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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54092 条记录，以下是391-400 订阅

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SYNCVISION: High-Resolution Speech-Synchronized Portrait Animation Using Conditional Generative Adversarial Networks

SYNCVISION: High-Resolution Speech-Synchronized Portrait Ani...

引用

International conference on Communication Systems and Network Technologies (CSNT)

作者： Boggarapu Srinivasulu P. Soumya R. Harini R. Hyndhavi N. Devaki Department of Information BVRIT Hyderabad College of Engineerin for Women Hyderabad Telangana India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

SYNCVISION is a deep learning-based system designed to generate high-resolution speech-synced portrait animations. By utilizing the pretrained Wav2Lip model, it aligns lip movements in portrait images with corresponding audio input, ensuring high-quality animations with computational efficiency. The system is optimized to handle various accents, languages, and visual contexts, making it versatile across different scenarios. SYNCVISION is evaluated using metrics like Mean Absolute Error (MAE) and Lip Synchronization Error (LSE), as well as subjective quality assessments. This project aims to provide a scalable solution for creating realistic speech-driven animations in applications like entertainment, education, and virtual communication.

关键词： Measurement Deep learning visualization Lips Education Entertainment industry Animation Generative adversarial networks Quality assessment Synchronization

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VPCI: Self-Supervised Visual Prompt-Guided Cross-Domain Interactive Image Fusion Framework

VPCI: Self-Supervised Visual Prompt-Guided Cross-Domain Inte...

引用

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

作者： Yong Liu Chengyu Wu Jiayuan Cui Bin Jiang Shenzhen Research Institute of Shandong University Shenzhen China School of Mechanical Electrical & Information Engineering Shandong University Weihai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Image fusion combines information from multi-modality images to produce high-quality fused images with enhanced clarity, contrast, and informativeness. However, limited ground truth fusion data lead to difficulties in effectively training these fusion models. Moreover, current studies lack of fine-grained, domain style-specific guidance during image fusion processing. To address these issues, we propose a self-supervised Visual Prompt-guided Cross-Domain Interactive image fusion framework (VPCI), which introduces a Visual Prompt Generation (VPG) strategy that embeds visual features from different modalities as prompts, guiding the model to learn complementary information. Furthermore, to better integrate modality-specific styles, we introduce a Cross-domain Prompt-guided Interaction (CPI) module to generate information-rich fusion images by fully interacting with visual prompts. Experimental results show that our framework outperforms existing state-of-the-art methods. The code is available at https://***/liwead/VPCI.

关键词： Training visualization Codes Signal processing Data models Acoustics High frequency Speech processing Image fusion

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Automated Wind Turbine Inspection and Structural Damage Detection Using YOLOv5s

Automated Wind Turbine Inspection and Structural Damage Dete...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Vishwajeet Karpe Rushikesh Gaikwad Om Gole Prasad Gore Riddhi Mirajkar Nitin Sawalkar Dept. of Information Technology Vishwakarma Institute of Information Technology Pune India Dept. of Civil Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

An automated damage detection method based on deep learning for wind turbine blades uses the YOLOv5 framework, which is object detection that is quite fast and relatively accurate for detecting objects and localization instances of damage in images. Source data has been a curated collection dataset of wind turbine images with annotated regions of damage; thus, a model was trained from Roboflow. This uses transfer learning in which the model is started with pre-trained YOLOv5s weights and further fine-tunes it over the specific dataset. It also fits well into the nature of the data-set including the number of classes of damage. The training process, therefore, is optimizing the model parameters to achieve the minimization of differences between predicted bounding boxes and the available ground-truth annotations. Standard metrics for object detection are used in the evaluation of the model: precision, recall, mean Average Precision (mAP), and F1-score. Qualitative assessment is also carried through as a visual check on the predictions that the model would produce on unseen images, validating its use in real conditions. It contributes to developing an automated and efficient system concerning structural health monitoring of wind turbines and enables maintenance at the appropriate time, thus decreasing down times.

