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Semantic Enhanced Few-Shot Object Detection

Semantic Enhanced Few-Shot Object Detection

引用

ieee International conference on image processing

作者： Zheng Wang Yingjie Gao Qingjie Liu Yunhong Wang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hangzhou Innovation Institute Beihang University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Few-shot object detection (FSOD), which aims to detect novel objects with limited annotated instances, has made significant progress in recent years. However, existing methods still suffer from biased representations, especially for novel classes in extremely low-shot scenarios. During fine-tuning, a novel class may exploit knowledge from similar base classes to construct its own feature distribution, leading to classification confusion and performance degradation. To address these challenges, we propose a fine-tuning based FSOD framework that utilizes semantic embeddings for better detection. In our proposed method, we align the visual features with class name embeddings and replace the linear classifier with our semantic similarity classifier. Our method trains each region proposal to converge to the corresponding class embedding. Furthermore, we introduce a multimodal feature fusion to augment the vision-language communication, enabling a novel class to draw support explicitly from well-trained similar base classes. To prevent class confusion, we propose a semantic-aware max-margin loss, which adaptively applies a margin beyond similar classes. As a result, our method allows each novel class to construct a compact feature space without being confused with similar base classes. Extensive experiments on Pascal VOC and MS COCO demonstrate the superiority of our method.

关键词： Degradation visualization image processing Semantics Object detection Proposals Information exchange

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Enhancing Performance and Reducing Injury Risk: Analyzing Basketball Players' Jumping Landing Form through image processing and Support Vector Machine Algorithm

Enhancing Performance and Reducing Injury Risk: Analyzing Ba...

引用

Electronic communications, Internet of Things and Big Data (ICEIB), International conference on

作者： Royce D. Bumagat Sebastian Paul O. Uy Jessie Jaye R. Balbin School of Electronics Electrical and Computer Engineering Mapua University Manila Philippines Manila Philippines

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Basketball involves frequent direction changes, quick acceleration, and big jumps, which increase the risk of landing-related injuries. Ankle sprains and knee injuries, such as ACL tears, are common among basketball players. Thus, we developed an analytical system to assess a basketball player's jump-landing performance with reduced risks of injuries. The system adopted an image processing algorithm, specifically Support Vector Machine and image processing techniques, to evaluate jumping landing techniques. The system was implemented using HTML, CSS, PHP, and JavaScript, providing real-time video feedback and evaluation reports. The accuracy of the system was validated through the expertise of a basketball coach. Data was collected using a Kinect sensor and a laptop webcam, and specialized software processed the data to extract relevant features. The system's performance was compared with assessments by coaches, and the collected data and analysis results were stored. The developed system accurately identified ideal jump landing techniques by assessing joint angles and key components of the landing. The analysis result showed strong correlations between evaluations and reference postures. The system can be used to reduce the risk of injuries and improve player performance in basketball. The study's findings provide promising implications for player safety and performance enhancement.

关键词： Support vector machines Webcams System performance Streaming media Feature extraction Vectors Software

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WITT: A Wireless image Transmission Transformer for Semantic communications

WITT: A Wireless Image Transmission Transformer for Semantic...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ke Yang Sixian Wang Jincheng Dai Kailin Tan Kai Niu Ping Zhang Beijing University of Posts and Telecommunications Beijing China Peng Cheng Laboratory Shenzhen China

In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are inefficient in capturing global dependencies, resulting in degraded end-to-end transmission performance especially for high-resolution images. To tackle this, the proposed WITT employs Swin Transformers as a more capable backbone to extract long-range information. Different from ViTs in image classification tasks, WITT is highly optimized for image transmission while considering the effect of the wireless channel. Specifically, we propose a spatial modulation module to scale the latent representations according to channel state information, which enhances the ability of a single model to deal with various channel conditions. As a result, extensive experiments verify that our WITT attains better performance for different image resolutions, distortion metrics, and channel conditions. The code is available at https://***/KeYang8/WITT.

关键词： Wireless communication Measurement image resolution Speech coding image communication Semantics Modulation

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A Robust Log Parsing Algorithm—Practice of Logslaw in Heterogeneous Logs of Pacific Credit Card Center of Bank of communications(PCCC)

A Robust Log Parsing Algorithm—Practice of Logslaw in Heter...

