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检索条件"任意字段=2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025"

共 580 条记录，以下是401-410 订阅

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Local Feature Alignment Prompt-Tuning for Few-shot Multimodal Aspect Sentiment Analysis

Local Feature Alignment Prompt-Tuning for Few-shot Multimoda...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Meirong Ding Chuang Zou Hongyi Lin School of Artificial Intelligence South China Normal University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-modal aspect-oriented sentiment classification (MASC) is a fine-grain task, which aims to detect the sentiment polarity of specific aspect. However, conventional studies suffer from two issues. It is difficult to collect the annotated multi-modal data in fine-grained domains. Meanwhile, the local information corresponding to aspect words in the image has not been mined, and redundant information can affect the accuracy of fine-grained sentiment analysis. To alleviate the above two issues, we propose a Prompt-tuning method based on Alignment between Aspect and Local Images (PAALI). Our approach introduces a novel multi-modal prompt template to bridge the gap between text and visual data modalities. Furthermore, we employ a strategy of randomly masking image patches to align them with aspect word features, capturing deeper semantic information. Extensive experiments on multiple benchmark datasets in few-shot settings consistently demonstrate the superiority and robustness of our PAALI method over state-of-the-art competitors.

关键词： Sentiment analysis visualization Analytical models Semantics Signal processing Benchmark testing Feature extraction Robustness data mining Speech processing

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BirdClassifier: An Advanced Bird Classification Model Using Deep Neural Networks

BirdClassifier: An Advanced Bird Classification Model Using ...

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international conference on Inventive Computation Technologies (ICICT)

作者： Deepali Jadhav Saket Patayeet Prachi Salunkhe Sameera Pujari Mrudul Sakharkar Siddhika Zanje Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Automated bird species classification plays a crucial role in biodiversity conservation and ecological monitoring. This study proposes a hybrid deep learning approach that integrates both acoustic and visual data to enhance classification accuracy. Bird calls are processed into Mel-spectrogram representations and classified using a Convolutional Neural Network (CNN) based on EfficientNetV2BO. Simultaneously, a CNN model is employed for species identification from image data, leveraging data augmentation techniques to improve generalization. The audio classification model benefits from transfer learning to refine feature extraction. Experimental results demonstrate that the multimodal fusion of audio and image data significantly outperforms single-modal approaches, achieving superior classification performance. The proposed system provides a scalable and robust framework for automated bird identification, with promising applications in wildlife monitoring, ecological assessments, and conservation efforts.

关键词： visualization Biological system modeling Computational modeling Wildlife Transfer learning Birds data augmentation data models Convolutional neural networks Monitoring

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Analyzing Impact of data Augmentation Techniques in Computer Vision

Analyzing Impact of Data Augmentation Techniques in Computer...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Aayushi Agarwal Anurakta Dash Ganesh Galbale Simar Preet Singh Bennett University Greater Noida

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

data Augmentation is a prevalent practice within computer vision, which uses transformations like random flipping, rotation, jittered colors and advanced techniques like Mixup and CutMix to artificially increase a training dataset. This, in turn, seeks to give generalization skills, minimize overfitting, and amplify the diversity of the data. In this paper, we explore the effectiveness of different data augmentation techniques for computer vision applications significantly concerning their effects on image classification tasks using convolutional neural networks (CNNs). The study bases its analysis on Residual Network-18 (ResNet-18), visual Geometry Group-16 (VGG-16), visual Geometry Group-19 (VGG-19), Densely Connected Networks-121 (DenseNet-121), and a Modified CNN. These were all trained and evaluated on the Canadian Institute for Advanced Research –10 (CIFAR-10) dataset. According to the results, the modified CNN model consistently attains the highest accuracy of the different tried models. Although its performance record of 90.9% with training augmented and 91.14% without augmentation is better than others, the results show an outstanding generalization capability of the model in different environments indicating resilience and adaptability of the model across an even wider variety.

