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

共 570 条记录，以下是431-440 订阅

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A CLIP-based Video Question Answering framework with Explainable AI

A CLIP-based Video Question Answering framework with Explain...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Jeshmol P J Binsu C. Kovoor Division of Information Technology School of Engineering Cochin Univesity of Science and Technology Kochi Kerala

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Video analytics utilizing Video Question Answering (VideoQA) techniques to develop AI systems capable of comprehending and responding to user-specific questions from the content of the custom video datasets. The proposed system detects objects from the custom video datasets using YOLO v8, a deep learning-based object detection algorithm driven by the provided questions. The SORT algorithm helps to track detected objects throughout the entire video. Also, significant frames are extracted from the input questions utilizing a pre-trained model Contrastive Language-Image Pretraining (CLIP) specialized for analyzing video clips. The proposed model comprises video processing where the raw video data is analyzed and transformed for further analysis. Subsequently, questions are encoded and processed facilitating the integration of natural language understanding into the system. The proposed model integrates video comprehension with questions and enables it to correlate the content of the videos with the queries posed by the user. Finally, the model generates answers to the questions asked, accompanied by the selection of the most pertinent frame from the video based on similarity scores. The explainable LIME algorithm enhances the interpretability of the frame with the highest similarity score. This selection process ensures that the provided answers are grounded in the relevant visual context, enhancing the overall quality and relevance of the responses generated by the system. The model exhibited superior performance when applied to specialized custom video datasets.

关键词： YOLO Measurement Deep learning Computer science Analytical models Accuracy Explainable AI visual analytics Question answering (information retrieval) data models

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Automated Extraction of Spatio-Semantic Graphs for Identifying Cognitive Impairment

Automated Extraction of Spatio-Semantic Graphs for Identifyi...

引用

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

作者： Si-Ioi Ng Pranav S. Ambadi Kimberly D. Mueller Julie Liss Visar Berisha Arizona State University USA University of Wisconsin-Madison USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing methods for analyzing linguistic content from picture descriptions for assessment of cognitive-linguistic impairment often overlook the participant’s visual narrative path, which typically requires eye tracking to assess. Spatio-semantic graphs are a useful tool for analyzing this narrative path from transcripts alone, however they are limited by the need for manual tagging of content information units (CIUs). In this paper, we propose an automated approach for estimation of spatio-semantic graphs (via automated extraction of CIUs) from the Cookie Theft picture commonly used in cognitive-linguistic analyses. The method enables the automatic characterization of the visual semantic path during picture description. Experiments demonstrate that the automatic spatio-semantic graphs effectively differentiate between cognitively impaired and unimpaired speakers. Statistical analyses reveal that the features derived by the automated method produce comparable results to the manual method, with even greater group differences between clinical groups of interest. These results highlight the potential of the automated approach for extracting spatio-semantic features in developing clinical speech models for cognitive impairment assessment.

关键词： visualization Dictionaries Statistical analysis Semantics Manuals Tagging Signal processing Feature extraction data mining Speech processing

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Exploring Advanced Optimized Machine Learning Frameworks for Enhanced Analysis and Predictive Modeling of Drilling Well Production Performance

Exploring Advanced Optimized Machine Learning Frameworks for...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Md Ahammad Sharif K. Siva Kumar Venkata Ramana Avula Mohammed Huzaifa Sonu Kumar Emandi Krishna Sai Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The oil and gas industry is increasingly depending on advanced data analytics to maximize production and reduce costs. Perhaps the greatest difficult issue in this industry is precisely estimating the performance of drilling wells to improve decision-making processes. This paper provides ML techniques for predicting drilling well production using ensemble learning. To develop an accurate prediction system, the framework includes Exploratory data Analysis (EDA), data preprocessing, visualization, optimization, and model selection. In this work, combining RF with XGBoost aims to improve predictive performance through utilizing the capabilities of both algorithms. Firefly Optimization is utilized to tune hyperparameters in the hybrid model-building process. This hybrid model is specifically intended for error prediction in firefly optimization scenarios. The approach is developed in Python software and exhibits the real-world application of advanced ML techniques in industrial conditions. The proposed hybrid RF-XGBoost model exceeds in terms of error predicting for drilling well production, with an RMSE of 0.01, MAE of 0.01, and MSE of 0.10. The results indicate the approach is effectively accurate in predicting errors in complex data settings, providing insights that have a significant effect on operational decisions in the field of well data management.

关键词： Drilling Accuracy data analysis Production Predictive models Gas industry Software Petroleum industry Optimization Python

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Seg-diffusion: Text-to-Image Diffusion Model for Open-Vocabulary Semantic Segmentation

Seg-diffusion: Text-to-Image Diffusion Model for Open-Vocabu...