引用

image processing and Computer Applications (ICIPCA), ieee International conference on

作者： Peng Chen Guoyong Chao Li Yang Hua He Lei Hong Mei Li Dongdong Gao Sichen Guo Pacific Credit Card Center of Bank of Communications Shanghai China

Log is a semi-structured text that records the important behavior of the system operation. Through log analysis, we can understand the system behavior, detect system anomaly, locate system fault and even find the root cause of the fault after a fault is found. Generally, log parsing is the first and crucial step of log analysis. In recent years, a great deal of work has been devoted to log parsing. However, most of the work is based on some strong assumptions. These works can achieve good results in some scenarios, but not in others (e.g., the business log of the bank). What's more, their parsing effect is highly dependent on tokenization. In short, these algorithms are weak in robustness, so our paper proposed a robust log parsing algorithm - LogSlaw. LogSlaw is an online log parsing algorithm clustering logs based on the improved Jaccard Similarity, which can perform log parsing on heterogeneous logs with a large amount of data. To improve the accuracy and efficiency of clustering, we proposed to utilize the pseudo log template and the length difference to early grouping and early abandonment. Our algorithm has high parsing efficiency and effectiveness. It can achieve more than four times the efficiency of existing algorithms and get the best results in most datasets. LogSlaw also has strong robustness. It can achieve good results with a very simple tokenization strategy no matter how complex the scene is. We conducted experiments on both the public datasets and the business log datasets from Pacific Credit Card Center of Bank of communications (PCCC), and achieved SOTA results on both datasets.

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A Study of Predictive Analysis of Community Transportation Demand Based on Gravity Model and Computer Simulation

A Study of Predictive Analysis of Community Transportation D...

引用

image processing and Computer Applications (ICIPCA), ieee International conference on

作者： Xuanzeng Yi Weiyu Tang College of Transportation & Communications Shanghai Maritime University Shanghai China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

As urbanization progresses around the world, it is crucial to mitigate and avoid traffic congestion problems in city roads. This necessitates making reasonable predictions on traffic distribution and conducting in-depth research with appropriate forecasting methods to assess the rationality of the road network. In this study, a community was taken as an example to calculate the total travel demand and the total traffic production and attraction of the community based on road network and land use planning. Secondly, utilizing the gravity model and TransCAD software, the future origin-destination matrix of the community was computed. Finally, by combining the road network, origin-destination matrix, and impedance matrix, traffic flow was allocated to the road network to obtain the final road network traffic volume prediction results. Through analysis, it was found that the “high density, narrow road width” road network structure of the community could effectively accommodate traffic flow, but congestion might occur in certain local areas in the future, requiring improvements. The above findings validated the effectiveness and feasibility of using the gravity model for predicting traffic distribution, providing strong support for subsequent urban road planning and design.

关键词： visualization Roads Computational modeling Urban areas Transportation Production Predictive models Planning Impedance Gravity

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Hybrid Encryption Strategy Integrating Hyper-Chaotic Systems and Spatiotemporal Cryptography for Enhanced image Security in Cloud Computing

Hybrid Encryption Strategy Integrating Hyper-Chaotic Systems...

引用

ACS/ieee International conference on Computer Systems and Applications

作者： Ismehene Chaouch Anis Naanaa Sadok El Asmi COSIM Higher School of Communications of Tunis University of Carthage Tunisia RISC National Engineering School of Tunis University of Tunis El Manar Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Cloud Computing is fundamentally revolutionizing the use, storage, management and processing of data and IT applications, providing increased flexibility, improved accessibility and substantial cost savings for businesses and individuals. However, data security remains a major concern in this area due to lack of control and data vulnerability, raising concerns among cloud users. This article presents an image encryption system designed to enhance the security of data stored in the cloud. The proposed scheme relies on Hyper-Chaos and spatiotemporal based encryption to improve data security in Cloud Computing. The simulation results demonstrate that the resulting encrypted image is highly secure, its execution is faster and its implementation is easier compared to several state-oftheart encryption schemes.

关键词： Cloud computing Data privacy Costs Simulation Storage management Robustness Encryption Spatiotemporal phenomena Complexity theory Testing

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DFMA: Adaptive Dual Fusion for Multimodal Relation Extraction with Mutual Attention

DFMA: Adaptive Dual Fusion for Multimodal Relation Extractio...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yunhao Li Tianxi Huang Guiduo Duan Yunrui Dong Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China College of Humanities and General Education Chengdu Textile College Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal relation extraction (MRE) is an emerging research field that combines techniques from natural language processing, computer vision, and machine learning, helping us better understand and interpret data. However, current methods are faced with two main issues. The first issue is that the auxiliary images may focus on irrelevant information, leading the model to place too much attention on unrelated entities when extracting visual features from the global image. The second issue is modality imbalance during the multimodal fusion stage, where the encoder might overly focus on one modality’s information over the other. Therefore, we propose a MRE method using Dual Fusion with Mutual Attention (DFMA). To address the first issue, we introduce the Pyramid Feature Extraction (PFE) module. To tackle the second issue, we propose the Multimodal Mutual Fusion (MMF) module. PFE dynamically generates prompt vectors to mitigate the impact of irrelevant information, while MMF, designed with mutual attention, can balance the fusion of textual and visual information simultaneously. Experiments show that our approach achieves the state-of-the-art performances on the multimodal neural relation extraction (MNRE) dataset.

