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Knowledge-Guided Cross-Modal Alignment and Progressive Fusion for Chest X-Ray Report Generation

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 557-567页

作者： Huang, Lili Cao, Yiming Jia, Pengcheng Li, Chenglong Tang, Jin Li, Chuanfu Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc Anhui Prov Key Lab Secur Artificial IntelligenceM Hefei 230601 Peoples R China Anhui Univ Chinese Med Affiliated Hosp 1 Hefei 230601 Peoples R China

The task of chest X-ray report generation, which aims to simulate the diagnosis process of doctors, has received widespread attention. compared with the image caption task, chest X-ray report generation is more challenging since it needs to generate a longer and more accurate description of each diagnostic part in chest X-ray images. Most of existing works focus on how to extract better visual features or more accurate text expression based on existing reports. However, they ignore the interactions between visual and text modalities and are thus obviously not in line with human thinking. A small part of works explore the interactions of visual and text modalities, but data-driven learning of cross-modal information mapping can not break the semantic gap between different modalities. In this work, we propose a novel approach called Knowledge-guided Cross-modal Alignment and Progressive fusion (KCAP), which takes the knowledge words from a created medical knowledge dictionary as the bridge to guide the cross-modal feature alignment and fusion, for accurate chest X-ray report generation. In particular, we create the medical knowledge dictionary by extracting medical phrases from the training set and then selecting some phrases with substantive meanings as knowledge words based on their frequency of occurrence. Based on the knowledge words from the medical knowledge dictionary, the visual and text modalities are interacted by a mapping layer for the enhancement of the features of two modalities, and then the alignment fusion module is introduced to mitigate the semantic gap between visual and text modalities. To retain the important details of the original information, we design a progressive fusion scheme to integrate the advantages of both salient fused and original features to generate better medical reports. The experimental results on IU-Xray and MIMIC datasets demonstrate the effectiveness of the proposed KCAP.

关键词： Visualization Feature extraction X-ray imaging Semantics Decoding Medical diagnostic imaging Diseases Accuracy Dictionaries Logic gates Chest X-ray image processing cross-modal alignment progressive information fusion report generation

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IterSum: Iterative summarization based on document topological structure

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INFORMATION PROCESSING & MANAGEMENT 2025年第1期62卷

作者： Yu, Shuai Gao, Wei Qin, Yongbin Yang, Caiwei Huang, Ruizhang Chen, Yanping Lin, Chuan Guizhou Univ Engn Res Ctr Text Comp & Cognit Intelligence Minist Educ Guiyang 550025 Guizhou Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Guizhou Peoples R China

Document structure plays a crucial role in understanding and analyzing document information. However, effectively encoding document structural features into the Transformer architecture faces significant challenges. This is primarily because different types of documents require the model to adopt varying structural encoding strategies, leading to a lack of a unified framework that can broadly adapt to different document types to leverage their structural properties. Despite the diversity of document types, sentences within a document are interconnected through semantic relationships, forming a topological semantic network. This topological structure is essential for integrating and summarizing information within the document. In this work, we introduce IterSum, a versatile text summarization framework applicable to various types of text. In IterSum, we utilize the document's topological structure to divide the text into multiple blocks, first generating a summary for the initial block, then combining the current summary with the content of the next block to produce the subsequent summary, and continuing in this iterative manner until the final summary is generated. We validated our model on nine different types of public datasets, including news, knowledge bases, legal documents, and guidelines. Both quantitative and qualitative analyses were conducted, and the experimental results show that our model achieves state-of-the-art performance on all nine datasets measured by ROUGE scores. We also explored low-resource summarization, finding that even with only 10 or 100 samples in multiple datasets, top-notch results were obtained. Finally, we conducted human evaluations to further validate the superiority of our model.

