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Video Stabilization via Prediction with Time-Series Network and Image Inpainting with Pyramid Fusion

Chinese Journal of Electronics 2021年第6期30卷 1103-1110页

作者： CHENG Keyang LI Shichao RONG Lan WANG Wenshan SHI Wenxi ZHAN Yongzhao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center Cyber Space Security Academy of Jiangsu University National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Xinjiang Lianhaichuangzhi Information Technology Co. Ltd.

Due to the poor filling effect of the video image defect commonly used in the video stabilization field, the video is seemed still unstable after the image stabilization process, which seriously affects the visual effect. To solve this problem, we improve a video stabilization method based on time-series network prediction and pyramid fusion restoration is proposed to optimize the visual effect after image *** flow of the proposed method is as follows: First,it is adaptive to determine whether the defect of the corresponding frame at the current time needs padding inpainting. Then, for the frame that needs to be inpainting,the frames generated before the current moment are sent to the model combining the convolutional neural networks and the gate recurrent unit to predict the part to be filled. Next the current defect image and the complete image to be filled are brought into the Laplacian pyramid reconstruction, and the improved weighted optimal suture is introduced for splicing during the fusion. Finally, the video frame is cut after reconstruction. The method is tested on a data set composed of videos commonly used in the field of video stabilization. The experimental results show that the average peak signal to noise ratio of the method is 2 to 5 d B higher than that of the comparison algorithm, and the average structural similarity index is improved by about 2% to 7% compared with the contrast algorithm.

关键词： image restoration image sequences video frame cutting improved weighted optimal suture gate recurrent unit video signal processing video stabilization method image fusion Laplacian pyramid reconstruction padding inpainting image resolution neural nets convolutional neural networks video image defect pyramid fusion restoration image inpainting visual effect optimization image reconstruction time-series network prediction image stabilization process

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A Dual-Branch Model with Inter- and Intra-branch Contrastive Loss for Long-tailed Recognition

arXiv

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arXiv 2023年

作者： Chen, Qiong Huang, Tianlin Zhu, Geren Lin, Enlu School of Computer Science and Engineering South China University of Technology China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China

Real-world data often exhibits a long-tailed distribution, in which head classes occupy most of the data, while tail classes only have very few samples. Models trained on long-tailed datasets have poor adaptability to tail classes and the decision boundaries are ambiguous. Therefore, in this paper, we propose a simple yet effective model, named Dual-Branch Long-Tailed Recognition (DB-LTR), which includes an imbalanced learning branch and a Contrastive Learning Branch (CoLB). The imbalanced learning branch, which consists of a shared backbone and a linear classifier, leverages common imbalanced learning approaches to tackle the data imbalance issue. In CoLB, we learn a prototype for each tail class, and calculate an inter-branch contrastive loss, an intra-branch contrastive loss and a metric loss. CoLB can improve the capability of the model in adapting to tail classes and assist the imbalanced learning branch to learn a well-represented feature space and discriminative decision boundary. Extensive experiments on three long-tailed benchmark datasets, i.e., CIFAR100-LT, ImageNet-LT and Places-LT, show that our DB-LTR is competitive and superior to the comparative methods. © 2023, CC BY-NC-SA.

关键词： Learning systems

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SteadySketch: Finding Steady Flows in Data Streams

SteadySketch: Finding Steady Flows in Data Streams

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International Workshop on Quality of Service

作者： Xiaodong Li Zhuochen Fan Haoyu Li Zheng Zhong Jiarui Guo Sheng Long Tong Yang Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application School of Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen China

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub.

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Utilizing the Fem Approach, PSO, and Firefly Algorithm, Surge Wire Grades Ring Modification was Achieved

Utilizing the Fem Approach, PSO, and Firefly Algorithm, Surg...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Amandeep Kaur Harveer Singh Department of Master of Computer Application Chandigarh School of Business Jhanjeri Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

ISBN: (纸本)9781665487351

As well as an arrester, semi power and electromagnetic current distributions have been seen, which impacts the dependability and lifespan of the device. With the HV load resistor, a rating ring is used to provide an even spread of the electric field. The Finite Element Technique (FEM), a tried-and-true approach, is used to assess the effects of sorting ring characteristics. It contributes to extending the useful life of surge arrester ZnO blocks under extreme stress. Optimizer (PSO) and BAT techniques are used for validation.

关键词： Resistors Arresters II-VI semiconductor materials Wires Zinc oxide Finite element analysis Surges

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Correction: Structural and shielding effectiveness properties of (NiFe2O4/chopped strands) composites for 6.5–18 GHz applications

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Journal of Materials science: Materials in Electronics 2024年第30期35卷 1-2页

作者： Sahin, Ethem İlhan Ibrahim, Jamal-Eldin F. M. Emek, Mehriban Sinsoysal, Bahaddin Amanzholova, Alina Advanced Technology Research and Application Center Adana Alparslan Türkes ̧ Science and Technology University Adana Turkey Doctoral School of Material Science and Technology Institute of Ceramic and Polymer Engineering Faculty of Material Science and Engineering University of Miskolc Miskolc Hungary Department of Physics Golbasi Vocational School Computer Technologies Adiyaman University Adıyaman Turkey Department of Mathematics and Computer Engineering Faculty of Engineering İstanbul Gedik University Istanbul Turkey Department of Mathematics Faculty of Science Khoja Akhmet Yassawi International Kazakh-Turkish University Turkistan Kazakhstan

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ML-GOOD: Towards Multi-Label Graph Out-Of-Distribution Detection 39

