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Zhifei Li

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8 papers

1 author row

IS Journal 2026 Journal Article

High-Speed Dorsal Hand Vein Recognition Using Siamese Lightweight Neural Network

Yinfei Zheng
Zeyi Luo
Qiongwen Zhang
Zhifei Li
Qianguan Fu
Huilong Duan
Gaokai Liu
Yonghua Chu

Dorsal hand vein recognition possesses unique advantages, such as liveness detection and high stability in identity recognition. However, high computational demands and the need to retrain the model as the number of registered users increases, make it challenging to fully apply current deep learning research results to most edge devices. To address this limitation, MSGNet (modified Siamese GhostNet) in this article utilizes Siamese GhostNet backbones to reduce computational demands and enhance adaptability to dynamic datasets. Additionally, optimizations, such as the introduction of a multiscale convolution module, improve accuracy and robustness. A dataset comprising 118 subjects was constructed for this study. Experimental results showed that the model achieved an average matching time of 25. 61 ms, a recognition rate of 98. 82%, and an equal error rate of 1. 60%. These results outperform existing lightweight dorsal hand vein recognition algorithms, establishing MSGNet as a state-of-the-art solution for practical deployment.

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AAAI Conference 2026 Conference Paper

KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing

Zhifei Li
Lifan Chen
Jiali Yi
Xiaoju Hou
Yue Zhao
Wenxin Huang
Miao Zhang
Kui Xiao

Knowledge Tracing (KT) aims to dynamically model a student’s mastery of knowledge concepts based on their historical learning interactions. Most current methods rely on single-point estimates, which cannot distinguish true ability from outburst or carelessness, creating ambiguity in judging mastery. To address this issue, we propose a Knowledge Mastery-State Disambiguation for Knowledge Tracing model (KeenKT), which represents a student’s knowledge state at each interaction using a Normal-Inverse-Gaussian (NIG) distribution, thereby capturing the fluctuations in student learning behaviors. Furthermore, we design an NIG-distance-based attention mechanism to model the dynamic evolution of the knowledge state. In addition, we introduce a diffusion-based denoising reconstruction loss and a distributional contrastive learning loss to enhance the model’s robustness. Extensive experiments on six public datasets demonstrate that KeenKT outperforms state-of-the-art KT models in terms of prediction accuracy and sensitivity to behavioral fluctuations. The proposed method yields the maximum AUC improvement of 5.85% and the maximum ACC improvement of 6.89%.

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AAAI Conference 2026 Conference Paper

MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answering

Zhifei Li
Yiran Wang
Chenyi Xiong
Yujing Xia
Xiaoju Hou
Yue Zhao
Miao Zhang
Kui Xiao

Visual Question Answering (VQA) requires models to reason over multimodal information, combining visual and textual data. With the development of continual learning, significant progress has been made in retaining knowledge and adapting to new information in the VQA domain. However, current methods often struggle with balancing knowledge retention, adaptation, and robust feature representation. To address these challenges, we propose a novel framework with adaptive memory allocation and global noise filtering called MacVQA for visual question answering. MacVQA fuses visual and question information while filtering noise to ensure robust representations, and employs prototype-based memory allocation to optimize feature quality and memory usage. These designs enable MacVQA to balance knowledge acquisition, retention, and compositional generalization in continual VQA learning. Experiments on ten continual VQA tasks show that MacVQA outperforms existing baselines, achieving 43.38% average accuracy and 2.32% average forgetting on standard tasks, and 42.53% average accuracy and 3.60% average forgetting on novel composition tasks.

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AAAI Conference 2026 Conference Paper

MyGram: Modality-aware Graph Transformer with Global Distribution for Multi-modal Entity Alignment

Zhifei Li
Ziyue Qin
Xiangyu Luo
Xiaoju Hou
Yue Zhao
Miao Zhang
Zhifang Huang
Kui Xiao

Multi-modal entity alignment aims to identify equivalent entities between two multi-modal Knowledge graphs by integrating multi-modal data, such as images and text, to enrich the semantic representations of entities. However, existing methods may overlook the structural contextual information within each modality, making them vulnerable to interference from shallow features. To address these challenges, we propose MyGram, a \textbf{m}odalit\textbf{y}-aware \textbf{gra}ph transformer with global distribution for \textbf{m}ulti-modal entity alignment. Specifically, we develop a modality diffusion learning module to capture deep structural contextual information within modalities and enable fine-grained multi-modal fusion. In addition, we introduce a Gram Loss that acts as a regularization constraint by minimizing the volume of a 4-dimensional parallelotope formed by multi-modal features, thereby achieving global distribution consistency across modalities. We conduct experiments on five public datasets. Results show that MyGram outperforms baseline models, achieving a maximum improvement of 4.8\% in Hits@1 on FBDB15K, 9.9\% on FBYG15K, and 4.3\% on DBP15K.