关键词： visualization Analytical models Reviews Predictive models Signal processing Feature extraction Vectors Natural language processing Data mining Speech processing

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Focal Modulation Based End-to-End Multi-Label Classification for Chest X-Ray image Classification

Focal Modulation Based End-to-End Multi-Label Classification...

引用

ieee Signal processing and communications Applications (SIU)

作者： Şaban Öztürk Tolga Çukur Elektrik ve Elektronik Mühendisligi Bölümü Bilkent Üniversitesi Ankara Türkiye Elektrik ve Elektronik Mühendisligi Bölümü Amasya Üniversitesi Amasya Türkiye Ulusal Manyetik Rezonans Araştırma Merkezi (UMRAM) Bilkent Üniversitesi Ankara Türkiye

Chest X-ray imaging is of critical importance in order to effectively diagnose chest diseases, which are increasing today due to various environmental and hereditary factors. Although chest X-ray is the most commonly used device for detecting pathological abnormalities, it can be quite challenging for specialists due to misleading locations and sizes of pathological abnormalities, visual similarities, and complex backgrounds. Traditional deep learning (DL) architectures fall short due to relatively small areas of pathological abnormalities and similarities between diseased and healthy areas. In addition, DL structures with standard classification approaches are not ideal for dealing with problems involving multiple diseases. In order to overcome the aforementioned problems, firstly, background-independent feature maps were created using a conventional convolutional neural network (CNN). Then, the relationships between objects in the feature maps are made suitable for multi-label classification tasks using the focal modulation network (FMA), an innovative attention module that is more effective than the self-attention approach. Experiments using a Chest x-ray dataset containing both single and multiple labels for a total of 14 different diseases show that the proposed approach can provide superior performance for multi-label datasets.

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An Interference Detection Method for Satellite communications based on Maximum Between-Class Variance of Spectral Histogram

An Interference Detection Method for Satellite Communication...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Tao Guan Kaili Cui Lin Yang Yufeng Zhang Peng Wei Xin Ma The Sixty-Third Research Institute National University of Defense Technology Nanjing China Nanjing University of Information Science & Technology Nanjing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Interference detection is one of the critical prerequisites for avoiding and suppressing interference in satellite communication applications. In this article, we propose to treat the frequency-domain interference detection as a threshold segmentation problem and develop an interference detection method from a novel perspective. First, to deeply analyze the characteristic differences between interference and background noise from a statistical perspective, a Spectral Histogram of mixed signals is introduced. Then, a frequency-domain preprocessing method, which combines a morphological filter and a mean filter, is designed to reduce the impact of background noise. Inspired by the Otsu adaptive thresholding method in the field of image processing, an adaptive interference detection method based on a Maximum Between-Class Variance (MBCV) criterion is finally proposed to detect the interference in a wideband spectrum environment. Simulation experiments have been conducted on interference signals with different types and parameters, and the detection results validate the effectiveness of the proposed method.

关键词： Histograms Thresholding (Imaging) Satellites Frequency-domain analysis Interference Signal processing Background noise Satellite communications Wideband

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Optimizing Diabetic Retinopathy Detection with Advanced SMOTE-based Data Augmentation

Optimizing Diabetic Retinopathy Detection with Advanced SMOT...

引用

ieee International conference on Signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Minnu C Tomy C Malathy Department of Networking and Communications SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Diabetic retinopathy (DR) is a prevalent condition associated with prolonged diabetes, which impacts the ocular functions of individuals and has the potential to result in irreversible visual impairment. The early detection of DR is most vital for effective and better treatment, as the symptoms often manifest in later stages. Ophthalmologists use fundus images of the retina as part of the manual diagnostic process for diabetic retinopathy (DR). Most of the existing models utilized publicly accessible datasets for their research. Publicly available datasets exhibit a notable disparity in the distribution of data. The presence of data imbalance can significantly hinder the performance of classifiers, particularly when the minority class holds significant importance. This challenge becomes even more arduous when the dataset consists of more than two target classes. The primary aim of the proposed model is to create an automated model for classifying exudates for diabetic retinopathy using the Random Forest (RF) algorithm using three oversampling methods, such as ROS (Random Over Sampling), SMOTE (Synthetic Minority Oversampling Technique) and ADASYN (Adaptive Synthetic Sampling Approach). The model was trained and evaluated using the publicly available DR datasets DIARETDB0 and DIARETDB1. The findings of this study demonstrate that SMOTERF has superior performance compared to other data-level techniques. Along with this, the study proposed a new and advanced SMOTE-based data augmentation technique for more accurate DR classification results.

关键词： Radio frequency Deep learning Diabetic retinopathy Adaptation models Accuracy visual impairment Signal processing Data augmentation SPICE Blood pressure

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