Author name cluster

Mengze Li

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Active Multi-source Domain Adaptation for Multimodal Fake News Detection

Yanping Chen
Weijie Shi
Mengze Li
Yue Cui
Jiaming Li
Ruiyuan Zhang
Hao Chen
Hanghui Guo

Multimodal fake news detection plays a crucial role in combating online misinformation. The inherent domain diversity of news in the real world has driven the development of cross-domain detection methods. However, these detection methods either suffer from significant performance degradation due to semantic and deception pattern shifts between the training (source) and test (target) domains or heavily rely on annotated labels. To address the problems, we propose ADOSE, an active multi-source domain adaptation framework for multimodal fake news detection which actively annotates a small subset of target samples to improve detection performance. Specifically, for domain shifts, we design a multi-expert classifier network based on refined features to comprehensively capture and adapt to the semantic space and deception patterns of news across different domains. To maximize adaptation performance with limited annotation cost, we propose a least-disagree uncertainty selector equipped with a diversity calculator for selecting the most informative samples. The selector leverages the uncertainty of inconsistent predictions before and after perturbations by multiple classifiers as an indicator of unfamiliar samples. It further incorporates diversity scores derived from multi-view features to ensure the chosen samples achieve maximal coverage of target domain features. The extensive experiments on multiple datasets show that ADOSE outperforms existing domain adaptation methods by 2.45% ~ 9.1%, indicating the superiority of our model.

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IJCAI Conference 2025 Conference Paper

Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer

Wenkang Han
Wang Lin
Liya Hu
Zhenlong Dai
Yiyun Zhou
Mengze Li
Zemin Liu
Chang Yao

Knowledge tracing (KT) aims to predict learners' future performance based on historical learning interactions. However, existing KT models predominantly focus on data from a single course, limiting their ability to capture a comprehensive understanding of learners' knowledge states. In this paper, we propose TransKT, a contrastive cross-course knowledge tracing method that leverages concept graph guided knowledge transfer to model the relationships between learning behaviors across different courses, thereby enhancing knowledge state estimation. Specifically, TransKT constructs a cross-course concept graph by leveraging zero-shot Large Language Model (LLM) prompts to establish implicit links between related concepts across different courses. This graph serves as the foundation for knowledge transfer, enabling the model to integrate and enhance the semantic features of learners' interactions across courses. Furthermore, TransKT includes an LLM-to-LM pipeline for incorporating summarized semantic features, which significantly improves the performance of Graph Convolutional Networks (GCNs) used for knowledge transfer. Additionally, TransKT employs a contrastive objective that aligns single-course and cross-course knowledge states, thereby refining the model's ability to provide a more robust and accurate representation of learners' overall knowledge states. Our code and datasets are available at https: //github. com/DQYZHWK/TransKT/.

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IJCAI Conference 2025 Conference Paper

ExpTalk: Diverse Emotional Expression via Adaptive Disentanglement and Refined Alignment for Speech-Driven 3D Facial Animation

Zhan Qu
Shengyu Zhang
Mengze Li
Zhuo Chen
Chengfei Lv
Zhou Zhao
Fei Wu

Speech-driven 3D facial animation aims to create lifelike facial expressions that synchronize accurately with speech. Despite significant progress, many existing methods may focus on generating facial animation with a fixed emotional state, neglecting the diverse transformations of facial emotions under a given speech input. To solve this issue, we focus on exploring the refined alignment between speech representations and multiple domains in facial expression information. We aim to disentangle the spoken language and emotion facial priors from speech expressions, to guide the refinement of the facial vertices based on speech. To accomplish this objective, we propose ExpTalk, which first applies an Adaptive Disentanglement Variational Autoencoder (AD-VAE) to decouple facial expression aligned with spoken language and emotions of speech through contrastive learning. Then a Refined Alignment Diffusion (RAD) is employed to iteratively refine the decoupled facial expression priors through diffusion-based perturbations, producing facial animations that align with the emotional variations of the given speech. Extensive experiments prove the effectiveness of our ExpTalk by surpassing state-of-the-arts by a large margin.

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AAAI Conference 2025 Conference Paper

Few-Shot Incremental Learning via Foreground Aggregation and Knowledge Transfer for Audio-Visual Semantic Segmentation

Jingqiao Xiu
Mengze Li
Zongxin Yang
Wei Ji
Yifang Yin
Roger Zimmermann

Audio-Visual Semantic Segmentation (AVSS) has gained significant attention in the multi-modal domain, aiming to segment video objects that produce specific sounds in the corresponding audio. Despite notable progress, existing methods still struggle to handle new classes not included in the original training set. To this end, we introduce Few-Shot Incremental Learning (FSIL) to the AVSS task, which seeks to seamlessly integrate new classes with limited incremental samples while preserving the knowledge of old classes. Two challenges arise in this new setting: (1) To reduce labeling costs, old classes within the incremental samples are treated as background, similar to silent objects. Training the model directly with background annotations may worsen the loss of distinctive knowledge about old classes, such as their outlines and sounds. (2) Most existing models adopt early cross-modal fusion with a single-tower design, incorporating more characteristics into class representations, which impedes knowledge transfer between classes based on similarity. To address these issues, we propose a Few-shot Incremental learning framework via class-centric foregrouNd aggreGation and dual-tower knowlEdge tRansfer (FINGER) for the AVSS task, which comprises two targeted modules: (1) The class-centric foreground aggregation gathers class-specific features for each foreground class while disregarding background features. The background class is excluded during training and inferred from the foreground predictions. (2) The dual-tower knowledge transfer postpones cross-modal fusion to separately conduct knowledge transfer for each modality. Extensive experiments validate the effectiveness of the FINGER model, significantly surpassing state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

