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publications
Accurate Segmentation of Optic Disc and Cup from Multiple Pseudo-labels by Noise-aware Learning
Published in IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2024
A noise-aware learning method for optic disc and cup segmentation from multiple pseudo-labels.
Recommended citation: Weng, Tengjin, Yang Shen, Zhidong Zhao, Zhiming Cheng, and Shuai Wang. (2024). "Accurate Segmentation of Optic Disc and Cup from Multiple Pseudo-labels by Noise-aware Learning." CSCWD.
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Enhancing point annotations with superpixel and confident learning guided for improving semi-supervised OCT fluid segmentation
Published in Biomedical Signal Processing and Control, 2024
A semi-supervised OCT fluid segmentation method using point annotations, superpixels, and confident learning.
Recommended citation: Weng, Tengjin, Yang Shen, Kai Jin, Yaqi Wang, Zhiming Cheng, Yunxiang Li, Gewen Zhang, and Shuai Wang. (2024). "Enhancing point annotations with superpixel and confident learning guided for improving semi-supervised OCT fluid segmentation." Biomedical Signal Processing and Control.
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LlamaSeg: Image Segmentation via Autoregressive Mask Generation
Published in Submitted to ICLR 2026, 2025
A visual autoregressive framework that unifies image segmentation tasks through natural language instructions.
Recommended citation: Deng, Jiru, Tengjin Weng, Tianyu Yang, Wenhan Luo, Zhiheng Li, and Wenhao Jiang. (2025). "LlamaSeg: Image Segmentation via Autoregressive Mask Generation." Submitted to ICLR 2026.
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VisNumBench: Evaluating Number Sense of Multimodal Large Language Models
Published in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025
A benchmark for evaluating visual number sense in multimodal large language models.
Recommended citation: Weng, Tengjin, Jingyi Wang, Wenhao Jiang, and Zhong Ming. (2025). "VisNumBench: Evaluating Number Sense of Multimodal Large Language Models." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 3830-3840.
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Multi-annotation agreement and prediction consistency networks: Improving semi-supervised segmentation of medical images with ambiguous boundaries
Published in Artificial Intelligence in Medicine, 2026
Co-first author. Semi-supervised medical image segmentation with ambiguous boundaries and multi-annotated data.
Recommended citation: Wang, Shuai*, Tengjin Weng*, Jingyi Wang, Kai Zhao, Yang Shen, Zhidong Zhao, Yixiu Liu, Pengfei Jiao, Zhiming Cheng, Yaoqi Sun, and Yaqi Wang. (2026). "Multi-annotation agreement and prediction consistency networks: Improving semi-supervised segmentation of medical images with ambiguous boundaries." Artificial Intelligence in Medicine, 171, 103289. *Equal contribution.
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GRPO-VPS: Enhancing Group Relative Policy Optimization with Verifiable Process Supervision for Effective Reasoning
Published in ICLR 2026 Workshop on LLM Reasoning, 2026
A verifiable process supervision method for improving GRPO credit assignment and reasoning efficiency.
Recommended citation: Wang, Jingyi, Lei Zhu, Tengjin Weng, Song-Li Wu, Haochen Tan, Jierun Chen, Chaofan Tao, Haoli Bai, Lu Hou, Lifeng Shang, and Xiao-Ping Zhang. (2026). "GRPO-VPS: Enhancing Group Relative Policy Optimization with Verifiable Process Supervision for Effective Reasoning." ICLR 2026 Workshop on LLM Reasoning.
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BEAP-Agent: Backtrackable Execution and Adaptive Planning for GUI Agents
Published in CoRR, 2026
A GUI agent framework for backtrackable execution and adaptive planning.
Recommended citation: Lu, Ziyu, Tengjin Weng, Yiying Yang, Yuhang Zhao, Xinxin Huang, and Wenhao Jiang. (2026). "BEAP-Agent: Backtrackable Execution and Adaptive Planning for GUI Agents." CoRR.
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OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models
Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Highlight, 2026
CVPR 2026 Highlight. A controllable benchmark for evaluating fine-grained visual discrepancy sensitivity in multimodal large language models.
Recommended citation: Weng, Tengjin, Wenhao Jiang, Jingyi Wang, Ming Li, Lin Ma, and Zhong Ming. (2026). "OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Highlight.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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