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Anchor-Free RepPoints and Attention Mechanism Based Adaptive Siamese Network for Object Tracking

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence 2024年第11期37卷 974-985页

作者： Yuan, Shuai Dou, Huize Geng, Jinyu Luan, Fangjun School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu University Shenyang110168 China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu University Shenyang110168 China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang110168 China

The high computational complexity of current Siamese network based target tracking algorithm during the candidate box generation stage results in poor real-time performance and reduced accuracy in complex scenarios. To address these issues, an anchor-free RepPoints and attention mechanism based adaptive Siamese network for object tracking is proposed. First, a large-kernel convolutional attention module is introduced in the backbone network of the Siamese subnetwork to extract global features of the target, enhancing the precision and generalization ability of the model. Second, a module for anchor-free multi-RepPoints is utilized to learn multiple RepPoints of the target, and then an adaptive learning weight coefficient module is employed to filter out more accurate target RepPoints, further improving model precision and robustness. Finally, RepPoints are transformed into predicted boxes, thereby eliminating the need for predefined candidate boxes, reducing computational complexity and enhancing real-time tracking performance. Experiments indicate that the proposed method achieves significant improvements in precision and success rate on four datasets. © 2024 science Press. All rights reserved.

关键词： Target tracking

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LMCG-Net: Integrating LLMs and ECG for LVEF-Based Classification for Pacing Patients

LMCG-Net: Integrating LLMs and ECG for LVEF-Based Classifica...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shim, Wonkyeong Park, Namjun Ko, Donggeun Gwag, Hyebin Park, Youngjun Park, Seungjung Kim, Jaekwang Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of Aim Future Ai Seoul Korea Republic of Samsung Changwon Hospital Changwon Korea Republic of Wonju Severance Christian Hospital Kangwon-do Korea Republic of Samsung Medical Center Seoul Korea Republic of Sungkyunkwan University Convergence Program for Social Innovation School of Convergence Seoul Korea Republic of

ISBN: (纸本)9798350386226

Detecting left ventricular systolic dysfunction (LVSD) traditionally relies on expensive and specialized echocardiography, limiting accessibility for many patients. To address this, researchers explore the potential of electrocardiography (ECG), a more affordable and widely available alternative, despite its historically limited performance in cardiac dysfunction detection. In this study, we present a novel approach that integrates a 1D convolutional neural networks (CNNs) with a large-scale language model (LLM) to simultaneously analyze sequential ECG data and non-sequential clinical metadata. To validate our model's effectiveness, we conducted rigorous comparative experiments on both specially collected clinical data and public datasets, achieving an impressive AUROC of 0.97 across both. Our findings underscore the capability of ECG-based ai to accurately predict LVSD including pacemaker patients, offering a rapid and cost-effective alternative to traditional echocardiography. This innovative approach could significantly enhance early diagnosis and management of cardiac dysfunction. © 2024 IEEE.

关键词： Convolutional neural networks

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Synthesizing CTA Image data for Type-B Aortic Dissection using Stable Diffusion Models

Synthesizing CTA Image Data for Type-B Aortic Dissection usi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ayman Abaid Muhammad Ali Farooq Niamh Hynes Peter Corcoran Ihsan Ullah School of Computer Science University of Galway Ireland School of Engineering College of Science and Engineering University of Galway Ireland University Hospital Galway Newcastle Road University of Galway Ireland Insight SFI Research Center for Data Analytics University of Galway Ireland

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative ai thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions.

关键词： Medical conditions Statistical analysis Generative ai Image processing Text to image Diffusion models Synthetic data Biomedical imaging

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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Confounder-Aware Medical data Selection for Fine-Tuning Pretrained Vision Models 22

Confounder-Aware Medical Data Selection for Fine-Tuning Pret...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ji, Anyang Kang, Qingbo Xu, Wei Wang, Changfan Li, Kang Lao, Qicheng West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China College of Computer Science Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China School of Biomedical Engineering University of Science and Technology of China Hefei China

ISBN: (纸本)9798331520526

The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-tuning paradigm. However, selecting appropriate medical data for downstream fine-tuning remains a significant challenge considering its annotation cost, privacy concerns, and the detrimental effects of confounding variables. In this work, we present a confounder-aware medical data selection approach for medical dataset curation aiming to select minimal representative data by strategically mitigating the undesirable impact of confounding variables while preserving the natural distribution of the dataset. Our approach first identifies confounding variables within data and then develops a distance-based data selection strategy for confounder-aware sampling with a constrained budget in the data size. We validate the superiority of our approach through extensive experiments across diverse medical imaging modalities, highlighting its effectiveness in addressing the substantial impact of confounding variables and enhancing the fine-tuning efficiency in the medical imaging domain, compared to other data selection approaches. © 2025 IEEE.