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

作者： Shuo Zhang Jiaming Huang Yan Wu Tao Hu Wenbing Tang Jing Liu Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Huolala Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Open-vocabulary semantic segmentation (OVSS) is a challenging computer vision task that labels each pixel within an image based on text descriptions. Recent advancements in OVSS are largely attributed to the increased model capacity. However, these models often struggle with unfamiliar images or unseen text, as their visual language understanding is limited to training data. Text-to-image (T2I) diffusion models have demonstrated strong image generation with diverse open-vocabulary descriptions. It prompted us to explore whether the comprehensive priors in T2I diffusion models could enhance the zero-shot generalization of OVSS. In this study, we define OVSS as a denoising diffusion task from noisy to object mask and introduce Seg-diffusion, a novel method based on Stable Diffusion that utilizes its extensive visual and linguistic prior knowledge. Specifically, the object mask diffuses from ground-truth to a random distribution in latent space. The model learns to reverse this noisy process to reconstruct object mask to segment objectives using text embeddings with our proposed Content Attention Module (CAM). Extensive experiments on popular OVSS benchmarks show that Seg-diffusion outperforms previous well-established methods and achieves impressive zero-shot generalization to unseen datasets.

关键词： visualization Semantic segmentation Computational modeling Semantics Text to image Training data Signal processing Diffusion models Noise measurement Speech processing

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Mapping Network Paths: Enhancing Traceroute Testing for Network Analysis

Mapping Network Paths: Enhancing Traceroute Testing for Netw...

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international conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Hasan Ahammad Md. Samsuzzaman Faculty of Computer Science & Engineering Patuakhali Science & Technology University Patuakhali Bangladesh Department of Computer & Communication Engineering Patuakhali Science & Technology University Patuakhali Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

As the world and its technologies continue to develop, complicating processes of connecting multiple networks, network management and engineering professionals need to have the skills to diagnose network connectivity problems. One such network analytical tool goes by the name of traceroute and its primary use is to trace the path that the packets take from the source host to the destination host in the given IP network. However, understanding of Traceroute results that are usually printed in the form of a text table would be hardly possible for a common user and in any case could not give a sufficient idea of the network structure. This challenge is lack of visual tool to analyze the traceroute result in a graphical manner, The Traceroute visualization Tool is the solution for this challenge because it offers the means for visualizing the traceroute results with an interface that is graphically friendly and allows for an interactive map on which to display such results. As previously explained, the packet route can be visualized simplistically using geocoding techniques which allow the conversion of IP addresses to geographical coordinates. Through the use of network hops, latency and many other features, it becomes easier for users to recognize various problems that may be associated with networks and badly connection points hence making it easier for users to diagnose problematic points in their networks.

关键词： visualization data visualization Network analyzers Reliability engineering Real-time systems Teamwork IP networks Usability Optimization Testing

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Case Study on the Effectiveness Feature Engineering Techniques for Performance Improvement of data Mining Applications

Case Study on the Effectiveness Feature Engineering Techniqu...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： Shruti Mehra B S Shajee Mohan Neha Agarwal M.Tech CSE Data Science Sharda University Gr.Noida Sharda University Gr.Noida

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

In this paper we try to demonstrate the effectiveness of different techniques for feature selection and optimization strategies that are used for data mining applications. Feature extraction is an important step in every data mining application, which aims to find discriminative patterns, interconnection, and structures within large datasets. In this paper we try to illustrate the effective use of the classical methods such as Principal Component Analysis (PCA), Kernel principal component analysis (KPCA), as well as more innovatory methods to better represent the data so as to achieve a better performance of the underlying pattern analysis, datamining tasks. We also consider effective representation of the data items in a discriminative way. We demonstrate this by making use of synthetic data so that a visual analysis can be made. We also use 3 real world datasets to demonstrate the effectiveness of PCA and KPCA based representations for obtaining improved performance for an SVM based multi-class classifier.

关键词： Wavelet transforms Text mining Support vector machines visualization Feature extraction Transformers data mining Kernel Principal component analysis Synthetic data

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Leveraging visual Language Information for Enhanced Person Re-Identification: A Robust Framework for Real-World Applications

Leveraging Visual Language Information for Enhanced Person R...

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IEEE international conference on Consumer Electronics (ICCE)

作者： Seunghee Han Dasol Jeong Hasil Park Jiwon Park Hyebean Lee Joonki Paik Department of Image Chung-Ang University Seoul Korea Department of Artificial Intelligence Chung-Ang University Seoul Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Recently, performance improvements through vision-language learning have gained significant attention in the field of image re-identification (ReID). In particular, the CLIP-ReID [1] achieved enhanced visual representations by introducing learnable text token sets for each identity. However, existing approaches have not fully exploited fine-grained part-specific features of the human body, which are crucial when applying person re-identification technology in real-world scenarios where partial occlusions or other factors might affect accuracy. To address this limitation, we propose a novel framework that incorporates body part-specific learnable text tokens, leveraging visual language information related to specific body regions. This approach is essential for mitigating the impact of occlusions and enhancing the robustness of ReID systems in practical settings. The process of applying visual language information through CLIP-ReID involves several stages. First, body part regions are identified and segmented within the image. Next, visual features specific to these regions are extracted and mapped to corresponding text tokens that describe these features in natural language. Finally, these text tokens are integrated with the image features to create a more comprehensive representation that captures both visual and semantic aspects, improving the discrimination of individuals based on specific anatomical features. Experimental results not only using the MSMT17 [2] and Market-1501 [3] datasets but also from a real-world scenario involving 11 CCTV cameras installed on the campus of Chung-Ang University demonstrated that the proposed method significantly outperformed existing models. This confirms the effectiveness of vision-language learning that utilizes segmented body part information in the image re-identification task.

