

Major Publications

主要论文

Tianren Ma, Mu Zhang, Yibing Wang, Qixiang Ye
Consolidating Reinforcement Learning For Multimodal Discrete Diffusion Models
Accepted by ICLR 2026.
[Paper] [Code]
Tianren Ma, Xiaosong Zhang, Boyu Yang, Junlan Feng, Qixiang Ye
ReDDiT: Rehashing Noise for Discrete Visual Generation
Accepted by ICLR 2026.
[Paper] [Code]
Tianren Ma, Lingxi Xie, Yunjie Tian, Boyu Yang, Qixiang Ye
ClawMachine: Learning to Fetch Visual Tokens for Referential Comprehension
Accepted by ICLR 2025.
[Paper] [Code]
Tianren Ma, Mingxiang Liao, Xijin Zhang, Qixiang Ye
AceTone: Bridging Words and Colors for Conditional Image Grading
Accepted by CVPR 2026.
[Paper] [Code]
Mingxiang Liao*, Tianren Ma*, Xijin Zhang
Open World Image Aesthetic Assessment
Preprint.
[Paper] [Code]
Yunjie Tian*, Tianren Ma*, Lingxi Xie, Qixiang Ye
ChatterBox: Multimodal Referring and Grounding with Chain-of-Questions
Accepted by AAAI 2025.
[Paper] [Code]

Co-author Publications

其他论文

Mu Zhang, Tianren Ma, Yunfan Liu, Kun Hu, Qixiang Ye
RebRL: Reinforcing Discrete Visual Diffusion Models with Rebalanced Timestep Credits
Accepted by CVPR 2026.
[Paper] [Code]
Jihao Qiu*, Yuan Zhang*, Xi Tang*, Lingxi Xie, Tianren Ma, Pengyu Yan, David Doermann, Qixiang Ye, Yunjie Tian
Artemis: Towards Referential Understanding in Complex Videos
Accepted by NeurIPS 2024.
[Paper] [Code]

* indicates equal contribution.

* 表示作者贡献相同。