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 work on leveraging artificial intelligence techniques for in-depth analysis of social media data. 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 ChinaSchool of Information and Software Engineering
Master StudentSep. 2023 - present
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 )
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.
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.
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.
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.
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.