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Baoyao Yang

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7 papers

2 author rows

JBHI Journal 2026 Journal Article

CAM-Interacted Vision GNN for Multi-Label Medical Images

Jingchao Wang
Baoyao Yang
Siqi Liu
Xiaoqi Zheng
Wenbin Yao
Junxiang Chen

Vision Graph Neural Network (ViG) is designed to recognize different objects through graph-level processing. However, ViG constructs graphs with appearance-level neighbors and neglects the category semantic. The oversight results in the unintentional connection of patches that belong to different objects, thus affecting the distinctiveness of categories in multi-label medical image learning. Since the pixel-level annotations for images are not easily available, category-aware graphs can not be directly built. To solve this problem, we consider localizing category-specific regions using Class Activation Maps (CAMs), an effective way to highlight regions belonging to each category without requiring manual annotations. Specifically, we propose a CAM-interacted Vision GNN (CiV-GNN), in which category-aware graphs are formed to perform intra-category graph processing. CIV-GNN includes a Class-activated Patch Division (CAPD) module, which introduces CAMs as guidance for category-aware graph building. Furthermore, we develop a Multi-graph Interactive Processing (MIP) module to model the relations between category-aware graphs, promoting inter-category interaction learning. Experimental results show that CiV-GNN performs well in surgical tool localization and multi-label medical image classification. Specifically, for m2cai16-localization, CiV-GNN exhibits a 1. 43% and 7. 02% improvement in mAP50 and mAP50-95, respectively, compared to YOLOv8.

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ECAI Conference 2025 Conference Paper

FairFed++: Closing the Fairness Gap in Federated Learning Through Self-Evolving Clustered Optimization

Zhixiang Fang
Baoyao Yang
Weide Zhan
Yanchao Tang
Yiqun Zhang

Performance fairness in federated learning (FL) aims to ensure that the server treats all clients equitably, thereby encouraging the participation of low-performance clients and enhancing the generalization capabilities of the global model. However, due to client heterogeneity, conflicts may arise among the gradients of different clients during FL, suppressing performance fairness. Although current FL algorithms can achieve convergence, such conflicts lead to performance unfairness when reaching the optimal solution, thereby significantly restricting the generalization ability of the global model learned through FL. To address this issue, strategies such as reweighting and data augmentation have been proposed. However, these approaches often result in performance degradation for certain clients while striving for fairness. Recent studies have highlighted the potential of cluster federated learning (CFL) in achieving performance fairness. Nevertheless, the heavy reliance on a pre-specified number of clusters not only limits its adaptability but also increases the complexity of FL. Inspired by the principle of species evolution, where cells divide under specific internal conditions, we propose a novel FL method, namely FairFed++. Specifically, FairFed++ performs self-evolving clustered optimization, explicitly releasing the reliance on prior knowledge of clustering. By utilizing the accuracy variance within clusters as the splitting criterion, FairFed++ automatically determines the optimal clustering strategy in each round of FL communication until convergence. This approach dynamically adjusts the number of clusters during training without requiring manual intervention, thus improving both adaptability and practicability. Experiments conducted on six datasets demonstrate that FairFed++ achieves superior performance fairness while preserving the generalization ability of the global model.

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ECAI Conference 2025 Conference Paper

Unlocking the Potential of mLLMs: Enhancing Video-Text Retrieval Through Caption Supplementation and Conical Embedding Optimization

Baoyao Yang
Junxiang Chen
Wenbin Yao

The burgeoning field of video-text retrieval has witnessed significant advancements with the advent of deep learning. However, understanding and matching textual descriptions and video data remains a formidable challenge due to the large information gap across textual and video modalities. As observed, the caption of a video is commonly under-described, lacking expressions of minor characters or local details. Some recent advances have attempted to leverage multimodal Large Language Model (mLLM) to bridge the comprehension gap. However, mLLMs’ potential in enhancing video-text retrieval (VTR) is understudied. This paper aims to fill this research vacancy, analyzing the practical significance and model preferences for utilizing mLLMs in VTR enhancement, as well as investigating the effective integration of mLLM-derived information into the retrieval learning. Based on our analytical insights, we innovatively propose treating mLLM as caption supplements rather than substitutes to bridge the expression gap across modalities. To achieve better cross-modal alignment, we systematically generate diverse variations of videos to construct an elastic visual space. By treating mLLM-supplemented captions as out-of-space points, cross-modal representation learning is accomplished through the optimization of a conical-like representation space. Our model achieves state-of-the-art results on various benchmarks, including MSR-VTT, MSVD, and DiDeMo, and analytical experiments suggest appropriate prompt proposals and indicate our method’s robustness to different mLLMs.