关键词： Abstractive summarization Iterative summarization Document topological structure Low-resource summarization

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Phased Noise Enhanced Multiple Feature Discrimination Network for fabric defect detection

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ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2025年 149卷

作者： Ma, Haoran Li, Zuoyong Fan, Haoyi Zheng, Xiangpan Yan, Jiaquan Hu, Rong Fujian Univ Technol Sch Comp Sci & Math Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Minjiang Univ Sch Comp & Big Data Fujian Prov Key Lab Informat Proc & Intelligent Co Fuzhou 350121 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Minjiang Univ Coll Phys & Elect Informat Engn Fuzhou 350121 Peoples R China Wuyi Univ Key Lab Cognit Comp & Intelligent Informat Proc Fujian Educ Inst Wuyishan 354300 Peoples R China

Fabric defect detection is crucial for evaluating the quality of textile products. However, the subtlety and scarcity of fabric defects pose challenges to the task of detecting. Therefore, we propose a Phased Noise Enhanced Multiple Feature Discrimination Network, which is based on phased noise enhancement strategy and multiple feature discrimination module to improve the model's ability to identify complex and subtle flaws. Specifically, we propose the phased noise enhancement strategy in the feature space to simulate feature-level anomalies that are closer to reality. This strategy can improve the input quality of the feature reconstructor, so that helps its perception and reconstruction ability. Then, we propose the multiple feature discrimination module, which has dual feature branches to improve its ability to distinguish more complex detailed texture features. In addition, we propose a subsampling module to reduce feature redundancy and ensure efficient inference speed. Finally, we conduct extensive experiments and ablation studies on two publicly available fabric datasets, AITEX and Kaggle Fabric. The experimental results show that the proposed method achieved 92% and 100% image level metrics and 97.5% and 67.1% pixel level metrics on two datasets, respectively, which is superior to the current state-of-the-art methods. In addition, our method also demonstrated significant performance in generalization experiments.

关键词： Fabric defect detection Generative adversarial network Anomaly detection

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Partial multi-label feature selection based on label distribution learning

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PATTERN REcognitION 2025年 164卷

作者： Lin, Yaojin Li, Yulin Lin, Shidong Guo, Lei Mao, Yu Minnan Normal Univ Sch Comp Sci Zhangzhou 363000 Peoples R China Fujian Prov Univ Key Lab Data Sci & Intelligence Applicat Zhangzhou Peoples R China Wuyi Univ Fujian Key Lab Big Data Applicat & Intellectualiza Wuyishan 354300 Fujian Peoples R China

Partial Multi-label Learning (PML) induces a multi-classifier in an imprecise supervised environment, where the candidate labels associated with each training sample are partially valid. The high-dimensional feature space, presented in PML data accompanied by ambiguous labeling information, is a significant challenge for learning. In this paper, we propose a PML feature selection method based on label Distribution Learning (LDL), which handles the above challenges by correcting misleading and then selecting common and label-specific features. In the first procedure, the error distribution hypothesis is constructed, which divides the structure of ambiguous label information into minority and majority error distribution according to the error amount that may appear in the data annotation process. Under the analysis of the hypothesis, the label credibility distribution data (LCDD) was generated by identifying and correcting errors, where the fractional category of each label associated with each training sample describes the probability that the label belongs to that sample. In the second procedure, a discriminative feature subset is selected for PML based on LCDD by common and label-specific feature constraints. Experiments on three synthetic and five real PML datasets demonstrate the effectiveness of the proposed method.

关键词： Partial multi-label learning Feature selection Partial label enhancement Error distribution hypothesis label distribution learning

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Hierarchical Convolutional Neural Network with Knowledge complementation for Long-Tailed Classification

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM data 2024年第6期18卷 1-22页

作者： Zhao, Hong Li, Zhengyu He, Wenwei Zhao, Yan Minnan Normal Univ Sch Comp Sci Zhangzhou Peoples R China Minnan Normal Univ Key Lab Data Sci & Intelligence Applicat Zhangzhou Peoples R China

Existing methods based on transfer learning leverage auxiliary information to help tail generalization and improve the performance of the tail classes. However, they cannot fully exploit the relationships between auxiliary information and tail classes and bring irrelevant knowledge to the tail classes. To solve this problem, we propose a hierarchical CNN with knowledge complementation, which regards hierarchical relationships as auxiliary information and transfers relevant knowledge to tail classes. First, we integrate semantics and clustering relationships as hierarchical knowledge into the CNN to guide feature learning. Then, we design a complementary strategy to jointly exploit the two types of knowledge, where semantic knowledge acts as a prior dependence and clustering knowledge reduces the negative information caused by excessive semantic dependence (i.e., semantic gaps). In this way, the CNN facilitates the utilization of the two complementary hierarchical relationships and transfers useful knowledge to tail data to improve long-tailed classification accuracy. Experimental results on public benchmarks show that the proposed model outperforms existing methods. In particular, our model improves accuracy by 3.46% compared with the second-best method on the long-tailed tieredImageNet dataset.

