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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Semi-supervised Long-tail Endoscopic Image Classification

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Chinese Medical Sciences Journal 2022年第3期37卷 171-180,I0002页

作者： Runnan Cao Mengjie Fang Hailing Li Jie Tian Di Dong School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Advaneed Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited *** We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse ***-supervised learning algorithm FixMatch was applied based on consistency regularization and *** splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited *** The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall *** algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,*** a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average *** analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail *** SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume ***,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

关键词： endoscopic image artificial intelligence semi-supervised learning long-tail distribution image classification

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Overcoming data Limitations and Cross-Modal Interaction Challenges in Medical Visual Question Answering

Overcoming Data Limitations and Cross-Modal Interaction Chal...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yurun Bi Xingang Wang Qingao Wang Jinxiao Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Medical Visual Question Answering (Med-VQA) is a sophisticated multimodal technology within the medical domain that utilizes natural language processing and artificial intelligence techniques. It serves as a tool in the medical field to provide information and answers, aiming to correctly respond to medical questions related to specific professional medical images. The primary goal is to assist patients in understanding their physical condition and provide decision support for healthcare professionals. While Generalized Visual Question Answering has seen significant advancements, challenges persist in the medical domain. Firstly, medical data has inherent limitations, such as the scarcity of annotated medical datasets and the complexity of medical images (including factors like noise, artifacts, and low contrast). Secondly, in terms of cross-modal fusion, there exists an intricate relationship between medical images and textual information. The complexity of the relationship between medical images and medical text, as well as the incorporation of global contextual details, pose challenges. To address these issues, this paper proposes the Feature-Enhanced Network with Conditional Mixing. This innovative approach aims to overcome the scarcity and complexity of medical images without the need for additional datasets. Simultaneously, to tackle the challenge of cross-modal fusion and information interaction and to address the complex relationship between medical images and text, we propose the Cross-Adaptive Fusion Network. Experimental results conducted on the VQA-RAD dataset demonstrate noticeable improvements in accuracy compared to mainstream Med-VQA models.

关键词： Visualization Accuracy Noise Neural networks Medical services Question answering (information retrieval) data models

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Hierarchical Attention Feature Fusion and Refinement Network for Point Cloud Upsampling

Hierarchical Attention Feature Fusion and Refinement Network...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yaori Zhang Shujin Lin Fan Zhou Ruomei Wang National Engineering Research Center of Digital Life School of Computer Science and Engineering Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China School of Communication and Design Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

This paper presents a novel hierarchical attention feature fusion and refinement network designed to address challenges in existing deep learning based point cloud upsampling methods. The network combines self-attention layers with a multi-level feature extraction architecture, effectively integrating local and global features, thereby enhancing the robustness and uniformity of the point cloud. Furthermore, a spatial refinement module is employed to predict the offset between the generated coarse dense point clouds and real point clouds, thereby enhancing consistency with the ground truth. Concurrently, a filter function is applied in the loss function to handle outliers of generated point clouds. Extensive experimental results across multiple datasets indicate that our method outperforms existing approaches.

关键词： Point cloud compression Deep learning Feature extraction Robustness Encoding

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Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

arXiv

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arXiv 2024年

作者： Song, Ziying Liu, Lin Jia, Feiyang Luo, Yadan Jia, Caiyan Zhang, Guoxin Yang, Lei Wang, Li School of Computer Science & Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Information Technology and Electrical Engineering The University of Queensland St LuciaQLD4072 Australia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Intelligent Green Vehicle and Mobility The School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China

