Master StudentI am Jian Lang, currently a master candidate in Software Engineering at the University of Electronic Science and Technology of China (UESTC), under the supervision of Prof Fan Zhou. I received my Bachelor of Engineering degree from Fuzhou University.
My research interests focus on multimodal learning, specifically addressing learning problems under missing or incomplete data modalities. I also conduct in-depth analysis in multimodal social media data. Recently, my research extends into multimodal large language models (MLLMs) and open-domain question-answering. I have published several academic papers in top conferences (KDD,WWW,AAAI,SIGIR) in related fields.
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University of Electronic Science and Technology of ChinaMaster Student in Software EngineeringSep. 2023 - present
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Fuzhou UniversityBachlor's Degree in AutomationSep. 2019 - Jul. 2023
Experience
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Ruijie NetworksSoftware Development InternMar. 2022 - Jul. 2022
Honors & Awards
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National Scholarship2024
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Master's Student Academic Scholarship (1st Division)2024
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Artificial Intelligence Algorithm Challenge Runner-up (2nd), hosted by People's Daily Online2023
News
Selected Publications (view all )
REDEEMing Modality Information Loss: Retrieval-Guided Conditional Generation for Severely Modality Missing Learning
Jian Lang, Rongpei Hong, Zhangtao Cheng, Yong Wang, Ting Zhong, Fan Zhou
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025 CCF-A Full Paper
We propose REDEEM, the extension work of our RAGPT accetped to AAAI 2025, a novel framework that pioneers a retrieval-guided conditional generation paradigm for enhancing the robustness of pre-trained Multimodal Transformer.
REDEEMing Modality Information Loss: Retrieval-Guided Conditional Generation for Severely Modality Missing Learning
Jian Lang, Rongpei Hong, Zhangtao Cheng, Yong Wang, Ting Zhong, Fan Zhou
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025 CCF-A Full Paper
We propose REDEEM, the extension work of our RAGPT accetped to AAAI 2025, a novel framework that pioneers a retrieval-guided conditional generation paradigm for enhancing the robustness of pre-trained Multimodal Transformer.
Biting Off More Than You Can Detect: Retrieval-Augmented Multimodal Experts for Short Video Hate Detection
Jian Lang, Rongpei Hong, Jin Xu, Xovee Xu, Yili Li, Fan Zhou
The Web Conference (WWW) 2025 CCF-A Full Paper
We introduce MoRE (Mixture of Retrieval-augmented multimodal Experts), a novel framework designed to enhance short video hate detection.
Biting Off More Than You Can Detect: Retrieval-Augmented Multimodal Experts for Short Video Hate Detection
Jian Lang, Rongpei Hong, Jin Xu, Xovee Xu, Yili Li, Fan Zhou
The Web Conference (WWW) 2025 CCF-A Full Paper
We introduce MoRE (Mixture of Retrieval-augmented multimodal Experts), a novel framework designed to enhance short video hate detection.
Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning
Jian Lang*, Zhangtao Cheng*, Jin Xu, Xovee Xu, Yili Li, Fan Zhou (* equal contribution)
The Association for the Advancement of Artificial Intelligence (AAAI) 2025 CCF-A Full Paper
We propose RAGPT, a novel Retrieval-AuGmented dynamic Prompt Tuning framework for enhancing the robustness of pre-trained Multimodal Transformer under modality missing conditions.
Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning
Jian Lang*, Zhangtao Cheng*, Jin Xu, Xovee Xu, Yili Li, Fan Zhou (* equal contribution)
The Association for the Advancement of Artificial Intelligence (AAAI) 2025 CCF-A Full Paper
We propose RAGPT, a novel Retrieval-AuGmented dynamic Prompt Tuning framework for enhancing the robustness of pre-trained Multimodal Transformer under modality missing conditions.
Seeing the Unseen in Micro-Video Popularity Prediction: Self-Correlation Retrieval for Missing Modality Generation
Zhangtao Cheng*, Jian Lang*, Ting Zhong, Fan Zhou (* equal contribution)
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025 CCF-A Full Paper
We propose SCRAG, a novel Self-Correlation Retrieval-Augmented Generative framework designed to enhance missing-modality robustness in micro-video popularity prediction.
Seeing the Unseen in Micro-Video Popularity Prediction: Self-Correlation Retrieval for Missing Modality Generation
Zhangtao Cheng*, Jian Lang*, Ting Zhong, Fan Zhou (* equal contribution)
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025 CCF-A Full Paper
We propose SCRAG, a novel Self-Correlation Retrieval-Augmented Generative framework designed to enhance missing-modality robustness in micro-video popularity prediction.
