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9th IEEE International Conference on Behavioural and Social Computing, BESC 2022

作者： Bi, Xin Zhang, Guoliang Yuan, Mingxu Huang, Xuechun Zhao, Xiangguo Northeastern University Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Shenyang110819 China Northeastern University Key Laboratory of Liaoning Province on Deep Engineering and Intelligent Technology Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Software Shenyang110819 China

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

Prediction of electrocardiogram (ECG) recordings has great significance for early diagnosis of cardiac diseases. However, the long-term ECG prediction task aims to output future ECG recordings with multiple PQRST complexes. Leveraging deep learning methods in the long-term prediction task is still faced with the challenges of extreme peak points and varying periodic intervals. Thus, we address these issues by proposing an ECG prediction framework with extreme peak and interval calibrations. Our framework is capable of learning time-series features via a triple-channel prediction network and emphasizing amplitude residuals of extreme peaks in our designed training loss. In addition, we further design a calibration network to reduce the deviation of periodic intervals, which is capable of alleviating the problem of unaligned predictions resulting from the inaccurate periodic intervals. The experimental results indicate that our framework has an excellent performance in the long-term ECG prediction task. © 2022 IEEE.

关键词： Electrocardiograms

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing 39

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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LIFT+: Lightweight Fine-Tuning for Long-Tail Learning

arXiv

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arXiv 2025年

作者： Shi, Jiang-Xin Wei, Tong Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm has emerged as a prominent approach for addressing long-tail learning tasks in the era of foundation models. However, the impact of fine-tuning strategies on long-tail learning performance remains unexplored. In this work, we disclose that existing paradigms exhibit a profound misuse of fine-tuning methods, leaving significant room for improvement in both efficiency and accuracy. Specifically, we reveal that heavy fine-tuning (fine-tuning a large proportion of model parameters) can lead to non-negligible performance deterioration on tail classes, whereas lightweight fine-tuning demonstrates superior effectiveness. Through comprehensive theoretical and empirical validation, we identify this phenomenon as stemming from inconsistent class conditional distributions induced by heavy fine-tuning. Building on this insight, we propose LIFT+, an innovative lightweight fine-tuning framework to optimize consistent class conditions. Furthermore, LIFT+ incorporates semantic-aware initialization, minimalist data augmentation, and test-time ensembling to enhance adaptation and generalization of foundation models. Our framework provides an efficient and accurate pipeline that facilitates fast convergence and model compactness. Extensive experiments demonstrate that LIFT+ significantly reduces both training epochs (from ∼100 to ≤15) and learned parameters (less than 1%), while surpassing state-of-the-art approaches by a considerable margin. The source code is available at https://***/shijxcs/LIFT-plus. Copyright © 2025, The Authors. All rights reserved.

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A Simplified Student Network with Multi-teacher Feature Fusion for Industrial Defect Detection 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Pei, Mingjing Liu, Ningzhong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Electronics and Information Engineering West Anhui University Lu’an China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Improved industrial defect detection is deemed critical for ensuring high-quality manufacturing processes. Despite the effectiveness of knowledge distillation in detecting defects, there are still challenges in extracting useful features and designing a better student network. In this study, a new framework based on knowledge distillation is proposed to address these issues. To avoid overfitting and balance anomaly detection between image and pixel levels, a simplified student decoding network is designed. To extract more diverse features, a multi-teacher network is used in the teacher network. Simple addition and concatenation operations for feature maps were not sufficient, the method fuses features iteratively using attention mechanisms utilized for the multi-teacher networks. The approach is evaluated on two benchmark datasets for industrial defect detection, Mvtec and BTAD, and significantly improved performance is achieved compared to the other methods. An average accuracy of 99.22% and 97.46% for image-level and pixel-level ROC-AUC, respectively, is achieved on the Mvtec dataset, and 93.47% and 96.3% on the BTAD dataset. The proposed framework shows effectiveness in detecting defects in industrial settings, as demonstrated by the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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Scratch Each Other’s Back: Incomplete Multi-modal Brain Tumor Segmentation Via Category Aware Group Self-Support Learning

Scratch Each Other’s Back: Incomplete Multi-modal Brain Tum...