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IJCAI Conference 2025 Conference Paper

A Dual Stream Visual Tokenizer for LLM Image Generation

Yongqian Li
Yong Luo
Xiantao Cai
Zheng He
Zhennan Meng
Nidong Wang
Yunlin Chen
Zhifei Li

We proposes a novel visual tokenizer by combining high-level semantic tokens and low-level pixel tokens to represent images, aiming to address the challenges of image-to-sequence conversion for Large Language Models (LLMs). Existing visual tokenizers, such as VQ-VAE and diffusion-based models, either struggle with token explosion as image resolution increases or fail to capture detailed structural information. Our method introduces a dual-token system: high-level semantic tokens capture the main content of the image, while low-level pixel tokens preserve structural details. By integrating these tokens in a hybrid architecture, we leverage a VQ-VAE branch to generate low-resolution guidance and a diffusion process to reconstruct high-resolution images with both semantic coherence and structural accuracy. This approach significantly reduces the number of required tokens and enhances image reconstruction quality, offering an efficient solution for tasks like image generation and understanding based on LLMs.

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AAAI Conference 2025 Conference Paper

APKGC: Noise-enhanced Multi-Modal Knowledge Graph Completion with Attention Penalty

Yue Jian
Xiangyu Luo
Zhifei Li
Miao Zhang
Yan Zhang
Kui Xiao
Xiaoju Hou

Multimodal knowledge graphs (MMKG) store structured world knowledge enriched with multimodal descriptive information. However, MMKG often faces the challenge of incompleteness. The primary objective of multimodal knowledge graph completion (MMKGC) is to predict missing entities within MMKG. Current MMKGC methods struggle with addressing the issue of over-trust attention and how to enhance the robustness of the model. To overcome these problems, we introduce APKGC, a noise-enhanced multimodal method for knowledge graph completion with attention penalty. APKGC effectively adjusts the attention scores in the language model and alleviates over-trust attention through a specifically designed attention penalty module. Additionally, an adaptive noise sampling module is proposed to supplement the entity's multimodal information, thereby enhancing the model's robustness. Experimental evaluation demonstrates that APKGC excels in overcoming these challenges. Compared to the existing state-of-the-art MMKGC model, APKGC improves Hit@1 by 3.3% on the DB15K dataset and by 3.4% on the MKG-W dataset.

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IJCAI Conference 2025 Conference Paper

DGCPL: Dual Graph Distillation for Concept Prerequisite Relation Learning

Miao Zhang
Jiawei Wang
Jinying Han
Kui Xiao
Zhifei Li
Yan Zhang
Hao Chen
Shihui Wang

Concept prerequisite relations determine the learning order of knowledge concepts in one domain, which has an important impact on teachers' course design and students' personalized learning. Current research usually predicts concept prerequisite relations from the perspective of knowledge, and rarely pays attention to the role of learners' learning behavior. We propose a Dual Graph Distillation Method for Concept Prerequisite Relation Learning (DGCPL). Specifically, DGCPL constructs a dual graph structure from both the knowledge and learning behavior perspectives, and captures the high-order knowledge features and learning behavior features through the concept-resource hypergraph and the learning behavior graph respectively. In addition, we introduce a gated knowledge distillation to fuse the structural information of concept nodes in the two graphs, so as to obtain a more comprehensive concept embedding representation and achieve accurate prediction of prerequisite relations. On three public benchmark datasets, we compare DGCPL with eight graph-based baseline methods and five traditional classification baseline methods. The experimental results show that DGCPL achieves state-of-the-art performance in learning concept prerequisite relations. Our code is available at https: //github. com/wisejw/DGCPL.

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AAAI Conference 2025 Conference Paper

Learning Concept Prerequisite Relation via Global Knowledge Relation Optimization

Miao Zhang
Jiawei Wang
Kui Xiao
Shihui Wang
Yan Zhang
Hao Chen
Zhifei Li

Learning concept prerequisite relations helps better master and build a logically coherent knowledge structure. Many studies use graph neural networks to create heterogeneous knowledge networks that enhance concept representations. However, different types of relations in these networks can influence each other. Existing research often focuses solely on concept relations, neglecting other types of knowledge connections. To address this issue, this paper proposes a novel concept prerequisite relation learning model, named the Global Knowledge Relation Optimization Model(GKROM). Specifically, we capture the impact of different knowledge relation types on document and concept semantic representations separately, integrating the document and concept semantic representations. Then, we introduce multi-objective learning to optimize the knowledge relation network from a global perspective. Through the above optimization, GKROM learns richer semantic representations for concepts and documents, improving the accuracy of concept prerequisite relation learning. Extensive experiments on public datasets demonstrate the effectiveness of our GKROM, achieving state-of-the-art performance in concept prerequisite relation learning.

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