关键词： Deformable models visualization Image segmentation Deformation Semantics Natural languages data visualization Feature extraction Robustness Identification of persons

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Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras

Event Masked Autoencoder: Point-wise Action Recognition with...

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

作者： Jingkai Sun Qiang Zhang Jiaxu Wang Jiahang Cao Hao Cheng Renjing Xu Hong Kong University of Science and Technology Guangzhou

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dynamic vision sensors (DVS) are bio-inspired devices that capture visual information in the form of asynchronous events, which encode changes in pixel intensity with high temporal resolution and low latency. These events provide rich motion cues that can be exploited for various computer vision tasks, such as action recognition. However, most existing DVS-based action recognition methods lose temporal information during data transformation or suffer from noise and outliers caused by sensor imperfections or environmental factors. To address these challenges, we propose a novel framework that preserves and exploits the spatiotemporal structure of event data for action recognition. Our framework consists of two main components: 1) a point-wise event masked autoencoder (MAE) that learns a compact and discriminative representation of event patches by reconstructing them from masked raw event camera points data; 2) an improved event points patch generation algorithm that leverages an event data inlier model and point-wise data augmentation techniques to enhance the quality and diversity of event points patches. To the best of our knowledge, our approach introduces the pre-train method into event camera raw points data for the first time, and we propose a novel event points patch embedding to utilize transformer-based models on event cameras.

关键词： visualization Autoencoders Speech recognition Vision sensors Cameras Transformers data models Spatiotemporal phenomena Voltage control Speech processing

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Enhanced Image Steganography Using Neural Style Transfer

Enhanced Image Steganography Using Neural Style Transfer

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Nithiyanantham Vijaya Lakshmi A Dinesh S Roshan R Department of Computer Science Sri Sivasubramaniya Nadar College of Engineering Chennai India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper presents a novel approach to image steganography by integrating Neural Style Transfer (NST) with multi-layer embedding techniques. Traditional steganography methods often struggle with the fundamental challenges of limited data capacity and image quality degradation when embedding large volumes of data. Also traditional methods fail to accurately retrieve the secret data at the receiving end and the methods are vulnerable to steganalysis *** system transcends these limitations through a three-module design: a multi-layer embedding module based on LSB method over RGB channels, a Neural Style Transfer module for aesthetic processing, and a Style Generative Adversarial Network (StyleGAN) for preservation of image fidelity. The multi-layer embedding scheme boosts data capacity significantly without loss of visual quality, and NST provides an additional layer of security by hiding secret information naturally within stylized transformations. Experimental results demonstrate that our method provides higher embedding capacity compared to traditional methods without loss of imperceptibility through aesthetic processing. The addition of StyleGAN also enhances visual quality of stego images, rendering them steganalysis-immune. Our method is highly promising in applications where secure transmission of large data is necessary, with correct recovery of data without loss of aesthetic quality of the cover image.

关键词： Image quality Degradation Steganography visualization data security Generative adversarial networks Rendering (computer graphics) Neural style transfer Robustness Telecommunication computing

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Analytical Approach for Truthfulness Detection Using Multimodal Features: A Case Study with Vision and Audio data

Analytical Approach for Truthfulness Detection Using Multimo...

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international conference on Big data and Smart Computing (BIGCOMP)

作者： Jangkyum Kim Ji-Won Choi Donghyeon Park Young Dae Ko Department of Artificial Intelligence and Data Science Sejong University Seoul South Korea Department of Data Science Sejong University Seoul Republic of South Korea Department of Hotel and Tourism Management Sejong University Seoul Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Lie detection has been a critical area of research with applications in various fields such as forensics, criminal investigation, and psychology. Traditional methods like the polygraph have limitations, such as low accuracy, subjective interpretation, and invasive procedures, which hinder their practical use. Consequently, several studies have explored micro-expression analysis and voice signal-based detection, which show significant potential for detecting the authenticity of a conversation. However, these approaches face challenges in achieving high accuracy and generalizability. To address these issues, this paper proposes a multi-modal model-based lie detection system that integrates audio, text, and video features extracted from a single video. The system utilizes advanced algorithms for audio feature extraction and visual feature analysis. Additionally, a Vision-Language Model for multimodal data integration is employed to detect the authenticity of a conversation. This approach enables the robust detection of subtle signal changes in voice and visual expressions, providing a non-invasive and efficient solution. The proposed system is validated by demonstrating its effectiveness and potential through an actual dataset.

关键词： Analytical models visualization Accuracy Computational modeling Forensics Psychology Oral communication Feature extraction data models Stability analysis

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