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International Conference on computer Vision (ICCV)

作者： Yansheng Qiu Delin Chen Hongdou Yao Yongchao Xu Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering

Although Magnetic Resonance Imaging (MRI) is very helpful for brain tumor segmentation and discovery, it often lacks some modalities in clinical practice. As a result, degradation of prediction performance is inevitable. According to current implementations, different modalities are considered to be independent and non-interfering with each other during the training process of modal feature extraction, however they are complementary. In this paper, considering the sensitivity of different modalities to diverse tumor regions, we propose a Category Aware Group Self-Support Learning framework, called GSS, to make up for the information deficit among the modalities in the individual modal feature extraction phase. Precisely, within each prediction category, predictions of all modalities form a group, where the prediction with the most extraordinary sensitivity is selected as the group leader. Collaborative efforts between group leaders and members identify the communal learning target with high consistency and certainty. As our minor contribution, we introduce a random mask to reduce the possible biases. GSS adopts the standard training strategy without specific architectural choices and thus can be easily plugged into existing incomplete multi-modal brain tumor segmentation. Remarkably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets demonstrate that GSS can improve the performance of existing SOTA algorithms by 1.27-3.20% in Dice on average. The code is released at https://***/qysgithubopen/GSS.

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MTGA: Multi-View Temporal Granularity Aligned Aggregation for Event-Based Lip-Reading 39

MTGA: Multi-View Temporal Granularity Aligned Aggregation fo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Wenhao Wang, Jun Luo, Yong Yu, Lei Yu, Wei He, Zheng Shen, Jialie School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Electronics and Information Wuhan University China Department of Computer Science City University of London United Kingdom

ISBN: (纸本)157735897X

Lip-reading is to utilize the visual information of the speaker's lip movements to recognize words and sentences. Existing event-based lip-reading solutions integrate different frame rate branches to learn spatio-temporal features of varying granularities. However, aggregating events into event frames inevitably leads to the loss of fine-grained temporal information within frames. To remedy this drawback, we propose a novel framework termed Multi-view Temporal Granularity aligned Aggregation (MTGA). Specifically, we first present a novel event representation method, namely time-segmented voxel graph list, where the most significant local voxels are temporally connected into a graph list. Then we design a spatio-temporal fusion module based on temporal granularity alignment, where the global spatial features extracted from event frames, together with the local relative spatial and temporal features contained in voxel graph list are effectively aligned and integrated. Finally, we design a temporal aggregation module that incorporates positional encoding, which enables the capture of local absolute spatial and global temporal information. Experiments demonstrate that our method outperforms both the event-based and video-based lip-reading counterparts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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MODEL CHECKING FOR MULTI-AGENT SYSTEMS MODELED BY EPISTEMIC PROCESS CALCULUS

arXiv

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arXiv 2025年

作者： Yu, Qixian Cao, Zining Hui, Zong Zhou, Yuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Faculty of Computer and Software Engineering Huaiyin Institute Of Technology Huaian223001 China

This paper presents a comprehensive framework for modeling and verifying multi-agent systems. The paper introduce an Epistemic Process Calculus for multi-agent systems, which formalizes the syntax and semantics to capture the essential features of agent behavior interactions and epistemic states. Building upon this calculus, we propose ATLE, an extension of Alternating-time Temporal Logic incorporating epistemic operators to express complex properties related to agent epistemic state. To verify ATLE specifications, this paper presents a model checking algorithm that systematically explores the state space of a multi-agent system and evaluates the satisfaction of the specified properties. Finally, a case study is given to demonstrate the method. © 2025, CC BY.

关键词： Multi agent systems

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Hybrid Feature Measurement based on Linear and Nonlinear Nonnegative Matrix Factorization 22

Hybrid Feature Measurement based on Linear and Nonlinear Non...

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Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Sicong Ye Yang Zhao Jihong Pei The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering in Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450398336

The nonnegative matrix factorization algorithm is an effective data dimensionality reduction method. The principle is to convert the image into a nonnegative linear combination of low dimensional basis images. Nonnegative matrix factorization can be divided into linear algorithm and nonlinear algorithm. Because of different decomposition theory, linear NMF algorithms mainly extract first-order features of data, while nonlinear NMF algorithms mainly extract high-order features. Most of the current studies only focus on one of the models without combining the two together, which leads to the lack of data features. Therefore it is necessary to integrate the two types of algorithms for research. The paper proposes hybrid feature measurement based on linear and nonlinear nonnegative matrix factorization. The algorithm utilizes the idea of feature fusion. The basis image features of the two algorithms are mixed in the model. Finally a feature similarity measurement is obtained as the measure method. The proposed algorithm has good performance on the public datasets and effectively improves the recognition.

关键词： Hybrid feature measurement Nonnegative matrix factorization Basis image

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DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

arXiv

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arXiv 2025年

作者： Liu, Guanlin Yu, Xiaomei Liu, Zihao Li, Xue Fan, Xingxu Zheng, Xiangwei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China State Key Laboratory of High-end Server and Storage Technology Jinan250300 China

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： QiSong Wang JiaRou Wu YingZhuo Wang HuiLi Qu Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Australia

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.

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