关键词： Long-tailed classification deep learning knowledge transfer hierarchical relationship

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DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth-Based 3D Reconstruction

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ACM TRANSACTIONS ON MULTIMEDIA compUTING COMMUNICATIONS AND APPLICATIONS 2024年第12期20卷 1-17页

作者： Liu, Caixia Chen, Yali Zhu, Minhong Hao, Chenhui Li, Haisheng Wang, Xiaochuan Beijing Technol & Business Univ Sch Comp & Artificial Intelligence Beijing Key Lab Big Data Technol Food Safety Beijing Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing Key Lab Multimedia & Intelligent Software Beijing Peoples R China

Although deep networks-based 3D reconstruction methods can recover the 3D geometry given few inputs, they may produce unfaithful reconstruction when predicting occluded parts of 3D objects. To address the Decoder-Based Generator (EDGen) and Voxel-Point Embedding Network-Based Discriminator (VPDis) for 3D reconstruction from a monocular depth image of an object. Firstly, EDGen decodes the features from the 2.5D voxel grid representation of an input depth image and generates the 3D occupancy grid under GAN losses and a sampling point loss. The sampling loss can improve the accuracy of predicted points with high uncertainty. VPDis helps reconstruct the details under voxel and point adversarial losses, respectively. Experimental results show that DEGAN not only outperforms several state-of-the-art methods on both public ModelNet and ShapeNet datasets but also predicts more reliable occluded/missing parts of 3D objects.

关键词： 3D shape reconstruction Generative adversarial network Voxel-point embedding network Sampling point loss Monocular depth image

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Hierarchical feature selection via joint local label enhancement and neighborhood label distribution correlation

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KNOWLEDGE-BASED SYSTEMS 2025年 311卷

作者： Wang, Chenxi Liu, Weihang Guo, Lei Lin, Yaojin Minnan Normal Univ Sch Comp Sci Zhangzhou 363000 Peoples R China Fujian Prov Univ Key Lab Data Sci & Intelligence Applicat Zhangzhou Peoples R China Wuyi Univ Fujian Key Lab Big Data Applicat & Intellectualiza Wuyishan 354300 Fujian Peoples R China

Hierarchical classification learning is an efficient approach for classification tasks with hierarchical structure. For large-scale hierarchical classification, feature selection is effectively addressed the high-dimensional feature space problem caused by complex semantic structures. However, the performance of algorithms is often constrained by the class imbalance distribution in the dataset. In particular, different subtasks within the hierarchical structure each faces different degrees of class imbalance challenges. In this paper, we propose a new Hierarchical Feature Selection method joint local label enhancement and Neighborhood label Distribution correlation (HFS-NLD). Firstly, the class imbalance characteristics specific to each subtask in the hierarchy are exploited to transform hierarchical logical label into label distribution. Secondly, the label distribution prediction of each sample is driven by the unique neighborhood label distribution correlation of that sample. Subsequently, recursive selection process is employed to identify a discriminative common feature subset. Finally, the experimental results demonstrate the efficacy of our proposed algorithm across seven datasets with varying ratios of class imbalance.

关键词： Feature selection Hierarchical classification learning label enhancement label distribution

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Harnessing code domain insights: Enhancing programming Knowledge Tracing with Large Language Models

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KNOWLEDGE-BASED SYSTEMS 2025年 317卷

作者： Sun, Xinjie Liu, Qi Zhang, Kai Shen, Shuanghong Yang, Lina Li, Hui Liupanshui Normal Univ Sch Comp Sci Liupanshui Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Sci & Technol China Sch Artificial Intelligence & Data Sci Hefei Peoples R China State Key Lab Cognit Intelligence Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