Following Clues, Approaching the Truth: Explainable Micro-Video Rumor Detection via Chain-of-Thought Reasoning
Rongpei Hong, Jian Lang, Jin Xu, Zhangtao Cheng, Ting Zhong, Fan Zhou
The Web Conference (WWW) 2025 CCF-A Full Paper
In this work, we introduce ExMRD, a novel Explainable Micro-video Rumor Detection (MVRD) framework designed to generate detailed and coherent explanations for enhancing MVRD.
Following Clues, Approaching the Truth: Explainable Micro-Video Rumor Detection via Chain-of-Thought Reasoning
Rongpei Hong, Jian Lang, Jin Xu, Zhangtao Cheng, Ting Zhong, Fan Zhou
The Web Conference (WWW) 2025 CCF-A Full Paper
In this work, we introduce ExMRD, a novel Explainable Micro-video Rumor Detection (MVRD) framework designed to generate detailed and coherent explanations for enhancing MVRD.
REAL: Retrieval-Augmented Prototype Alignment for Improved Fake News Video Detection
Yili Li, Jian Lang, Rongpei Hong, Qing Chen, Zhangtao Cheng, Jia Chen, Ting Zhong, Fan Zhou
IEEE International Conference on Multimedia & Expo (ICME) 2025 CCF-B Full Paper
We propose a novel model-agnostic framework REAL that generates manipulation-aware representations to enhance existing methods in detecting fake news videos.
REAL: Retrieval-Augmented Prototype Alignment for Improved Fake News Video Detection
Yili Li, Jian Lang, Rongpei Hong, Qing Chen, Zhangtao Cheng, Jia Chen, Ting Zhong, Fan Zhou
IEEE International Conference on Multimedia & Expo (ICME) 2025 CCF-B Full Paper
We propose a novel model-agnostic framework REAL that generates manipulation-aware representations to enhance existing methods in detecting fake news videos.
Predicting Micro-video Popularity via Multi-modal Retrieval Augmentation
Ting Zhong, Jian Lang, Yifan Zhang, Zhangtao Cheng, Kunpeng Zhang, Fan Zhou
Special Interest Group on Information Retrieval (SIGIR) 2025 CCF-A Short Paper
We present MMRA, a multi-modal retrieval-augmented popularity prediction model that enhances prediction accuracy using relevant retrieved information.
Predicting Micro-video Popularity via Multi-modal Retrieval Augmentation
Ting Zhong, Jian Lang, Yifan Zhang, Zhangtao Cheng, Kunpeng Zhang, Fan Zhou
Special Interest Group on Information Retrieval (SIGIR) 2025 CCF-A Short Paper
We present MMRA, a multi-modal retrieval-augmented popularity prediction model that enhances prediction accuracy using relevant retrieved information.
Generative Thinking, Corrective Action: User-Friendly Composed Image Retrieval via Automatic Multi-Agent Collaboration
Zhangtao Cheng, Yuhao Ma, Jian Lang, Rongpei Hong, Kunpeng Zhang, Yong Wang, Ting Zhong, Fan Zhou
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025 CCF-A Full Paper
We propose a novel framework -- Automatic Multi-Agent Collaboration for Zero-Shot Composed Image Retrieval (AutoCIR).
Generative Thinking, Corrective Action: User-Friendly Composed Image Retrieval via Automatic Multi-Agent Collaboration
Zhangtao Cheng, Yuhao Ma, Jian Lang, Rongpei Hong, Kunpeng Zhang, Yong Wang, Ting Zhong, Fan Zhou
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025 CCF-A Full Paper
We propose a novel framework -- Automatic Multi-Agent Collaboration for Zero-Shot Composed Image Retrieval (AutoCIR).
In-context Prompt-augmented Micro-video Popularity Prediction
Zhangtao Cheng, Jiao Li, Jian Lang, Ting Zhong, Fan Zhou
The Association for the Advancement of Artificial Intelligence (AAAI) 2025 CCF-A Full Paper
Inspired by prompt learning, we propose ICPF, a novel In-Context Prompt-augmented Framework to enhance popularity prediction.
In-context Prompt-augmented Micro-video Popularity Prediction
Zhangtao Cheng, Jiao Li, Jian Lang, Ting Zhong, Fan Zhou
The Association for the Advancement of Artificial Intelligence (AAAI) 2025 CCF-A Full Paper
Inspired by prompt learning, we propose ICPF, a novel In-Context Prompt-augmented Framework to enhance popularity prediction.