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Wenjie Zhu
My name is Wenjie Zhu, and I am a fourth-year Ph.D. candidate in the VC Lab at The Hong Kong Polytechnic University. I am fortunate to be advised by IEEE Fellow Prof. Lei Zhang and IEEE Fellow Prof. Wenjun Zeng.
Before starting my Ph.D., I received my M.S. degree from New York University and my B.S. degree from Central South University.
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Research
I'm interested in computer vision, machine learning, trustworthy AI, AI in Health. Some papers are highlighted.
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Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs
Wenjie Zhu,
Yabin Zhang,
Liang Xu,
Xin Jin,
Wenjun Zeng,
Lei Zhang,
ECCV, 2026
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arXiv
we propose a novel framework named Dual Distribution Estimation (DDE), shifting the zero-shot NTTA paradigm from instance-level learning to training-free Gaussian distribution modeling.
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ANTS: Adaptive Negative Textual Space Shaping for OOD Detection via Test-Time MLLM Understanding and Reasoning
Wenjie Zhu
Yabin Zhang,
Xin Jin,
Wenjun Zeng,
Lei Zhang,
CVPR, 2026   (Oral)
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arXiv
we propose the ANTS approach by leveraging the understanding and reasoning capabilities of MLLMs.
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Knowledge Regularized Negative Feature Tuning of Vision-Language Models
Wenjie Zhu
Yabin Zhang,
Xin Jin,
Wenjun Zeng,
Lei Zhang,
ACMMM, 2025   (Oral)
project page
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arXiv
we propose a novel framework named Knowledge Reglarized Negative Feature Tuning (KR-NFT).
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MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models
Wenjie Zhu
Yabin Zhang,
Xin Jin,
Wenjun Zeng,
Lei Zhang,
arXiv, 2026
arXiv
MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models
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lapt: label-driven automated prompt tuning for ood detection with vision-language models
Yabin Zhang,
Wenjie Zhu,
Chenhang He,
Lei Zhang,
ECCV, 2024
bibtex
we introduce Label-driven Automated Prompt Tuning (LAPT), a novel approach to OOD detection that reduces the need for manual prompt engineering.
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Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
Yabin Zhang,
Wenjie Zhu,
Hui Tang,
Zhiyuan Ma,
Chenhang He,
Lei Zhang,
CVPR, 2024
we introduce a versatile adaptation approach that can effectively work under all three settings.
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