Knowledge Tracing (KT) evaluates students' mastery of knowledge by analyzing their historical interactions with exercises and predicts their performance on subsequent tasks. Although traditional KT methods have begun to focus on the assessment of programming skills, they are limited by the bottleneck of manually annotating knowledge concepts (KCs) and the inadequacy of constructing relationships between these points. To address this issue, we propose a Knowledge Tracing method Enhanced by the powerful Code insight capabilities of Large Language Models (CEKT). Specifically, we designed three different prompt tuning strategies for Large Language Models (LLMs) to comprehensively construct Q-matrices that cover KCs and their relationships across various programming domains and exercises. Additionally, we developed a knowledge graph integrating three dimensions to express the complex relationships between KCs in a fine-grained manner, thereby providing a more accurate assessment of students' knowledge mastery. Furthermore, we established a graph attention network among KCs to promote interaction between representations of similar syntactic KCs, enhancing the inference capability of students' programming knowledge state and the effectiveness of KT. Through this approach, we achieved high-quality and interpretable knowledge state inference and demonstrated outstanding performance in predicting student outcomes. Our work highlights a potential future research direction for prompt-tuned LLMs in the KT domain, emphasizing high interpretability and efficiency. For broader research purposes, we have prepared to release our data and source code at https://***/xinjiesun-ustc/CEKT, encouraging further innovation in this field.

关键词： Knowledge tracing data mining Intelligent tutoring systems Large language models Efficiency and interpretability

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Multimodal evidence for predictive coding in sentence oral reading

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CEREBRAL CORTEX 2023年第13期33卷 8620-8632页

作者： Zhao, Bin Zhang, Gaoyan Wang, Longbiao Dang, Jianwu Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin 300350 Peoples R China Pengcheng Lab Shenzhen 518055 Peoples R China

Sentence oral reading requires not only a coordinated effort in the visual, articulatory, and cognitive processes but also supposes a top-down influence from linguistic knowledge onto the visual-motor behavior. Despite a gradual recognition of a predictive coding effect in this process, there is currently a lack of a comprehensive demonstration regarding the time-varying brain dynamics that underlines the oral reading strategy. To address this, our study used a multimodal approach, combining real-time recording of electroencephalography, eye movements, and speech, with a comprehensive examination of regional, inter-regional, sub-network, and whole-brain responses. Our study identified the top-down predictive effect with a phrase-grouping phenomenon in the fixation interval and eye-voice span. This effect was associated with the delta and theta band synchronization in the prefrontal, anterior temporal, and inferior frontal lobes. We also observed early activation of the cognitive control network and its recurrent interactions with the visual-motor networks structurally at the phrase rate. Finally, our study emphasizes the importance of cross-frequency coupling as a promising neural realization of hierarchical sentence structuring and calls for further investigation.

关键词： predictive coding sentence oral reading brain network dynamics microstate analysis eye-voice span

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Adversarial Domain Generalized Transformer for Cross-Corpus Speech Emotion Recognition

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IEEE TRANSACTIONS ON AFFECTIVE compUTING 2024年第2期15卷 697-708页

作者： Gao, Yuan Wang, Longbiao Liu, Jiaxing Dang, Jianwu Okada, Shogo Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin 300072 Peoples R China Japan Adv Inst Sci & Technol JAIST Sch Informat Sci Nomi Ishikawa 9231211 Japan

Speech emotion recognition (SER) promotes the development of intelligent devices, which enable natural and friendly human-computer interactions. However, the recognition performance of existing approaches is significantly reduced on unseen datasets, and the lack of sufficient training data limits the generalizability of deep learning models. In this article, we analyze the impact of the domain generalization method on cross-corpus SER and propose an adversarial domain generalized transformer (ADoGT), which is aimed at learning a shared feature distribution for the source and target domains. Specifically, we investigate the effect of domain adversarial learning by eliminating nonaffective information. We also combine the center loss with the softmax function as joint supervision to learn discriminative features. Moreover, we introduce unsupervised transfer learning to extract additional features, and incorporate a gated fusion model to learn the complementary information of the features learned by the supervised feature extractor and pretrained model. The proposed transformer based domain generalization method is evaluated using four emotional datasets. We also provide an ablation study of different domain adversarial model structures and feature fusion models. The results of comparative experiments demonstrate the effectiveness of the proposed ADoGT.

关键词： Feature extraction Task analysis Transformers Training Emotion recognition data models data mining Speech emotion recognition cross-corpus adversarial learning domain generalization

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