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Yanfeng Li

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7 papers

1 author row

EAAI Journal 2026 Journal Article

Dual-level information interactive learning model for text-image person Re-identification

Jia Sun
Yanfeng Li
Houjin Chen
Luyifu Chen
Minjun Wang

Text-image person re-identification (TI-ReID), which retrieves corresponding person images via textual descriptions, stands out as a prominent research area within the field of object tracking. The core challenge of TI-ReID lies in the significant discrepancies between the text and image modalities, making it difficult to effectively associate positive sample pairs. Existing studies only focus on the relationships between samples at the instance-level, and overly emphasizing the uniqueness of positive pairs, resulting in insufficient learnable content for the model. In this study, building upon the improvement of instance-level work, we introduce a class-level learning component and put forward a novel Dual-level Information Interactive Learning (DIIL) model. The aim is to jointly learn the inter-modal correlation relationships from both the class-level and the instance-level. Specifically, DIIL consists of two principal components: (1) a class-level teacher guidance (CTG) module that constructs two sample embedding banks at the class-level to provide more comprehensive guidance for instance samples. (2) an instance-level information blending (IIB) module that establishes the bidirectional correlation between text and image from the two perspectives of mask prediction and information blending, thus fully narrowing the gap between the features of the two modalities. We conduct sufficient experiments on three public datasets, and the experimental results demonstrate that the DIIL model achieves state-of-the-art results, especially in terms of the mean average precision (mAP).

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AAAI Conference 2026 Conference Paper

HiFi-Mesh: High-Fidelity Efficient 3D Mesh Generation via Compact Autoregressive Dependence

Yanfeng Li
Tao Tan
Qinquan Gao
Zhiwen Cao
Xiaohong Liu
Yue Sun

High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequences, which severely constrains the expressible structural details. We introduce the Latent Autoregressive Network (LANE), which incorporates compact autoregressive dependencies in the generation process, achieving a 6× improvement in maximum generatable sequence length compared to existing methods. To further accelerate inference, we propose the Adaptive Computation Graph Reconfiguration (AdaGraph) strategy, which effectively overcomes the efficiency bottleneck of traditional serial inference through spatiotemporal decoupling in the generation process. Experimental validation demonstrates that LANE achieves superior performance across generation speed, structural detail, and geometric consistency, providing an effective solution for high-quality 3D mesh generation.

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AAAI Conference 2025 Conference Paper

Dynamic Entity-Masked Graph Diffusion Model for Histopathology Image Representation Learning

Zhenfeng Zhuang
Min Cen
Yanfeng Li
Fangyu Zhou
Lequan Yu
Baptiste Magnier
Liansheng Wang

Significant disparities between the features of natural images and those inherent to histopathological images make it challenging to directly apply and transfer pre-trained models from natural images to histopathology tasks. Moreover, the frequent lack of annotations in histopathology patch images has driven researchers to explore self-supervised learning methods like mask reconstruction for learning representations from large amounts of unlabeled data. Crucially, previous mask-based efforts in self-supervised learning have often overlooked the spatial interactions among entities, which are essential for constructing accurate representations of pathological entities. To address these challenges, constructing graphs of entities is a promising approach. In addition, the diffusion reconstruction strategy has recently shown superior performance through its random intensity noise addition technique to enhance the robust learned representation. Therefore, we introduce H-MGDM, a novel self-supervised Histopathology image representation learning method through the Dynamic Entity-Masked Graph Diffusion Model. Specifically, we propose to use complementary subgraphs as latent diffusion conditions and self-supervised targets respectively during pre-training. We note that the graph can embed entities' topological relationships and enhance representation. Dynamic conditions and targets can improve pathological fine reconstruction. Our model has conducted pretraining experiments on three large histopathological datasets. The advanced predictive performance and interpretability of H-MGDM are clearly evaluated on comprehensive downstream tasks such as classification and survival analysis on six datasets.

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EAAI Journal 2025 Journal Article

Mitigating Batch Normalization bias for single domain generalizable person re-identification

Luyifu Chen
Yanfeng Li
Jia Sun
Houjin Chen
Minjun Wang
Wanru Peng

Domain generalizable person re-identification (ReID) poses a significant challenge in real scenarios, as it aims at transferring the knowledge learned from single- or multi-source domains to unseen target domains. Due to the deterministic values of feature statistics (mean and standard deviation) learned from source-domain data, Batch Normalization (BN) exhibits a severe bias towards source domain. This bias is particularly pronounced in single-source domain scenarios, often leading to catastrophic performance degradation. In this paper, a novel Debiasing Batch Normalization (DBN) approach is proposed to alleviate the source-domain bias caused by deterministic values. The DBN is composed of two novel components, a Dynamical Transformed Module (DTM) component and a Gallery to Query Test-time Adaptation (G2QTA) component. Specifically, DTM can flexibly generate the feature statistics relying on the input samples, which is beneficial to adapting to unseen domain and addressing the domain shift. Note that DTM is only embedded in high level stages to replace several BN modules. A novel G2QTA is designed to revise deterministic values, which are not modified by DTM, and adapt to the style of unseen target domain. Extensive experiments demonstrate that our DBN outperform most methods on the task ‘Market1501 → DukeMTMC-ReID’ and ‘DukeMTMC-ReID → Market1501’.