In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning. The key step to this system is related to 3D object detection that utilizes vehicle-mounted sensors such as LiDAR and cameras to identify the size, the category, and the location of nearby objects. Despite the surge in 3D object detection methods aimed at enhancing detection precision and efficiency, there is a gap in the literature that systematically examines their resilience against environmental variations, noise, and weather changes. This study emphasizes the importance of robustness, alongside accuracy and latency, in evaluating perception systems under practical scenarios. Our work presents an extensive survey of camera-only, LiDAR-only, and multimodal 3D object detection algorithms, thoroughly evaluating their trade-off between accuracy, latency, and robustness, particularly on datasets like KITTI-C and nuScenes-C to ensure fair comparisons. Among these, multi-modal 3D detection approaches exhibit superior robustness, and a novel taxonomy is introduced to reorganize the literature for enhanced clarity. This survey aims to offer a more practical perspective on the current capabilities and the constraints of 3D object detection algorithms in real-world applications, thus steering future research towards robustness-centric advancements. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Design and implementation of intelligent heart rate detection system based on STM32 6

Design and implementation of intelligent heart rate detectio...

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6th International Symposium on System and Software Reliability, ISSSR 2020

作者： Cai, Zengyu Peng, Zhongyuan Zhang, Jianwei Feng, Yuan Zhengzhou University of Light Industry School of Computer and Communication Engineering Henan Key Laboratory of Food Safety Data Intelligence Zhengzhou China

ISBN: (纸本)9780738104973

At present, people's lifestyle is more and more diverse, and people's eating habits are constantly changing. In this case, people's health is facing great challenges. The sudden rate of various heart rate chronic diseases is also rising. Under the heart rate detection, it can be prevented in advance so that the disease can be effectively treated. This paper designs an intelligent heart rate detection system, which can real-time detect its own heart rate. When the heart rate beats too fast, the voice prompt will be given, and every time the heart rate value detected will be sent to the family through wireless transmission. The heart rate system satisfies the requirements of heart rate LCD display, voice prompt and Bluetooth transmission. It has certain application value for medical treatment. © 2020 IEEE.

关键词： Heart

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Underwater Image Enhancement by Combining Multi-Attention with Recurrent Residual Convolutional U-Net (Acu-Net)

SSRN

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SSRN 2023年

作者： Wang, Shuqi Chen, Zhixiang Wang, Hui College of Physics and Information Engineering Minnan Normal University Zhangzhou363000 China College of Computer Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Minnan Normal University Zhangzhou363000 China

The scattering and absorption of light lead to color distortion and blurred details in the captured underwater images. Although underwater image enhancement algorithms have made significant breakthroughs in recent years, enhancing the effectiveness and robustness of underwater degraded images is still a challenging task. To improve the quality of underwater images, we propose a combined multi-attention mechanism and recurrent residual convolutional U-Net (ACU-Net) for underwater image enhancement. First, we add a dual-attention mechanism and convolution module to the U-Net encoder. It can unequally extract features in different channels and spaces and make the extracted image feature information more accurate. Second, we add an attention gate module and recurrent residual convolution module to the U-Net decoder. It helps extract features fully and facilitates the recovery of more detailed information when the image is generated. Finally, we test the subjective results and objective evaluation of our proposed algorithm on synthetic and real datasets. The experimental results show that the robustness of our algorithm outperforms the other five classical algorithms, such as in enhancing underwater images with different color shifts and turbidity. Moreover, it corrects the color bias and improves the contrast and detailed texture of the images. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Hybrid Optimization Algorithm Based on Arithmetic and Golden Sine Algorithms for Constrained engineering Problem

Hybrid Optimization Algorithm Based on Arithmetic and Golden...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Qingxin Jia, Heming Wu, Di Li, Ni Qi, Qi Huang, Xiaoqing Hainan University School of Computer Science and Technology Haikou China Sanming University School of Information Engineering Sanming China Sanming University School of Education and Music Sanming China Hainan Normal University School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Intelligence Education Ministry of Education Haikou China Hainan University School of Tourism Haikou China