关键词： Training Geometry Adaptation models Computer vision visualization Computational modeling data augmentation data models Convolutional neural networks Image classification

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Enhancing Landmark Detection Models Through Multimodal Fusion of visual data and Image Captioning

Enhancing Landmark Detection Models Through Multimodal Fusio...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Sura Raya Imad Afyoni Zaher Al Aghbari Department of Computer Science University of Sharjah Sharjah UAE

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Detecting specific landmarks in images remains a challenging task, especially when existing traditional vision-based detection models struggle to identify the landmarks accurately. In tourism, effective landmark detection is crucial for aiding navigation and enriching cultural exploration by assisting travelers in discovering points of interest, planning trip routes, and accessing detailed historical and cultural insights. This paper introduces a multimodal approach that enhances landmark detection by combining visual and textual features extracted from images. The model utilizes a deep learning-based feature extraction technique to generate the image embedding and employs an automatic image captioning technique to extract text embeddings. These embeddings are then concatenated into a unified multimodal representation vector for each image-caption pair. By fusing visual data with corresponding captions, the proposed method improves both the accuracy and robustness of landmark identification. The proposed method has practical benefits in tourism since it supports real-time use cases as tourist route planning and culture discovery. The model is trained on a custom-built Dubai landmarks dataset and tested in live image streams from Instagram, demonstrating its potential for real-world deployment. The proposed multimodal model outperforms the visual-based model by achieving an accuracy of 91.95%.

关键词： visualization Technological innovation Accuracy Feature extraction data models Vectors Planning Cultural differences Web sites Streams

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Differential Analysis and Prediction Based on Independent T-Test and Linear Regression-Stacking Ensemble Algorithm

Differential Analysis and Prediction Based on Independent T-...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE international conference on

作者： Xiangtong Li Yingying Huang School of Economic and Management Tianjin University of Science and Technology Tianjin China College of Food Engineering and Biotechnology Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper presents a comprehensive analysis of the differences between experimental and theoretically calculated yield values, employing the Independent T-test to assess statistical significance. By examining the resulting p-values, we determine whether significant discrepancies exist between experimental and theoretical yields. visual representations of these comparisons are also provided to facilitate subgroup analysis and pinpoint specific mixing ratios where differences are most evident. Furthermore, we develop a predictive model for product yields using a hybrid approach that combines linear regression with a stacking ensemble algorithm. This integrated model, which leverages the strengths of both methodologies through weighted fusion, demonstrates improved predictive performance based on the experimental data.

关键词： visualization Accuracy Computational modeling Stacking Linear regression Predictive models Prediction algorithms data models Robustness Testing

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NAV: A Comparative Analysis Tool for Nsight Systems GPU Traces

NAV: A Comparative Analysis Tool for Nsight Systems GPU Trac...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Ethan Shama Ryan E. Grant Electrical and Computer Engineering Department Queen’s University Kingston Ontario Canada

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

High-performance computing (HPC) and data centers increasingly rely on Graphics Processing Units (GPUs) in large supercomputers. Yet, this reliance poses challenges in quickly understanding the performance impacts of code changes. This paper introduces NAV, a versatile tool designed to rapidly analyze and compare GPU performance traces. Built upon NVIDIA’s Nsight TM Systems (NSYS), NAV enhances NSYS’s capabilities by accelerating visualization for large traces, increasing the variety of data representation formats, and adding comparative analysis capabilities. Our tool, NAV, provides complimentary functions on top of NSYS to quickly access performance data and perform comparative analysis. NAV offers detailed visual and written representations of traces at various granularity levels and efficiently handles large trace files through parallelization. It extracts trace data 1.15 to 3.5 times faster than comparable NSYS recipes for typical developer trace sizes, automating the generation of valuable data representations and streamlining workload analysis. This paper outlines NAV’s key features and functionalities, demonstrating its effectiveness through use cases that highlight its benefits for rapid application analysis and assessing the impact of code changes.

