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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421-430 订阅

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Chitrarth: Bridging Vision and Language for a Billion People

Chitrarth: Bridging Vision and Language for a Billion People

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

作者： Shaharukh Khan Ayush Tarun Abhinav Ravi Ali Faraz Praveen Kumar Pokala Anagha Bhangare Raja Kolla Chandra Khatri Shubham Agarwal Krutrim AI Bangalore India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent multimodal foundation models are primarily trained on English or high resource European language data, which limits their applicability to other medium and low-resource languages, such as the Indian languages. To address this limitation, we introduce Chitrarth (Chitra: Image; Artha: Meaning), an inclusive Vision-Language Model (VLM), specifically targeting the rich linguistic diversity and visual reasoning across 10 prominent Indian languages. Our model effectively integrates a state-of-the-art (SOTA) multilingual Large Language Model (LLM) with a vision module, primarily trained on multilingual image-text data. Furthermore, we also introduce BharatBench, a comprehensive framework for evaluating VLMs across various low resource languages, ultimately contributing to more diverse and effective AI systems. Our model presents SOTA results for benchmarks across Indian languages while retaining its efficiency in English. Through our research, we aim to set new benchmarks in multilingual-multimodal capabilities, offering substantial improvements over existing models and establishing a foundation for facilitating future advancements in this arena.

关键词： Training Representation learning visualization Pipelines Training data Benchmark testing data models Multilingual Speech processing Signal resolution

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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

引用

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

作者： Ziyu Wang Tengyu Pan Zhenyu Li Ji Wu Xiuxing Li Jianyong Wang Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask.

关键词： Training visualization Noise Training data Functional magnetic resonance imaging Brain modeling Decoding Labeling Speech processing Optimization

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Navigating High-Dimensional data with Advanced Clustering Algorithms

Navigating High-Dimensional Data with Advanced Clustering Al...

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

作者： Ziyan Shirin Raha Md. Arafat Alam Khandaker Md. Zahid Hossain Shamim Akhter Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This research presents a comprehensive analysis of various high-dimensional clustering algorithms applied to a large dataset comprising 5,000,000 data points. To manage the high dimensionality, Principal Component Analysis (PCA) reduces the datasets high dimensionality while keeping substantial variance. The study evaluates various clustering algorithms: DBSCAN, HDBSCAN, Spectral Clustering, BIRCH, Autoencoder + KMeans, KMedoids and CLARANS. Performance is assessed using Silhouette Score, Davies-Bouldin Score, and Calinski-Harabasz Index, measuring cluster compactness, separation and dispersion ratio, respectively. Our findings demonstrate that BIRCH excels with a high Silhouette Score of 0.9685, the lowest Davies-Bouldin Score of 0.0650, and a Calinski-Harabasz Index of 2105.4370, proving its ability in constructing well-defined, cohesive clusters. This study combines quantitative metrics and visual analyses to assist discover the best clustering approaches for high-dimensional data.

关键词： Measurement visualization High dimensional data Navigation Autoencoders Clustering algorithms Intrusion detection Feature extraction Indexes Principal component analysis

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Faces in the Fog: A Deep Dive into Face De-Identification Techniques and Their Comparative Analysis

Faces in the Fog: A Deep Dive into Face De-Identification Te...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Shaonti Goswami Sudipty Konica Paul Sanyam Jawlia Anurag Goel Department of CSE Delhi Technological University New Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Face de-identification have drawn a lot of attention due to growing privacy concerns and the growing requirement for anonymization in visual data. This review suggests a systematic exploration of current face de-identification methods, emphasizing three main deep learning-based methods: diffusion models, latent Variable based transformations, and GAN-based models. The in-depth analysis of this literature provides a comparison of how well each strategies protect privacy while retaining data utility by focusing on the advantages and disadvantages of them. Our analysis delves deeper into the benefits and limitations of these models, focusing on important issues such as adversarial robustness, systematic biases, and trade-offs between realistic image generation and privacy preservation. This comprehensive overview synthesizes data from several studies, identifies important gaps in existing approaches and provides guidance to researchers and industry practitioners directing to more reliable and ethical de-identification frameworks.

关键词： Deep learning data privacy Privacy visualization Systematics Reviews Focusing Diffusion models Protection Faces

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PERCEVO: AI-powered Smart Cane with Dynamic Object Recognition to Support People with Disabilities

PERCEVO: AI-powered Smart Cane with Dynamic Object Recogniti...

