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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Tingting Zhao Zuoyong Li Li Quan Kun Zeng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance.

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Projection methods with linesearch technique for pseudomonotone equilibrium problems and fixed point problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第1期83卷 3-14页

作者： Zhu, Li-Jun Yao, Yonghong Postolache, Mihai The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China Health Big Data Research Institute of North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China Health Big Data Research Institute of North Minzu University Yinchuan750021 China School of Mathematical Sciences Tiangong Uni-versity Tianjin300387 China Center for General Education China Medical University Taichung40402 Taiwan Department of Interior Design Asia University Taichung Taiwan Romanian Academy Gh. Mihoc-C. Iacob Institute of Mathematical Statistics and Applied Mathematics Bucharest050711 Romania University "Politehnica" of Bucharest Department of Mathematics and Informatics Bucharest060042 Romania

In this paper, we discuss pseudomonotone equilibrium problems and fixed point problems in real Hilbert spaces. With the help of linesearch technique, we propose a projection algorithm without any Lipschitz-type condition for solving equilibrium problems and fixed point problems. We show that the constructed algorithm converges strongly to a common element of the investigated problems. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Computers

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China SYSTEMA Research Center European University Cyprus Nicosia Cyprus

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multistage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Unsupervised Time Series Anomaly Detection Based on Adversarial Interpolation and Pseudo-anomaly Calibration

Unsupervised Time Series Anomaly Detection Based on Adversar...

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International Conference on control, Automation and Robotics (ICCAR)

作者： Xinwei Chen Xiaohui Lin Zuoyong Li Haoyi Fan Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Existing work on unsupervised time series anomaly detection relies on clean training datasets (free of anomalies), which is often violated in practice. In this work, we propose an unsupervised time series anomaly detection method based on adversarial interpolation and pseudo-anomaly calibration for training an anomaly detector under data con-tamination. Specifically, the normality description learned from the model is improved by using the following two proposed calibration methods: (1) by enforcing adversarial interpolation to learn an effective normality description from the typical samples rather than the edge or abnormal samples, and (2) by discriminating the original samples from the simulated pseudo-anomaly samples to learn the exact normality description boundary. These two calibration ways result in the normality description that prevents data contamination. Experimental results compared with baselines on public time series datasets demonstrate the effectiveness of our proposed method.

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical information

STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medi...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Wentong Xu Chao Fu Jing Yu Caifeng Si Zuoyong Li Chaochao Li Haoyi Fan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Ultrasound First Affiliated Hospital of Zhengzhou University Zhengzhou Henan China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data. Medical image analysis, as a common tool, faces the challenge of how to further enhance the detection performance for thyroid cancer, especially with the application of various deep learning methods in the thyroid-related field. In this paper, we validate the effectiveness of sonographic feature descriptions (textual information) for thyroid cancer diagnosis. Building on this, we propose a simple multimodal self-supervised framework that leverages both textual and image modalities for thyroid cancer anomaly detection. Results on a private dataset demonstrate the effectiveness and superiority of our proposed method compared to advanced methods.

关键词： Deep learning Training Image analysis Thyroid cancer Signal processing Feature extraction Informatics Anomaly detection Medical diagnostic imaging Thyroid

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Dual-Stream Fusion Network With Multi-Head Self-Attention for Multi-Modal Fake News Detection

SSRN

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SSRN 2024年

作者： Yang, Yimei Liu, Jinping Yang, Yujun Cen, Lihui School of Mathematics and Statistics Hunan Normal University Changsha410081 China Huaihua University Hunan Huaihua418000 China College of Information Science and Engineering Hunan Normal University Changsha China School of Automation Central South University Changsha410083 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

With the rapid development of social media platforms such as Weibo, WeChat, and other new media, as well as the rise of deepfake technologies, the identification and containment of fake information have posed significant challenges, becoming one of the key challenges faced by society and governmental institutions. Therefore, the development of intelligent and efficient methods for detecting fake news has become a top priority in combating misinformation. Addressing challenges in current fake news detection techniques, such as cross-lingual issues, data imbalance, and multimodal information fusion, this paper aims to propose a dual-stream fusion network model (DSF-MHSA) based on deep learning for detecting fake news across web pages, images, and text. The model combines the deep learning models ERNIE-M, AlexNet, ShuffleNet, and three multi-head self-attention mechanisms to extract and classify features from multilingual text and images. By utilizing two separate multi-head self-attention mechanisms to process extracted textual and image information, the model captures long-range dependencies among texts to better understand contextual information and captures global information among images to enhance object recognition and localization capabilities. Finally, a single multi-head self-attention mechanism is employed to integrate textual and image information to capture cross-modal correlations, thereby strengthening the model's understanding and identification capabilities of news content. This dual-stream fusion network architecture ensures the model's ability to detect multimodal news scenarios in both English and Chinese short texts and images. Experimental evaluations conducted on three publicly available datasets - Twitter, Weibo, and IKCEST-2023 dataset, which contains real online news data collected from domestic and international social media platforms - demonstrate superior performance of the proposed model in news detection tasks, achieving an accuracy rate

关键词： Network architecture

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Visible-Thermal Multiple Object Tracking: Large-scale Video dataset and Progressive Fusion Approach

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Qianwu Li, Chenglong Tang, Jin Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Public Safety and Emergency Management Anhui University of Science and Technology Hefei231131 China School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Center for Big Data and Population Health of IHM China

The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods. From the evaluation results and analysis, we specify several potential future directions for visible-thermal MOT. The project is released in https://***/wqw123wqw/PFTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xiuen Wu Tao Wang Yuanzheng Cai Lingyu Liang George Papageorgiou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians’ future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.

关键词： Deep learning Pedestrians Computational modeling Predictive models Prediction algorithms Vectors Inference algorithms

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall.

关键词： Training Runtime Computational modeling Superresolution Memory management Feature extraction Diffusion models Convolutional neural networks Image reconstruction Standards

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