关键词： Measurement visualization Codes Graphics processing units data visualization Feature extraction Supercomputers Performance analysis Regression analysis data mining

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Corporate Portal as a visual Information Analysis Tool for the Management Decision Support System

Corporate Portal as a Visual Information Analysis Tool for t...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Darya A. Skvortsova Boris D. Byshovets Evgenii N. Bocharnikov Katerina D. Rudenko Department of Management Bauman Moscow State Technic University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In the contemporary business landscape, there is an observable trend towards the extensive utilization corporate portals. This phenomenon has garnered attention due to its capacity to engender cost reductions and enhance organisational profitability. The utilisation of automation tools that employ business logic has been demonstrated to expedite the analysis of information, thereby ensuring its enhanced quality. The challenges associated with the management of corporate information portals through a security control system also examined. In the course of the work, the following management tasks of the security control system were solved as an example: the task of studying initial data, inventory of information assets, dynamics of vulnerability distribution, efficiency control, compliance with standards.

关键词： visualization data analysis Translation Profitability Standards organizations Information security Organizations Strategic planning Control systems Portals

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Dugdale-GAN: Physical Dugdale Model Integrated Generative Adversarial Network for High-quality Crack Image Generation

Dugdale-GAN: Physical Dugdale Model Integrated Generative Ad...

引用

Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Yiyun Sun Tus-Design Group Co. Ltd Suzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Vision-based crack detection systems face significant challenges in training data scarcity and imbalance. While Generative Adversarial Networks (GANs) offer potential solutions, existing methods often generate physically implausible patterns. We propose Dugdale-GAN, a novel framework that integrates the physical Dugdale model with GANs to generate realistic crack patterns. Our approach incorporates material-specific parameters and features a dual-generator and dual-discriminator architecture to ensure both physical consistency and visual quality. We also introduce the Diversity Quality Indicator (DQI), a comprehensive metric for evaluating both image quality and pattern diversity. Experimental results demonstrate that Dugdale-GAN outperforms existing methods in generating physically plausible crack patterns.

关键词： Image quality visualization Technological innovation Sensitivity Image synthesis Computational modeling Training data Computer architecture Generative adversarial networks Power electronics

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Multi-Prototype Grouping for Continual Learning in visual Question Answering

Multi-Prototype Grouping for Continual Learning in Visual Qu...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Licheng Zhang Zhendong Mao Yixing Peng Zheren Fu Yongdong Zhang School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

visual Question Answering (VQA) aims to answer questions utilizing information from both textual and visual modalities. New data categories and novel combinations of the two modalities will continuously emerge in practical applications, necessitating continual learning. For this unique compositional generalization challenge, existing work adopts prototype learning to separately capture sample-invariant features of question types and image objects, retrieving the most similar prototype from two modalities as generalizable representations. However, for semantic fuzzy samples that deviate from the corresponding prototype, relying on a single prototype may lead to error-prone representation learning. Additionally, for the catastrophic forgetting challenge, existing methods employ memory to store past samples but lack an effective selection of samples to be stored, merely updating the memory through random sampling. In this paper, we propose ProtoGroup, a multi-prototype grouping continual learning framework for VQA, which groups prototypes based on their similarity to obtain more accurate and stable sample-invariant features. We also devise a memory sample selection method based on the correlation with prototype groups from old tasks. Experimental results demonstrate our framework outperforms previous methods significantly across multiple datasets and settings.

关键词： Continuing education Representation learning visualization Accuracy Semantics Prototypes Signal processing data aggregation Question answering (information retrieval) Speech processing

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Study and Efficiency Analysis of data Frames Unification Process for a Pair of Wireless Sensor Networks Protocols ZigBee and WirelessHART

Study and Efficiency Analysis of Data Frames Unification Pro...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Farah J. Yousef Department of Information Systems Moscow State University of Technology “STANKIN” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents an efficiency and performance analysis of the data frame unification process for two wireless sensor network (WSN) protocols, ZigBee and WirelessHART, at the data link layer. A software emulator was developed to enable the unification and interoperability of multiple data frames for both standards, based on a proposed theoretical solution to address interoperability issues in WSN standards. The emulator was implemented using Python within the visual Studio Code environment. Key performance indicators, such as frame loss rate and total frame processing time, were evaluated under various scenarios and parameters. The results demonstrate that increasing the packet intensity and the number of nodes leads to a proportional increase in both frame processing time and frame loss rate. These findings provide valuable insights into the scalability and efficiency of the proposed solution for WSN interoperability.

关键词： Wireless sensor networks visualization Protocols Codes Scalability Zigbee Software Interoperability Standards Python

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