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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Wei, Yanxia Xiao, Hairong Feng, Deying Mu, Jian Yang, Jie School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Liaocheng University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781450396110

Recently, Discriminative Correlation Filter based trackers have increasingly become popular in the domain of visual object tracking, which is benefited by their effective and robustness in terms of tracking performance. However, the significant variations of target appearance, such as Occlusion or Deformation, always suppress the performance improvement. Although the multi-feature fusion strategy partly alleviates the above factors, the synthetic scheme also ambiguates the overall presentation ability of multiple features and results in degradation of discriminative power during classification filter learning. In this paper, we present a novel tracking framework based on the correlation filter with multiple kernel learning. Instead of directly integrating different features, we decompose a tracking process into several module, including low-dimension modules and high-dimension modules. The handcraft features extracted from low-dimension modules are robust to the small variation of target while the semantic convolutional features from the high-dimension modules can distinguish the target object from the relative complex tracking environment. In addition, we also design a reliability detection mechanism based on the confidence map to accurately measure the important degree of each kernel learning and precisely estimate the target state in the subsequent frames. For verifying our proposed tracker, we conduct extensive experiments on several standard sequence benchmark. Experiment results show that our proposed trackers perform favorably against other state-of-the-art trackers. © 2022 ACM.

关键词： Semantics

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Attn-VAE-GAN:Text-Driven High-Fidelity image Generation Model with Deep Fusion of Self-Attention and Variational Autoencoder

Attn-VAE-GAN:Text-Driven High-Fidelity Image Generation Mode...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chen Yang Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University School of Computer Science and Engineering Chongqing Three Gorges University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the current realm of research on text-to-image (T2I) transformation, the daunting task of translating natural language descriptions into visually realistic images is apparent. This intricate undertaking requires models to deeply comprehend cross-modal information and achieve precise semantic mapping. Despite the commendable progress made by GANs in image synthesis—crafting high-resolution, lifelike visual effects from random noise—persistent challenges like training instability, mode collapse, and fidelity issues persist, prompting the need for innovative *** study introduces an avant-garde text-driven high-fidelity image generation model, labeled as Attn-VAE-GAN. The initial integration of a generator module featuring a fused Self-Attention mechanism empowers the model to thoroughly grasp and accurately capture intricate semantic information from the input text. Moreover, the assimilation of a Variational Autoencoder (VAE) module taps into its latent representation learning prowess to optimize both image quality and diversity. To amplify the realism and detail expression of the generated images, a meticulously designed comprehensive loss function combines intrinsic VAE loss and hinge *** results underscore the substantial enhancement achieved by the Attn-VAE model in both the quality and diversity of the generated images across diverse publicly available benchmark datasets.

关键词： Training Representation learning Measurement Technological innovation image synthesis Semantics Neural networks

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Antifungal activity of curcumin-mediated photodynamic inactivation against Fusarium graminearum on maize

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Grain & Oil Science and Technology 2025年第1期8卷 21-31页

作者： Shuliang Dong Lu Chen Shaojie Li Konglong Feng Guang Liu Hao Dong Guizhi Xu Huajian Ou Yang Liu Yong Zhao Jingjing Wang Guangdong Provincial Key Laboratory of Intelligent Food Manufacturing Foshan UniversityFoshan 528225China College of Food Science and Technology Shanghai Ocean UniversityShanghai 201306China Shanghai Sixth People’s Hospital Affiliated to Shanghai JiaoTong University Shanghai 201306China Sericultural&Agri-Food Research Institute Guangdong Academy of Agricultural Sciences/Key Laboratory of Functional Foods Ministry of Agriculture and Rural Affairs/Guangdong Key Laboratory of Agricultural Products ProcessingGuangzhou 510610China College of Light Industry and Food Sciences Zhongkai University of Agriculture and EngineeringGuangzhou 510225China National Technical Center(Foshan)for Quality Control of Famous and Special Agricultural Products Foshan 528225China

The natural curcumin-mediated photodynamic inactivation(PDI)was developed,and its inactivation potency against Fusarium graminearum in vitro and in vivo was systematically investigated by fluorescence probe assay,trypan blue staining,scanning electron microscope(SEM),confocal laser scanning microscopy(CLSM),*** showed that under the irradiation of blue LED,the photosensitizer of curcumin was excited to generate massive reactive oxygen species(ROS)in the cells of ***,and the PDI completely inactivated their mycelia and spores under the treatment of 150μM curcumin and 10.8 J/cm^(2)*** analysis found that the PDI ruptured the cellular microstructures,damaged the cell membrane by increasing its permeability and oxidizing the lipids,degraded the intracellular DNA and proteins inside the spores of ***,the PDI also potently killed>99.99%spores of *** on maize under the treatment of 200μM curcumin and 10.8 J/cm^(2)***,the PDI suppressed the production of zearalenone(ZEN),and residual ZEN could not be detected after the storage of maize for 10 ***,this study systematically explored the inactivation efficiency of curcumin-mediated PDI against both the mycelia and spores of ***,which provides a valid and promising method to control the fungal hazards in grains.