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JBHI Journal 2024 Journal Article

DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series

Jianxuan Huang
Baoyao Yang
Kejing Yin
Jingwen Xu

The real-world Electronic Health Records (EHRs) present irregularities due to changes in the patient's health status, resulting in various time intervals between observations and different physiological variables examined at each observation point. There have been recent applications of Transformer-based models in the field of irregular time series. However, the full attention mechanism in Transformer overly focuses on distant information, ignoring the short-term correlations of the condition. Thereby, the model is not able to capture localized changes or short-term fluctuations in patients' conditions. Therefore, we propose a novel end-to-end Deformable Neighborhood Attention Transformer (DNA-T) for irregular medical time series. The DNA-T captures local features by dynamically adjusting the receptive field of attention and aggregating relevant deformable neighborhoods in irregular time series. Specifically, we design a Deformable Neighborhood Attention (DNA) module that enables the network to attend to relevant neighborhoods by drifting the receiving field of neighborhood attention. The DNA enhances the model's sensitivity to local information and representation of local features, thereby capturing the correlation of localized changes in patients' conditions. We conduct extensive experiments to validate the effectiveness of DNA-T, outperforming existing state-of-the-art methods in predicting the mortality risk of patients. Moreover, we visualize an example to validate the effectiveness of the proposed DNA.

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AAAI Conference 2020 Conference Paper

DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

Qingxiong Tan
Mang Ye
Baoyao Yang
Siqi Liu
Andy Jinhua Ma
Terry Cheuk-Fung Yip
Grace Lai-Hung Wong
PongChi Yuen

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a timeaware structure to directly adjust the inﬂuence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can signiﬁcantly outperform state-of-the-art methods and provide meaningful clinical interpretation.

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AAAI Conference 2019 Conference Paper

Cross-Domain Visual Representations via Unsupervised Graph Alignment

Baoyao Yang
Pong C. Yuen

In unsupervised domain adaptation, distributions of visual representations are mismatched across domains, which leads to the performance drop of a source model in the target domain. Therefore, distribution alignment methods have been proposed to explore cross-domain visual representations. However, most alignment methods have not considered the difference in distribution structures across domains, and the adaptation would subject to the insufficient aligned cross-domain representations. To avoid the misclassification/misidentification due to the difference in distribution structures, this paper proposes a novel unsupervised graph alignment method that aligns both data representations and distribution structures across the source and target domains. An adversarial network is developed for unsupervised graph alignment, which maps both source and target data to a feature space where data are distributed with unified structure criteria. Experimental results show that the graph-aligned visual representations achieve good performance on both crossdataset recognition and cross-modal re-identification.

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AAAI Conference 2018 Conference Paper

Domain-Shared Group-Sparse Dictionary Learning for Unsupervised Domain Adaptation

Baoyao Yang
Andy Ma
Pong Yuen

Unsupervised domain adaptation has been proved to be a promising approach to solve the problem of dataset bias. To employ source labels in the target domain, it is required to align the joint distributions of source and target data. To do this, the key research problem is to align conditional distributions across domains without target labels. In this paper, we propose a new criterion of domain-shared groupsparsity that is an equivalent condition for conditional distribution alignment. To solve the problem in joint distribution alignment, a domain-shared group-sparse dictionary learning method is developed towards joint alignment of conditional and marginal distributions. A classiﬁer for target domain is trained using the domain-shared group-sparse coef- ﬁcients and the target-speciﬁc information from the target data. Experimental results on cross-domain face and object recognition show that the proposed method outperforms eight state-of-the-art unsupervised domain adaptation algorithms.

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