关键词： YOLO Training visualization Technological innovation Transfer learning Predictive models Minimization Wind turbines Monitoring Standards

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LINK: Adaptive Modality Interaction for Audio-Visual Video Parsing

LINK: Adaptive Modality Interaction for Audio-Visual Video P...

引用

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

作者： Langyu Wang Bingke Zhu Yingying Chen Jinqiao Wang School of Logistics Engineering Shanghai Maritime University China Foundation Model Research Center Institute of Automation Chinese Academy of Sciences China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Audio-visual video parsing focuses on classifying videos through weak labels while identifying events as either visible, audible, or both, alongside their respective temporal boundaries. Many methods ignore that different modalities often lack alignment, thereby introducing extra noise during modal interaction. In this work, we introduce a Learning Interaction method for Non-aligned Knowledge (LINK), designed to equilibrate the contributions of distinct modalities by dynamically adjusting their input during event prediction. Additionally, we leverage the semantic information of pseudo-labels as a priori knowledge to mitigate noise from other modalities. Our experimental findings demonstrate that our model outperforms existing methods on the LLP dataset.

关键词： Training visualization Adaptation models Semantics Noise Feature extraction Acoustics Speech processing

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Evaluating the Impact of Sonification in an Immersive Analytics Environment Using Real-World Geophysical Datasets

Evaluating the Impact of Sonification in an Immersive Analyt...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Disha Sardana Lee Lisle Denis Gračanin Ivica Ico Bukvic Krešimir Matković Gregory Earle Virginia Tech VRVis GmbH

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

In this paper, we evaluated the impact of audio in an immersive analytics environment using real-world geophysical datasets. We specifically used sonification, i.e., the use of non-speech audio to convey information. To evaluate the impact of sonification, we designed a between-subject experiment in a mixed-reality environment and conducted a user study with 50 participants in two scenarios: Audio-Visual and Visual-Only. In the study, we compared task metrics such as the number of patterns identified by the participants, the level of confidence, participants’ task responses, the NASA task load index, and the SUS questionnaire between each scenario to study the role of sonification in augmenting the analysis process in an immersive environment. We found that event-based sonification, used in addition to the visual channel, is helpful in finding patterns and relations in geophysical datasets. Our results also suggest that using audio in immersive analytics might increase users’ confidence in performing analytics tasks such as pattern finding. Using real-world datasets, we identified the advantages and limitations of using sonification in an immersive analytics context.

关键词： visualization Three-dimensional displays Event detection NASA Virtual reality User interfaces Sonification Particle measurements Spatiotemporal phenomena Pattern recognition

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DeepMatch: Navigating the Complexities of Underwater Textures for Enhanced Keypoint Matching

DeepMatch: Navigating the Complexities of Underwater Texture...

引用

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

作者： Sainan Zhang Tong Liu Zhe Wang Zhibin Yu Bing Zheng Faculty of Information Science and Engineering Ocean University of China Qingdao China Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Sanya China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Driven by demands for oceanic exploration, advancements in 3D visual tasks based on video frames are essential. Keypoint matching, essential for camera pose and motion, is hindered by the unique challenges of underwater imagery, such as sparse and repetitive textures. To tackle these issues, we introduce the DeepMatch framework, tailored for aquatic environments. It comprises two main components: a feature extraction network with a multi-level feature fusion module for enhanced keypoint detection in sparse textures, and an attention-based graph neural network with relative position encoding to correct matches in repetitive texture areas. Our experiments demonstrate that DeepMatch surpasses traditional and learning-based methods in underwater image matching, providing a robust solution for accurate camera pose estimation.

关键词： Learning systems visualization Three-dimensional displays Navigation Image matching Pose estimation Speech enhancement Signal processing Feature extraction Cameras

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LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Expression in Vision-Language Models

LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Express...

