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Xingyuan Chen

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4 papers

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EAAI Journal 2026 Journal Article

Segmentation-enhanced Medical Visual Question Answering with mask-prompt alignment using contrastive learning and multitask object grounding

Qishen Chen
Huahu Xu
Wenxuan He
Xingyuan Chen
Minjie Bian
Honghao Gao

Medical Visual Question Answering (MedVQA) aims to provide clinical suggestions by analyzing medical images in response to textual queries. However, existing methods struggle to accurately identify anatomical structures and pathological abnormalities, leading to unreliable predictions. Many deep learning-based approaches also lack interpretability, making their diagnostic reasoning opaque. To address these challenges, this paper proposes Mask-Prompt Aligned Visual Question Answering (MPA-VQA), a two-stage framework that integrates segmentation information into the MedVQA process. First, a segmentation model is trained to detect key structures within medical images. To mitigate the issue of limited segmentation annotations, this paper introduces an improved CutMix-based data augmentation strategy. Second, segmentation masks are used to generate prompts, which are incorporated into the question-answering process for the first time to enhance interpretability. Third, to improve the alignment between image, mask, and prompt representations, this paper proposes a dual-granularity mask-prompt alignment (MPA) method. At the image level, MPA employs contrastive learning to encourage global consistency, while at the object level, it leverages multi-task object grounding to enhance localization accuracy. A mask-guided attention mechanism is also introduced to ensure the model focuses on clinically relevant image regions. Finally, the proposed MPA-VQA is validated on the SLAKE and MedVQA-GI datasets, demonstrating state-of-the-art performance. Notably, MPA-VQA improves location-related question accuracy by 6. 37% on MedVQA-GI. MPA-VQA is also a plug-and-play framework that can be seamlessly integrated into existing MedVQA architectures without requiring major modifications.

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EAAI Journal 2024 Journal Article

Adversarial robust decision-making under uncertainty learning and dynamic ensemble selection

Ruoxi Qin
Linyuan Wang
Xuehui Du
Jian Chen
Xingyuan Chen
Bin Yan

As the adversarial robustness research of deep neural networks has struggled in attack and defense games with static defense methodology, scholars have introduced the dynamic idea of the systems control to changeover the passive defense position though adapting decision-making. According to the different levels at which dynamism acts on neural networks, dynamic defense methods can be mainly divided into two categories: dynamic feedback control based on input level and uncertainty estimation detection based on decision level. Although both methods aim to hinder the success of the attacker, they cannot achieve the perfect conditions for constructing black box attacks because they ignore the positive role of dynamics in defense at the model level. Inspired by conventional ensemble selection technology in machine learning that treats different models as mutable objects for improving accuracy in uncertain data, this work investigates the robustness issue from a new dynamic aspect: model-level dynamic defense, whether the dynamic attributes depend on input or decision. Specifically, the Dirichlet prior combined with diversity constraint is imposed on the ensemble parameter in training phase to construct select criterion and candidate sub-models. Therefore, the final prediction of ensemble can be strategically selected though the rank of different sub-models’ uncertainty value for robust decision-making in the test phase. The experimental results indicate the comprehensive promotion of robustness (at least 4. 17% in black-box attack conditions and at least 1. 78% in the case of high-disturbance white-box attack budge) of the proposed method compared with common dynamic and static defense methods.

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IJCAI Conference 2016 Conference Paper

Bag-of-Embeddings for Text Classification

Peng Jin
Yue Zhang
Xingyuan Chen
Yunqing Xia

Words are central to text classification. It has been shown that simple Naive Bayes models with word and bigram features can give highly competitive accuracies when compared to more sophisticated models with part-of-speech, syntax and semantic features. Embeddings offer distributional features about words. We study a conceptually simple classification model by exploiting multi-prototype word embeddings based on text classes. The key assumption is that words exhibit different distributional characteristics under different text classes. Based on this assumption, we train multi-prototype distributional word representations for different text classes. Given a new document, its text class is predicted by maximizing the probabilities of embedding vectors of its words under the class. In two standard classification benchmark datasets, one is balance and the other is imbalance, our model outperforms state-of-the-art systems, on both accuracy and macro-average F-1 score.

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AAAI Conference 2015 Conference Paper

Dataless Text Classification with Descriptive LDA

Xingyuan Chen
Yunqing Xia
Peng Jin
John Carroll

Manually labeling documents for training a text classifier is expensive and time-consuming. Moreover, a classifier trained on labeled documents may suffer from overfitting and adaptability problems. Dataless text classification (DLTC) has been proposed as a solution to these problems, since it does not require labeled documents. Previous research in DLTC has used explicit semantic analysis of Wikipedia content to measure semantic distance between documents, which is in turn used to classify test documents based on nearest neighbours. The semantic-based DLTC method has a major drawback in that it relies on a large-scale, finely-compiled semantic knowledge base, which is difficult to obtain in many scenarios. In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents. In DescLDA, the LDA model is assembled with a describing device to infer Dirichlet priors from prior descriptive documents created with category description words. The Dirichlet priors are then used by LDA to induce category-aware latent topics from unlabeled documents. Experimental results with the 20Newsgroups and RCV1 datasets show that: (1) our DLTC method is more effective than the semantic-based DLTC baseline method; and (2) the accuracy of our DLTC method is very close to state-of-the-art supervised text classification methods. As neither external knowledge resources nor labeled documents are required, our DLTC method is applicable to a wider range of scenarios.

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