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EAAI Journal 2024 Journal Article

Unveiling the potential: Exploring the predictability of complex exchange rate trends

Yuntao Mao
Ziwei Chen
Siyuan Liu
Yanfeng Li

Forecasting exchange rates is challenging due to its diverse features and complex patterns. Inspired the theory of receptive fields, we proposed two models: the Transformer with Convolutional Neural Network (CNN-Transformer) and the Long Short Term Memory networks with Convolutional Neural Network (CNN-LSTM). These models leverage Convolutional Neural Network (CNN) modules to expand the learnable timestep of Long Short Term Memory networks (LSTM) and reduce the complexity of Transformer. Generally, CNN-LSTM demonstrates the highest predictive accuracy. Furthermore, there is a silver lining: the sentiment in past news is closely linked to future exchange rates. In order to introduce the news sentiment to improve the performance of models, we fine-tune a pre-trained model, Bidirectional Encoder Representations from Transformers with Whole Word Masking (BERT-WWM), to extract the sentiment information from news dataset. However, the effectiveness of introducing news sentiment is greatly dependent on the timesteps. Longer timesteps often increase the likelihood of success for this method. We believe this pattern relates to the timeliness of news and the delayed impact of news events. Also, it's important to note that the content of news text significantly influences the forecasting performance.

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JBHI Journal 2023 Journal Article

Coco-Attention for Tumor Segmentation in Weakly Paired Multimodal MRI Images

Pei Zhou
Yanfeng Li
Houjin Chen
Yahui Peng

Multimodal magnetic resonance imaging (MRI) contains complementary information in anatomical and functional images that help the accurate diagnosis and treatment evaluation of lung cancers. However, effectively exploiting the complementary information in chest MRI images remains challenging due to the lack of rigorous registration. In this paper, a novel method is proposed that can effectively exploit the complementary information in weakly paired images for accurate tumor segmentation, namely coco-attention mechanism. Coco-attention module consists of two parts: the multi-modal co-attention (MultiCo-attn) and the multi-level coordinate attention (MultiCord-attn). The former aims to obtain tumor-aware deep features for accurate tumor localization, and the latter aims to highlight tumor area for more precise segmentation. Specifically, the MultiCo-attn extracts complementary information from multimodal high-dimensional semantic features using a bidirectional algorithm to generate attention maps focused on tumor region, and then uses the attention maps to enhance the feature representations. The MultiCord-attn leverages multi-level feature information to highlight tumor regions by adjusting the weight of each point in the feature. We evaluate the proposed method on lung tumor segmentation with a clinical dataset of 90 chest MRI scans of non-small cell lung cancer (NSCLC). The results show that the proposed method is effective for tumor segmentation in weakly paired images and achieves significant improvement (p < 0. 005) over several commonly used multimodal segmentation methods. Furthermore, the ablation experiment results confirm the effectiveness and interpretability of the proposed coco-attention module.

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JBHI Journal 2022 Journal Article

Cross-Model Attention-Guided Tumor Segmentation for 3D Automated Breast Ultrasound (ABUS) Images

Yue Zhou
Houjin Chen
Yanfeng Li
Xuyang Cao
Shu Wang
Dinggang Shen

Tumor segmentation in 3D automated breast ultrasound (ABUS) plays an important role in breast disease diagnosis and surgical planning. However, automatic segmentation of tumors in 3D ABUS images is still challenging, due to the large tumor shape and size variations, and uncertain tumor locations among patients. In this paper, we develop a novel cross-model attention-guided tumor segmentation network with a hybrid loss for 3D ABUS images. Specifically, we incorporate the tumor location into a segmentation network by combining an improved 3D Mask R-CNN head into V-Net as an end-to-end architecture. Furthermore, we introduce a cross-model attention mechanism that is able to aggregate the segmentation probability map from the improved 3D Mask R-CNN to each feature extraction level in the V-Net. Then, we design a hybrid loss to balance the contribution of each part in the proposed cross-model segmentation network. We conduct extensive experiments on 170 3D ABUS from 107 patients. Experimental results show that our method outperforms other state-of-the-art methods, by achieving the Dice similarity coefficient (DSC) of 64. 57%, Jaccard coefficient (JC) of 53. 39%, recall (REC) of 64. 43%, precision (PRE) of 74. 51%, 95th Hausdorff distance (95HD) of 11. 91 mm, and average surface distance (ASD) of 4. 63 mm. Our code will be available online ( https://github.com/zhouyuegithub/CMVNet ).

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