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Women in data Science at Prince Sultan University (WiDS PSU), international conference of

作者： Haifa Ammar Zeineddin Aisha Ashworth Anees Ara Asma Vaheed Khan Raneem Alsoby Tanzila Saba Dept. of Computer Science Prince Sultan University Riyadh Saudi Arabia Dept. of Information Science Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Dta Analytics Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

With the recent advancement of AI technologies, many traditional tools have been developed to improve the quality of life for people with different impairments. One such field is technology for the visually impaired, where current solutions focus more on navigation and less on helping the visually impaired to understand their environment. This paper presents Percevo, an AI-powered smart cane attachment that provides real-time descriptions of the environment. Equipped with a camera and AI algorithms, Percevo detects, recognizes, and provides information about nearby objects, including their distance from the user. Additionally, it incorporates an ultrasonic sensor for collision avoidance, and vibration sensors extend its utility to users with hearing impairments. A physical prototype of Percevo was developed and tested in an indoor environment, demonstrating its potential to enhance quality of life for users with visual and hearing impairments. The object detection algorithm achieved a success rate of 45% indoors and 25% outdoors, highlighting the challenges that occur due to rapidly changing environments, varying lighting conditions, and cluttered backgrounds. While the detection speed itself was adequate for indoor environments, further optimization is required for outdoor environments. Nonetheless, in comparison to existing tools, the integration of the system's components combining all forms of effective feedback from the surrounding environment, achieved an overall 98% indoor accuracy and 90% outdoor accuracy in detecting obstacles and measuring distances within a range of 1.3 meters. This work highlights the potential of AI technologies for assistive devices and encourages continued exploration with larger datasets to further refine the system.

关键词： Vibrations visualization Accuracy Prototypes Auditory system People with disabilities Real-time systems Indoor environment Object recognition Artificial intelligence

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Automatic Emoticons Insertion System Based on Acoustic Information of User Voice: 4th Report on Silence Removal for Generalized Emotion Estimation and Improved visual Representations

Automatic Emoticons Insertion System Based on Acoustic Infor...

引用

international conference on Mechatronics (ICM)

作者： Ryo Senuma Sho Yokota Akihiro Matsumoto Daisuke Chugo Satoshi Muramatsu Hiroshi Hashimoto Dept. of Mechanical Engineering Toyo University Saitama Japan School of Engineering Kwansei Gakuin University Sanda Japan Dept. of Applied Computer Eng. Tokai University Hiratsuka Japan Adv. Institute of Industrial Tech Shinagawa Japan

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This study proposes an automatic emoticon insertion system based on acoustic information from user voice. The system estimates emotions from acoustic features using Support Vector Machine (SVM) and inserts corresponding emoticons to enhance emotional communication in text-based social media. Key advancements include the removal of silent segments from training model voice data, improving emotion estimation accuracy and generalizability, as well as a refined visual representation method to address the polysemous nature of emoticons. Experimental results show a significant performance improvement, with accuracy rising to 75.12% compared to the previous model. These findings lay the groundwork for real-time applications, supporting enhanced user communication efficiency and emotion expressiveness in digital contexts.

关键词： Support vector machines Training visualization Adaptation models Accuracy Social networking (online) Estimation data models Acoustics Real-time systems

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Pre-training with Synthetic Patterns for Audio

Pre-training with Synthetic Patterns for Audio

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

作者： Yuchi Ishikawa Tatsuya Komatsu Yoshimitsu Aoki LY Corporation Keio University Tokyo Japan LY Corporation Tokyo Japan Keio University Kanagawa Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose to pre-train audio encoders using synthetic patterns instead of real audio data. Our proposed framework consists of two key elements. The first one is Masked Autoencoder (MAE), a self-supervised learning framework that learns from reconstructing data from randomly masked counterparts. MAEs tend to focus on low-level information such as visual patterns and regularities within data. Therefore, it is unimportant what is portrayed in the input, whether it be images, audio mel-spectrograms, or even synthetic patterns. This leads to the second key element, which is synthetic data. Synthetic data, unlike real audio, is free from privacy and licensing infringement issues. By combining MAEs and synthetic patterns, our framework enables the model to learn generalized feature representations without real data, while addressing the issues related to real audio. To evaluate the efficacy of our framework, we conduct extensive experiments across a total of 13 audio tasks and 17 synthetic datasets. The experiments provide insights into which types of synthetic patterns are effective for audio. Our results demonstrate that our framework achieves performance comparable to models pre-trained on AudioSet-2M and partially outperforms image-based pre-training methods.