关键词： Fusarium graminearum Photodynamic inactivation(PDI) Spores Mycelia Zearalenone(ZEN)

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Edible prodelphinidins that improve postprandial hyperglycemia were discovered in Chinese bayberry(Myrica rubra *** Zucc.)fruits

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Food Science and Human Wellness 2025年第3期14卷 1008-1016页

作者： Yi Wang Laiming Zhang Jiaxiong Wu Chengxiao Yu Xingqian Ye Haibo Pan Shiguo Chen College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and EquipmentZhejiang Key Laboratory for Agro-food ProcessingIntegrated Research Base of Southern Fruit and Vegetable Preservation TechnologyZhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality ControlZhejiang UniversityHangzhou 310058China Ningbo Research Institute Zhejiang UniversityNingbo 315100China Zhejiang University Zhongyuan Institute Zhengzhou 450000China Innovation Center of Yangtze River Delta Zhejiang UniversityJiaxing 314102China

Plant polyphenols are potential substitutes for clinicalα-glucosidase *** previous studies indicated that prodelphinidins from Chinese bayberry leaves(BLPs)have strongerα-glucosidase inhibitory activity than other common polyphenols,but they have no safe history of *** is a reasonable prospect that prodelphinidins from Chinese bayberry fruits(BFPs)can improve postprandial hyperglycemia,though known active components are only myricetin and ***,the aim of this study was to analyze structure and hypoglycemic effect of BFPs of“Biqi”(BQPs)and“Dongkui”(DKPs)cultivars,and compare their difference with *** contents of BQPs and DKPs were(221.73±0.60)and(136.82±4.33)mg epicatechin(EC)equivalents/g dry weight,*** most subunits were epigallocatechin gallate along with a small amount of epigallocatechin,epicatechin gallate and EC connected by B-type linkages and a small portion of A-type linkages with mean polymerization degree of 4.25 and 4.08,***,BQPs and DKPs wereα-glucosidase inhibitors with half inhibitory concentration of 11.91 and 9.47μg/m L respectively,which were significantly stronger than *** could also improve postprandial hyperglycemia of normal mice and high fat diet-induced type II diabetic ***,edible prodelphinidins,which have stronger hypoglycemic effect than BLPs,were first found in Chinese bayberry fruits.

关键词： Chinese bayberry fruit Prodelphinidins Structure α-Glucosidase Postprandial hyperglycemia

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Insulator Defect Detection Based on YOLOv4-tiny with Improved Feature Fusion 12

Insulator Defect Detection Based on YOLOv4-tiny with Improve...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Zhong, Yuning Hu, Rong Li, Zuoyong Cai, Yuanzheng Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Insulator exposed to a wild environment for long-term running usually suffers from the damage of natural calamities that makes them become one of the most fragile components in the whole power system. In this case, even a slight fault will seriously affect the power supply safety of the power grid. Therefore, it is critical to find and identify the faulty insulator efficiently and accurately. However, an insulator is quite a small component in comparison with a huge power system which makes it hard to visible with naked eyes. This characteristic leads to poor recognition accuracy for existing detection methods. In order to solve the above problems, we propose an insulator defect detection algorithm based on an improved YOLOv4-tiny model. It differs from existing insulator detection models, our algorithm applies the effective channel attention network to the feature extraction network of YOLOv4-tiny, which significantly enhances the feature extraction ability of the backbone network. These two-way features of the pyramid can be fully fused in the two-way feature fusion stages seamlessly. Experimental results show that compared with, our proposed algorithm outperforms Faster RCNN and YOLOv3 in average classification accuracy, missed detection, and false detection. © 2022 IEEE.

关键词： Feature extraction

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image Denoising Using Bandelets and Hidden Markov Tree Models

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Chinese Journal of Electronics 2023年第4期19卷 646-650页

作者： Wenge Zhang Suang Wang Fang Liu Xinbo Gao Licheng Jiao School of Computer Science and Technology Xidian University Xi'an China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, both the marginal and joint statistics of second generation Orthogonal bandelet transform (OBT) coefficients of natural images are firstly studied, and the highly non-Gaussian marginal statistics and strong interscale, interlocation and interdirection dependencies among OBT coefficients are found. Then a Hidden Markov tree (HMT) model in OBT domain which can effectively capture all dependencies across scales, locations and directions is developed. The main contribution of this paper is that it exploits the edge direction information of OBT coefficients, and proposes an image denoising algorithm (B-HMT) based on HMT model in OBT domain. We apply B-HMT to denoise natural images which contaminated by additive Gaussian white noise, and experimental results show that B-HMT outperforms the Wavelet HMT (W-HMT) and Contourlet HMT (C-HMT) in terms of visual effect and objective evaluation criteria.