关键词： confounding variables data selection fine-tuning

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Synthesizing CTA Image data for Type-B Aortic Dissection using Stable Diffusion Models

arXiv

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arXiv 2024年

作者： Abaid, Ayman Farooq, Muhammad Ali Hynes, Niamh Corcoran, Peter Ullah, Ihsan School of Computer Science University of Galway Ireland School of Engineering College of Science and Engineering University of Galway Ireland Insight SFI Research Center for Data Analytics University of Galway Ireland University Hospital Galway University of Galway Newcastle Road Ireland

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative ai thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the tuned T2I CTA diffusion model was able to generate images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions. © 2024, CC BY.

关键词： Medical imaging

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Liu Min Li Chu He Xu Tian Junbin Mao Min Zeng Jin Liu Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications.

关键词： Performance evaluation Sensitivity Spaceborne radar Millimeter wave radar Depression data models Reliability Monitoring Signal resolution Diseases

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Variance-Covariance Regularization Improves Representation Learning

arXiv

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arXiv 2023年

作者： Zhu, Jiachen Evtimova, Katrina Chen, Yubei Shwartz-Ziv, Ravid LeCun, Yann New York University Computer Science Department United States New York University Center for Data Science United States UC Davis Electrical and Computer Engineering Department United States Meta AI FAIR United States

Transfer learning plays a key role in advancing machine learning models, yet conventional supervised pretraining often undermines feature transferability by prioritizing features that minimize the pretraining loss. In this work, we adapt a self-supervised learning regularization technique from the VICReg method to supervised learning contexts, introducing Variance-Covariance Regularization (VCReg). This adaptation encourages the network to learn high-variance, low-covariance representations, promoting learning more diverse features. We outline best practices for an efficient implementation of our framework, including applying it to the intermediate representations. Through extensive empirical evaluation, we demonstrate that our method significantly enhances transfer learning for images and videos, achieving state-of-the-art performance across numerous tasks and datasets. VCReg also improves performance in scenarios like long-tail learning and hierarchical classification. Additionally, we show its effectiveness may stem from its success in addressing challenges like gradient starvation and neural collapse. In summary, VCReg offers a universally applicable regularization framework that significantly advances transfer learning and highlights the connection between gradient starvation, neural collapse, and feature transferability. © 2023, CC0.

关键词： Supervised learning

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Soft Constraint in Local Structure Approximation-PINN

Soft Constraint in Local Structure Approximation-PINN

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Artificial Intelligence (Cai), IEEE Conference on

作者： Jian Cheng Wong Pao-Hsiung Chiu Chinchun Ooi My Ha Dao Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore Center for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

We previously presented a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-informed neural networks (PINNs). In these experiments, the incorporation of a Boundary Connectivity (BCXN) loss function was shown to greatly improve physics-informed learning across many problems, especially those with complex geometries. However, this imposition may not always be ideal, as the previously presented BCXN-loss strongly enforces a linear local structure at the boundary. While this assumption helps facilitate faster learning with an order of magnitude sparser training samples, this can adversely impact convergence in other situations. Hence, we propose a modification of this BCXN-loss that reduces the imposed structure to a soft constraint, allowing for more flexible learning and convergence. We further demonstrate the potential for this method to improve the convergence and performance of LSA-PINN across additional numerical experiments, with much smaller errors than existing methods in terms of the standard L2-norm metric. In particular, we have applied this method to the modelling of flow past complex geometries such as airfoils, which serve as the basic building block for various applications in fluid dynamics and renewable energy (e.g., in wind and tidal turbine design).

关键词： Geometry Training Renewable energy sources Wind energy Fluid dynamics Wind turbines Wind forecasting

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Detecting and Corrupting Convolution-based Unlearnable Examples 39

Detecting and Corrupting Convolution-based Unlearnable Examp...

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39th Annual AAai Conference on Artificial Intelligence, AAai 2025

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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