关键词： data privacy visualization Autoencoders Self-supervised learning Signal processing Licenses data models Speech processing Image reconstruction Synthetic data

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Multi-Robot Coordination with Adversarial Perception

Multi-Robot Coordination with Adversarial Perception

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international conference on Unmanned Aircraft Systems (ICUAS)

作者： Rayan Bahrami Hamidreza Jafarnejadsani Department of Mechanical Engineering University of Maryland College Park MD USA Department of Mechanical Engineering Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper investigates the resilience of perception-based multi-robot coordination with wireless communication to online adversarial perception. A systematic study of this problem is essential for many safety-critical robotic applications that rely on the measurements from learned perception modules. We consider a (small) team of quadrotor robots that rely only on an Inertial Measurement Unit (IMU) and the visual data measurements obtained from a learned multi-task perception module (e.g., object detection) for downstream tasks, including relative localization and coordination. We focus on a class of adversarial perception attacks that cause misclassification, mislocalization, and latency. We propose that the effects of adversarial misclassification and mislocalization can be modeled as sporadic (intermittent) and spurious measurement data for the downstream tasks. To address this, we present a framework for resilience analysis of multi-robot coordination with adversarial measurements. The framework integrates data from visual-Inertial Odometry (VIO) and the learned perception model for robust relative localization and state estimation in the presence of adversarially sporadic and spurious measurements. The framework allows for quantifying the degradation in system observability and stability in relation to the success rate of adversarial perception. Finally, experimental results on a multi-robot platform demonstrate the real-world applicability of our methodology for resource-constrained robotic platforms.

关键词： Location awareness Wireless communication visualization Systematics Robot kinematics data models Stability analysis State estimation Resilience Quadrotors

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FLODA: Harnessing Vision-Language Models for Deepfake Assessment

FLODA: Harnessing Vision-Language Models for Deepfake Assess...

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IEEE international conference on Consumer Electronics (ICCE)

作者： Seunghyeon Park Youngho Bae Gunhui Han Alexander W Olson School of Integrated Technology Yonsei University Incheon Republic of Korea dept. Data Science Hanyang University Seoul Republic of Korea dept. Mechanical Engineering Yonsei University Seoul Republic of Korea Centre for Analytics and Artificial Intelligence Engineering University of Toronto Toronto Canada

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The proliferation of advanced generative AI models has ushered in a new era of image creation, where the distinction between real and synthetic content has become increasingly blurred. This phenomenon poses significant challenges to the integrity and trustworthiness of digital content and AI systems. Recently, Vision-Language Models (VLMs) have shown great potential in addressing these challenges due to their superior performance in vision-language tasks. In this paper, we propose FLODA (FLorence-2 Optimized for Deepfake Assessment), an advanced method designed to surpass existing deepfake detection models. FLODA leverages the VLM model, Florence-2, and extends it by integrating image captioning and authenticity assessment into a single end-to-end architecture. By utilizing caption information, FLODA enhances visual analysis with contextual details, streamlining both caption generation and deepfake detection. To ensure FLODA's efficacy, we performed an ablation study to identify the optimal model configuration and demonstrated its contributions to performance improvements. We also compared our best FLODA model against existing benchmarks through rigorous evaluation. Notably, FLODA demonstrates strong generalization, showcasing its robustness across diverse scenarios with an average accuracy of 97.14%. This research advances digital content integrity and AI trustworthiness, providing a promising direction for future VLM applications. Code and models can be found at https://***/byh711/FLODA.

关键词： Training Deepfakes visualization Accuracy Generative AI Streaming media Robustness Faces Resilience Context modeling

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Study on Improved YOLOv8 Real-time Dynamic visual Detection Algorithm for Tobacco Clusters Based on Mosaic data Enhancement and Difficult Sample Mining

Study on Improved YOLOv8 Real-time Dynamic Visual Detection ...

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Sensors and Information Technology (ICSI), international conference on

作者： Jinsheng Hou Chuantong Chen Kang Liao Peng Zhang Sanqing Dua Yonghao Gao Baosheng Wu Yanzhao Sun Jinan Cigarette Factory Jinan Shandong China Qinhuangdao Tobacco Machinery Company Limited Qinhuangdao Hebei China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Aiming at the product quality problems caused by the agglomeration phenomenon during the production of tobacco in cigarette factories, this paper proposes a real-time dynamic visual inspection algorithm based on YOLOv8. By combining Mosaic data enhancement and difficult sample mining methods, it solves the challenges of small number of agglomerated samples, high real-time requirements for detection, and difficulties in detecting agglomerates of small sizes. The experimental results show that the improved YOLOv8 algorithm achieves 95.6% detection accuracy and 94.0% detection rate on the test set, which is significantly better than the traditional Faster R-CNN (89.6% accuracy, 92.9% detection rate) and the original YOLOv8 (93.1% accuracy, 91.2% detection rate). After the actual production line deployment, the number of false alarms of the system is less than 10 per month, and the success rate of manual simulation test is as high as 99.4%. This study provides an efficient automated solution for the quality control of tobacco filament agglomeration, which has significant application value.

关键词： visualization Accuracy Heuristic algorithms data enhancement Quality control Inspection Real-time systems Stability analysis data mining Textile industry

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