引用

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

作者： Jingyi Wang Jianzhong Ju Jian Luan Zhidong Deng Department of Computer Science and Technology BNRist THUAI Tsinghua University Beijing China Xiaomi AI Lab Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advances in large vision-language models (LVLMs) typically employ vision encoders based on the Vision Transformer (ViT) architecture. The division of the images into patches by ViT results in a fragmented perception, thereby hindering the visual understanding capabilities of LVLMs. In this paper, we propose an innovative enhancement to address this limitation by introducing a Scene Graph Expression (SGE) module in LVLMs. This module extracts and structurally expresses the complex semantic information within images, thereby improving the foundational perception and understanding abilities of LVLMs. Extensive experiments demonstrate that integrating our SGE module significantly enhances the LVLM’s performance in vision-language tasks, indicating its effectiveness in preserving intricate semantic details and facilitating better visual understanding.

关键词： visualization Computer vision Semantics Signal processing Transformers Acoustics Data mining Speech processing

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Boosting Movie and TV Tag Accuracy with Knowledge Graphs*

Boosting Movie and TV Tag Accuracy with Knowledge Graphs*

引用

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

作者： Hongxun Jiang Lin Zhang Lifeng Zhang School of Information Renmin University of China Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper introduces a fine-grained video tag classification algorithm that integrates knowledge graphs to identify specific TV series. A multimodal pretraining model extracts visual and textual features, feeding them into a multitask prediction model to classify type, genre, and entity tags. A similarity task enhances feature compactness, while an entity correction model refines predictions using co-occurrence data from knowledge graphs. Using a knowledge graph built from the data of Douban, a leading platform in China, the model achieved a 3.70% improvement in Top-1 accuracy for type tags, 3.35% for genre tags, and 16.57% for entity tags. The global-local attention model further increased entity tag accuracy from 38.7% to 45.6%. This approach boosts classification accuracy and provides insights for handling limited data.

关键词： visualization Technological innovation Accuracy TV Attention mechanisms Knowledge graphs Predictive models Streaming media Feature extraction Data models

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SpecViT: A Custom Vision-Transformer based Approach for Audio Deepfake Detection

SpecViT: A Custom Vision-Transformer based Approach for Audi...

引用

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

作者： Sharmistha Modak Arnab Kumar Das Ruchira Naskar Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Degrees of hyper-realism already attained by present-day deepfake technology poses one of the biggest social threats of today. Deepfakes may involve multiple forms of media including audio, video and images. While most of the literature deals with threats posed by visual synthetic media, the endeavor to uncover audio deepfakes is still evolving; it demands more extensive investigation. Our attempt towards audio deepfake detection in this article, involves investigating spectral patterns present in audio spectrograms, captured appropriately by a two-attention vision transformer model (SpecViT2A), finally exploited for discriminating synthetic audios from pristine ones. We employed our model for successful identification of audio as well as multimodal deepfakes, yielding best accuracy over 99%, F1-score 0.9911, and EER as low as 3.5 on ASVSpoof 2021.

关键词： Deepfakes Computer vision visualization Accuracy Media Transformers Robustness Fraud Speech processing Spectrogram

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Improved Cross-Lingual Speaker Verification Using Speaker Sensitive Feature Guidance and Fine-grained Phonetic information

Improved Cross-Lingual Speaker Verification Using Speaker Se...

引用

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

作者： Yongtai Ji Guangxing Li Hao Huang Yanbing Li Wushour Silamu School of Computer Science and Technology Xinjiang University Urumqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China Xinjiang Key Laboratory of Multi-Lingual Information Technology Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker verification performance significantly degrades when there exists a language mismatch between training and evaluation. Domain Adversarial Training (DAT) has shown to be effective in mitigating this gap by incorporating adversarial training with domain information (language id). Inspired by recent research of DAT, we propose to decompose and locate features sensitive to speaker identity, so that domain adaptation can better serve the speaker classification task. Additionally, we further promote DAT by replacing alignment based on language identification with alignment of fine-grained phonetic information, where pre-trained speech recognition models are utilized to provide frame-level phonetic labels. The speaker verification model is trained on VoxCeleb, while CnCeleb is used for adversarial training and evaluation. Results show the methods effectively mitigate the performance degradation caused by language mismatch.

关键词： Training Degradation Adaptation models visualization Speech recognition Phonetics Signal processing Acoustics Speech processing

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