ISBN: (纸本)9781665465335

Arithmetic Optimization Algorithm (AOA) is a newly proposed meta-heuristics (MAs) inspired by the major arithmetic operators. Although it provides satisfactory solutions to several real-world problems, when facing complex problems, AOA also easily suck into local optimal solution and slow convergence speed. Golden Sine Algorithm (Gold-SA) is another MAs and has an acceptable local search capability with fewer parameters. Considering these situations, this paper we propose a novel hybrid optimization algorithm (HAGA) that integrated both AOA and Gold-SA to achieve superior optimization ability. In HAGA, the search agents are divided into two consistent subgroups and optimized by AOA and Gold-SA, respectively. By this operator, we can exchange useful information and utilize their advantages to find the optimal or near-optimal regions throughout the iterative process. In addition, Lens Opposition- Based Learning (LOBL) is employed to accelerate the convergence speed. We test the proposed method using thirteen benchmark functions and a constrained engineering problem. The results prove the significant superiority of HAGA to those of four counterpart methods in terms of optimization accuracy and convergence speed. © 2022 IEEE.

关键词： Constrained optimization

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FortisAVQA and MAVEN: a Benchmark dataset and Debiasing Framework for Robust Multimodal Reasoning

arXiv

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arXiv 2025年

作者： Ma, Jie Gao, Zhitao Chai, Qi Liu, Jun Wang, Pinghui Tao, Jing Su, Zhou Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510000 China

Audio-Visual Question Answering (AVQA) is a challenging multimodal reasoning task requiring intelligent systems to answer natural language queries based on paired audio-video inputs accurately. However, existing AVQA approaches often suffer from overfitting to dataset biases, leading to poor robustness. Moreover, current datasets may not effectively diagnose these methods. To address these challenges, we first introduce a novel dataset, FortisAVQA, constructed in two stages: (1) rephrasing questions in the test split of the public MUSIC-AVQA dataset and (2) introducing distribution shifts across questions. The first stage expands the test space with greater diversity, while the second enables a refined robustness evaluation across rare, frequent, and overall question distributions. Second, we introduce a robust Multimodal Audio-Visual Epistemic Network (MAVEN) that leverages a multifaceted cycle collaborative debiasing strategy to mitigate bias learning. Experimental results demonstrate that our architecture achieves state-of-the-art performance on FortisAVQA, with a notable improvement of 7.81%. Extensive ablation studies on both datasets validate the effectiveness of our debiasing components. Additionally, our evaluation reveals the limited robustness of existing multimodal QA methods. We also verify the plug-and-play capability of our strategy by integrating it with various baseline models across both datasets. Our dataset and code are available at https://***/reml-group/fortisavqa. Copyright © 2025, The Authors. All rights reserved.

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Investigation of Vertically Stacked Horizontal Gate-All-Around SI Nanosheet Ion Sensitive Field Effect Transistor For Detection of C-Reactive Protein

Investigation of Vertically Stacked Horizontal Gate-All-Arou...

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China Semiconductor Technology International Conference (CSTIC)

作者： Yang Liu Qingzhu Zhang Junjie Li Cinan Wu Lei Cao Yanna Luo Zhaohao Zhang Shuhua Wei Qianhui Wei Jiaxin Yao Jiawei Hu Meiyan Qin Enxu Liu Yanchu Han LianLian Li YingLu Li Tao Yang Na Zhou Jianfeng Gao Junfeng Li Key Laboratory of Microelectronic Devices and Integration Technology Institute of Microelectronics Chinese Academy of Sciences Beijing China College of Big Data and Information Engineering Guizhou University Guiyang China School of Information Science and Technology North China University of Technology Beijing China State Key Laboratory of Advanced Materials for Smart Sensing GRINM Group Co. Ltd. Beijing China

In this work, based on advanced gate-all-around (GAA) technology, the extended sensing gate (ESG) GAA Si nanosheet (SiNS) ion sensitive field effect transistor (ISFET) sensor was fabricated and reported for the first time. Due to the GAA structures and the vertically-stacked SiNS channels, the average sensitivity of the ESG GAA SiNS ISFET sensor can be reached 58.8 mV/pH, which provides good gate control and ultra-sensitive detection capabilities. In addition, the actual minimum detection concentration of C-reactive protein (CRP) by ESG GAA SiNS ISFET sensor in 1$\times$ PBS environment is as low as 100pg/mL, which is much lower than the normal concentration of human body.

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