Semantic-guided Diverse Decoding for Large Language Model

Weijie Shi
Yue Cui
Yaguang Wu
Jingzhi Fang
Shibo Zhang
Mengze Li
Sirui Han
Jia Zhu

Diverse decoding of large language models is crucial for applications requiring multiple semantically distinct responses, yet existing methods primarily achieve lexical rather than semantic diversity. This limitation significantly constrains Best-of-N strategies, group-based reinforcement learning, and data synthesis. While temperature sampling and diverse beam search modify token distributions or apply n-gram penalties, they fail to ensure meaningful semantic differentiation. We introduce Semantic-guided Diverse Decoding (SemDiD), operating directly in embedding space that balances quality with diversity through three complementary mechanisms: orthogonal directional guidance, dynamic inter-group repulsion, and position-debiased probability assessment. SemDiD harmonizes these competing objectives using adaptive gain functions and constraint optimization, ensuring both quality thresholds and maximal semantic differentiation. Experiments show SemDiD consistently outperforms existing methods, improving Best-of-N coverage by 1. 4-5. 2% across diverse tasks and accelerating RLHF training convergence by 15% while increasing accuracy by up to 2. 1%.

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AAAI Conference 2024 Conference Paper

Panoptic Scene Graph Generation with Semantics-Prototype Learning

Li Li
Wei Ji
Yiming Wu
Mengze Li
You Qin
Lina Wei
Roger Zimmermann

Panoptic Scene Graph Generation (PSG) parses objects and predicts their relationships (predicate) to connect human language and visual scenes. However, different language preferences of annotators and semantic overlaps between predicates lead to biased predicate annotations in the dataset, i.e. different predicates for the same object pairs. Biased predicate annotations make PSG models struggle in constructing a clear decision plane among predicates, which greatly hinders the real application of PSG models. To address the intrinsic bias above, we propose a novel framework named ADTrans to adaptively transfer biased predicate annotations to informative and unified ones. To promise consistency and accuracy during the transfer process, we propose to observe the invariance degree of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities. Meanwhile, we continuously measure the distribution changes between each presentation and its prototype, and constantly screen potentially biased data. Finally, with the unbiased predicate-prototype representation embedding space, biased annotations are easily identified. Experiments show that ADTrans significantly improves the performance of benchmark models, achieving a new state-of-the-art performance, and shows great generalization and effectiveness on multiple datasets. Our code is released at https://github.com/lili0415/PSG-biased-annotation.

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AIIM Journal 2022 Journal Article

Attribute-aware interpretation learning for thyroid ultrasound diagnosis

Ming Kong
Qing Guo
Shuowen Zhou
Mengze Li
Kun Kuang
Zhengxing Huang
Fei Wu
Xiaohong Chen

Thyroid nodule diagnosis from ultrasound images is a critical computer-aided diagnosis task. Previous works tried to imitate the doctor's diagnosis logic by considering the key attributes to improve the diagnosis performance and explaining the conclusion. However, their clinical feasibilities are still ambiguous because of the ignorance of the correlation between attribute features and global characteristics, as well as the lack of clinical effectiveness evaluation of result interpretations. Following the common logic of ultrasonic investigation, we design a novel Attribute-Aware Interpretation Learning (AAIL) model, consisting of attribute properties discovery module and attribute-global feature fusion module. Adequate result interpretation ensures reliability and transparency of diagnostic conclusions, including the visualization of attribute features and the relationship between attributes and the global feature. Extensive experiments on a practical dataset demonstrate the model's effectiveness, and an innovative human-computer collaborative experiment demonstrates the auxiliary diagnostic ability of the interpretations that can benefit professional doctors.

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IROS Conference 2015 Conference Paper

Electric stimulation feedback for gait control of walking robot

Yasuhisa Hasegawa
Keisuke Nakayama
Kohei Ozawa
Mengze Li

This paper proposes a finger-mounted walk controller for a complete paraplegic patient wearing a powered exoskeleton. The wearable controller mounted on both hands of the patient compensates impaired efferent and afferent nerves of the patient through healthy index fingers. The user controls his hip joint angle through force sensors of the controller as if he controls a position of his swing leg voluntarily. Simultaneously, he perceives hip joint angles through an electric stimulation device of the controller. This paper introduces a walking robot which simulates the patient body with lofstrand crutches on his arms for preliminary experiments before a clinical trial. The finger-mounted walk controller is used to control a position of the robot's swing leg. A 5-meter walk experiment is conducted to evaluate feasibility of the controller. We confirmed that the user controlled a stride based only on the electric stimulation feedback and no visual feedback, and that the robot finished walking 5-meter in three minutes.

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