ML-GOOD: Towards Multi-Label Graph Out-Of-Distribution Detec...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cai, Tingyi Jiang, Yunliang Li, Ming Huang, Changqin Wang, Yi Huang, Qionghao School of Computer Science and Technology Zhejiang Normal University China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Information Engineering Huzhou University China Zhejiang Institute of Optoelectronics China

ISBN: (纸本)157735897X

The out-of-distribution (OOD) detection on graph-structured data is crucial for deploying graph neural networks securely in open-world scenarios. However, existing methods have overlooked the prevalent scenario of multi-label classification in real-world applications. In this work, we investigate the unexplored issue of OOD detection within multi-label node classification tasks. We propose ML-GOOD, a simple yet sufficient approach that utilizes an energy function to gauge the OOD score for each label. We further develop a strategy for amalgamating multiple label energies, allowing for the comprehensive utilization of label information to tackle the primary challenges encountered in multi-label scenarios. Extensive experimentation conducted on seven diverse sets of real-world multi-label graph datasets, encompassing cross-domain scenarios. The results show that the AUROC of ML-GOOD is improved by 5.26% in intra-domain and 6.54% in cross-domain compared to the previous methods. These empirical validations not only affirm the robustness of our methodology but also illuminate new avenues for further exploration within this burgeoning field of research. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization

arXiv

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arXiv 2023年

作者： Cao, Congqi Wang, Yizhe Lu, Yue Zhang, Xin Zhang, Yanning The National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710129 China

Temporal action localization (TAL) is a prevailing task due to its great application potential. Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling. (2) They ignore the semantic information in class labels and only use the visual information. To solve these problems, we propose a novel Co-Occurrence Relation Module (CORM) that explicitly models the co-occurrence relationship between actions. Besides the visual information, it further utilizes the semantic embeddings of class labels to model the co-occurrence relationship. The CORM works in a plug-and-play manner and can be easily incorporated with the existing sequence models. By considering both visual and semantic co-occurrence, our method achieves high multi-label relationship modeling capacity. Meanwhile, existing datasets in TAL always focus on low-semantic atomic actions. Thus we construct a challenging multi-label dataset UCF-Crime-TAL that focuses on high-semantic actions by annotating the UCF-Crime dataset at frame level and considering the semantic overlap of different events. Extensive experiments on two commonly used TAL datasets, i.e., MultiTHUMOS and TSU, and our newly proposed UCF-Crime-TAL demenstrate the effectiveness of the proposed CORM, which achieves state-of-the-art performance on these datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

arXiv

引用

arXiv 2023年

作者： Yang, Yong Chen, Qiong Feng, Yuan Huang, Tianlin School of Computer Science and Engineering South China University of Technology China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet. © 2023, CC BY-NC-SA.

关键词： Embeddings

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IoT based Truck Wheel Temperature Monitoring System

IoT based Truck Wheel Temperature Monitoring System

引用

Intelligent Technologies (ACOIT), Asian Conference on

作者： Mohd Zaid Nadeem Ahmad Mohammed Aljamal Sarosh Patel Asif Khan dept. of Computer Science and Engineering Integral University Lucknow India dept. of Electronics and communication Engg Goel Institute of Technology And Managment Lucknow India School of Engineering University of Bridgeport USA dept. of Computer Application Integral Robotics Lab Integral University Lucknow India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The main objective of this paper is to design an IoT based truck wheel temperature monitoring system using Arduino board with the aim to overcome endanger and raise the safety measures for truck and goods. By designing and implementing a temperature-detecting system, we can detect the temperature of the wheels trucks. By the use of such detecting device we can prevent the major problem associated with wheel’s temperature and may avoid the major accidents. This monitoring system senses the temperature data of the wheel and transmits through the Wi-Fi module. In addition, it upgrades Thingspeak channel that can collect the data and it can easily be accessed by the transport agency and the truck driver. This can further be analysed using any computer system or mobile with internet connectivity anywhere across the globe to take real-time decision. The core function of the proposed detecting system is to monitor the real- time value of truck wheel temperature and to inform the concerned people.

关键词： Temperature measurement Temperature sensors Temperature distribution Vehicle safety Wheels Weather forecasting Transportation Real-time systems Monitoring Wireless fidelity

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Using Classifier with Gated Recurrent Unit-Sigmoid Perceptron, Order to Get the Right Bird Species Detection

Using Classifier with Gated Recurrent Unit-Sigmoid Perceptro...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rainy Sikand Harveer Singh Department of Master of Computer Application Chandigarh School of Business Jhanieri Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

ISBN: (纸本)9781665487351

The method for classifying a picture using the CNN algorithm and various activation functions is the subject of this research study. From a human perspective, there are several sorts of situations found in birds, such as various sizes, altitudes, colours, and forms. There is a more substantial variety in photos to recognize the species of bird than there is in the accepted audio categorization of birds. Additionally, people have a really tough time identifying birds from photos. The system is suggested to support the concepts of identifying a portion of the birds and extracting the CNN characteristics using several convolutional layers in order to discriminate and categorise the birds. These characteristics are then gathered and presented to the decoder at that time for classification. Various bird photos and activation functions are used to test the CNN method. The nodes of the picture are checked to the different testing data sets, such as cifar-10 and Berkeley Birds 200, with order to determine their correctness. Python is used to create the suggested system in either Omnibus 3 or Dragonfly 3 programs. According to the experiment results and analyses, CNN with activated Re LU-Sigmoid outperforms CNN with the other activation functions in terms of accuracy.

关键词： Logic gates Birds Feature extraction Market research Classification algorithms Convolutional neural networks Kernel

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