I am currently a Ph.D. candidate in Tianjin Key Laboratory of Visual Computing and Intelligent Perception (VCIP) and Media Computing Lab (MCLab) at the College of Computer Science, Nankai University, supervised by Prof. Ming-Ming Cheng and Prof. Qibin Hou. Prior to this, I completed seven years of undergraduate and master's studies at Dalian University of Technology (DUT).
My current research interests focus on multimodal large language models, video understanding, reinforcement learning for LVLMs, and open-vocabulary semantic segmentation.
I am dedicated to contributing to open-source projects, and my work can be found in HVision-NKU. Additionally, I maintain a list of Awesome Open-Vocabulary Semantic Segmentation resources.
If you're interested in my research or have any research-related questions, please feel free to contact me via email at yunhengli [at] mail.nankai.edu.cn or yunheng.li.21 [at] gmail.com.
📚 Publications
* Eauql contribution. # Corresponding author
Preprint
Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions
Yunheng Li, Hengrui Zhang, Meng-Hao Guo, Wenzhao Gao, Shaoyong Jia, Shaohui Jiao, Qibin Hou1#, Ming-Ming Cheng
We introduce ASID-Caption, a data-and-model suite for fine-grained audiovisual video understanding, including a large-scale attribute-structured dataset (ASID-1M), a quality-verification pipeline (ASID-Verify), and Omni–based captioning models (ASID-Captioner).
Align Before Segment: Understanding Visual Encoder Fine-tuning for Open Vocabulary Segmentation
Yunheng Li, Quansheng Zeng, Zhong-Yu Li, Enguang Wang, Qibin Hou#, Ming-Ming Cheng
FineCLIP is an align-before-segment framework that fine-tunes CLIP with dense image-text alignment, notably enhancing open-vocabulary segmentation performance.
Conference
TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMs
Yunheng Li, JingCheng, Shaoyong Jia, Hangyi Kuang, Shaohui Jiao, Qibin Hou#, Ming-Ming Cheng [Paper] [Code] [Huggingface] [字节跳动技术团队]
TempSamp-R1 leverages ground-truth annotations as off-policy supervision to provide temporally precise guidance, effectively compensating for the sparsity and misalignment in on-policy solutions in Video LLMs.
Unbiased Region-Language Alignment for Open-Vocabulary Dense Prediction
Yunheng Li, Yuxuan Li, Quansheng Zeng, Wenhai Wang, Qibin Hou#, Ming-Ming Cheng
[Paper] [Code] [Huggingface] [中译版]
DenseVLM is an unsupervised fine-tuning framework, which retrieves region-level semantics from a powerful vision-language model and decouples foreground and background features to achieve unbiased region-language alignment.
Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation
Yunheng Li, Zhong-Yu Li, Quansheng Zeng, Qibin Hou#, Ming-Ming Cheng
[Paper] [Code] [中译版] [集智书童] [Poster]
Cascade-CLIP aligns vision-language embeddings via cascaded manner, effectively leveraging CLIP’s multi-level visual features for better zero-shot segmentation.
Reducing the Label Bias for Timestamp Supervised Temporal Action Segmentation
Kaiyuan Liu*, Yunheng Li*, Shenglan Liu#, Chenwei Tan, Zihang Shao
D-TSTAS employs a masked timestamp prediction method to reduce dependency on timestamps and a center-oriented timestamp expansion technique to capture semantic-rich motion representations.
Journal
A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation
Yunheng Li, Zhong-Yu Li, Shanghua Gao, Qilong Wang, Qibin Hou#, Ming-Ming Cheng
Decoupled Spatio-Temporal (DeST) framework is the first to decouple spatio-temporal modeling for effective skeleton-based action segmentation.
Yunheng Li, Kai-Yuan Liu, Sheng-Lan Liu#, Lin Feng, Hong Qiao
IDT-GCN employs an Involving Distinction Graph Convolutional Network (ID-GC) to effectively capture both similar and differential dependencies among spatial joints through multiple adaptive topologies. Additionally, Temporal Segment Regression (TSR) is used to model action sequences.
📃 Others
Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment. IEEE ICCV, 2025.
Shicheng Zhang, Yunheng Li, et al. [Paper] [Code]
Spatial Focus Attention for Fine-grained Skeleton-based Action Tasks. IEEE SPL, 2022. Kaiyuan Liu, Yunheng Li, et al. [Paper]
Double Attention Network Based on Sparse Sampling. IEEE ICME, 2022. Zhuben Dong, Yunheng Li, et al. [Paper]
Eicient Two-Step Networks for Temporal Action Segmentation. Neurocomputing, 2021. Yunheng Li, Zhuben Dong, Kaiyuan Liu, et al. [Paper] [Code]
Temporal Segmentation of Fine-gained Semantic Action: A Motion-centered Figure Skating Dataset. AAAI, 2021. Shenglan Liu, Aibin Zhang*, Yunheng Li*, et al. [Paper] [Datasets]
🛠️ Orobot: Ball-Wheel Self-Balancing Robot
🎓 Educations
- 2023.09 - Present, Ph.D. Student in Computer Science and Technology, Nankai University, Tianjin, China.
- 2020.09 - 2023.06, M.S. in Computer Science and Technology, Dalian University of Technology, Dalian, China.
- 2016.09 - 2020.06, B.S. in Electrical Engineering and Automation, Dalian University of Technology, Dalian, China.
👥 Services
- Conference: CVPR; ICCV; NeurIPS; ICML; ICLR; ECCV; etc.
- Journal: IEEE TCSVT; Neurocomputing.