关键词： Geometry Analytical models Additives Hidden Markov models Transforms White noise Visual effects

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Spatial-Temporal-Rule-based Traffic Movement Status Interpretation at Intersections *

Spatial-Temporal-Rule-based Traffic Movement Status Interpre...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Zhuang, Hanyang Gao, Liyang Qian, Yeqiang Wang, Chunxiang Yang, Ming University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350327182

Traffic light is the most significant component of road transportation. Traffic lights control the traffic movement at intersections by allowing the movement of some lanes and forbidding that of the others to ensure safety and efficiency. It is important for intelligent vehicles to understand the meaning of traffic lights. Conventional research works on traffic light detection and tracking consider each light independently, so the occlusion of some lights may mislead the decision of the vehicle. Since all traffic lights at an intersection work together, it is more essential to understand the traffic movement status controlled by the traffic lights rather than the perception of a single light. Therefore, this paper proposes a spatial-temporal-rule-based framework to interpret the current traffic movement status and predict the next status. The spatial information, including position and class, is obtained by traffic light detection. The temporal information is perceived by the proposed topologic-feature-based tracking method. The rule information is recognized by combination of spatial and temporal information. A dual-view camera system consisting of a telephoto camera and a wide-angle camera is adopted to ensure long perception distance and wide coverage simultaneously. The successful tracking of traffic lights from 200 m to 10 m away provides enough data to recognize the phase-change rule, which leads to correct interpretation and prediction. A customized dataset was collected, and experiments were conducted to illustrate the capability of the proposed method. © 2023 IEEE.

关键词： Cameras

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AoI-Reliability Analysis with Block Length over Erasure Channels Using Rateless Codes 24

AoI-Reliability Analysis with Block Length over Erasure Chan...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Huang, Haobo Fu, Yusun Qiao, Yue Yin, Junpeng Yan, Weiwu Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350363760

As a metric to measure the information freshness, Age of Information (AoI) is widely used to evaluate low-latency high-reliability communication. Since the communication in Industrial Internet of Things (IIoT) often has the special requirements, the relationship among AoI, reliability and block length in time-critical scenarios with bounded latency and non-time-critical scenarios without bounded latency is explored respectively in this paper, based on erasure channels using rateless codes. We build a model using LT codes over erasure channels and consider two metrics: average AoI and bounded AoI. Theoretical analysis and simulation results show that AoI has a minimum value with different block lengths. Furthermore, we draw a conclusion that the minimum value of AoI under different reliability is monotonically decreasing functioned with reliability. Finally, the impact of overhead on reliability and AoI of LT codes in time-critical scenarios is studied. © 2024 IEEE.

关键词： Turbo codes

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical Information 17

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

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17th International Congress on image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Xu, Wentong Fu, Chao Yu, Jing Si, Caifeng Li, Zuoyong Li, Chaochao Fan, Haoyi School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China First Affiliated Hospital of Zhengzhou University Department of Ultrasound Henan Zhengzhou450052 China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798331507398

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. © 2024 IEEE.

关键词： Self-supervised learning

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GraphMFT: A graph network based multimodal fusion technique for emotion recognition in conversation

arXiv

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arXiv 2022年

作者： Li, Jiang Wang, Xiaoping Lv, Guoqing Zeng, Zhigang Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Wuhan430074 China

Multimodal machine learning is an emerging area of research, which has received a great deal of scholarly attention in recent years. Up to now, there are few studies on multimodal Emotion Recognition in Conversation (ERC). Since Graph Neural Networks (GNNs) possess the powerful capacity of relational modeling, they have an inherent advantage in the field of multimodal learning. GNNs leverage the graph constructed from multimodal data to perform intra- and inter-modal information interaction, which effectively facilitates the integration and complementation of multimodal data. In this work, we propose a novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion recognition in conversation. Multimodal data can be modeled as a graph, where each data object is regarded as a node, and both intra- and inter-modal dependencies existing between data objects can be regarded as edges. GraphMFT utilizes multiple improved graph attention networks to capture intra-modal contextual information and inter-modal complementary information. In addition, the proposed GraphMFT attempts to address the challenges of existing graph-based multimodal conversational emotion recognition models such as MMGCN. Empirical results on two public multimodal datasets reveal that our model outperforms the State-Of-The-Art (SOTA) approaches with the accuracy of 67.90% and 61.30%. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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