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Deep Learning Technique for Computer Vision-Based Pose Estimation for Augmented Reality 3rd

Deep Learning Technique for Computer Vision-Based Pose Estim...

引用

3rd International conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Generation of Scene Graph and Semantic Image: A Review and Challenge Ahead

Generation of Scene Graph and Semantic Image: A Review and C...

引用

International conference on Artificial Intelligence in information and communication (ICAIIC)

作者： Shao-Kai Hsieh Huey-Ing Liu Department of Electrical Engineering Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Scene graph generation creates a structured representation of visual scenes by identifying objects and their attributes and the relationships between them. Conversely, semantic image generation converts semantic representations such as scene graphs, textual descriptions, or object layouts, into photorealistic images. This paper provides an overview of recent advancements in generations of scene graph and semantic image. The survey explores how innovations in both fields can complement each other, paving the way for advanced applications in autonomous systems, semantic communication, visual reasoning, and creative industries. Challenges such as data imbalance, scalability, and semantic coherence and future research directions including multimodal integration, prior processing etc. are throughly discussed.

关键词： Surveys visualization Technological innovation Image synthesis Reviews Scalability Semantics Semantic communication Artificial intelligence Photorealistic images

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Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven communication 25

Pluto: Authoring Semantically Aligned Text and Charts for Da...

引用

30th International conference on Intelligent User Interfaces, IUI 2025

作者： Srinivasan, Arjun Setlur, Vidya Satyanarayan, Arvind Tableau Research SeattleWA United States Tableau Research Palo AltoCA United States Csail Mit CambridgeMA United States

ISBN: (纸本)9798400713064

Textual content (including titles, annotations, and captions) plays a central role in helping readers understand a visualization by emphasizing, contextualizing, or summarizing the depicted data. Yet, existing visualization tools provide limited support for jointly authoring the two modalities of text and visuals such that both convey semantically-rich information and are cohesively integrated. In response, we introduce Pluto, a mixed-initiative authoring system that uses features of a chart's construction (e.g., visual encodings) as well as any textual descriptions a user may have drafted to make suggestions about the content and presentation of the two modalities. For instance, a user can begin to type out a description and interactively brush a region of interest in the chart, and Pluto will generate a relevant auto-completion of the sentence. Similarly, based on a written description, Pluto may suggest lifting a sentence out as an annotation or the visualization's title, or may suggest applying a data transformation (e.g., sort) to better align the two modalities. A preliminary user study revealed that Pluto's recommendations were particularly useful for bootstrapping the authoring process and helped identify different strategies participants adopt when jointly authoring text and charts. Based on study feedback, we discuss design implications for integrating interactive verification features between charts and text, offering control over text verbosity and tone, and enhancing the bidirectional flow in unified text and chart authoring tools. © 2025 Copyright held by the owner/author(s).

关键词： visualization

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ALIC: Adaptive Fusion Entropy Model for Learned Image Compression

ALIC: Adaptive Fusion Entropy Model for Learned Image Compre...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Lingxue Liu, Meiqin Zhang, Yifan Tang, Qi Yao, Chao Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing China Visual Intelligence + X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Recently, learned image compression algorithms have achieved significant performance. The entropy model is crucial for improving the rate-distortion performance by estimating the probability distribution of latent representation. In this paper, we propose an adaptive fusion entropy model for learned image compression (ALIC). To explore the correlation between channel and global spatial features, an adaptive fusion entropy model (AFEM) is designed. AFEM first slices the latent representation along the channels and leverages the adaptive channel fusion context module (ACFC) to capture correlations between the decoded and current slices. Subsequently, AFEM uses the adaptive spatial fusion context module (ASFC) to further divide the current slice into encoding pixels and reference pixels, thus improving the accuracy of probability estimation. The attention map and modulation parameter are introduced in ACFC and ASFC to interact with channel and spatial features. In addition, the variable-rate residual transformer (VResFormer) is proposed to control dynamic bit-rate by selectively modulating the high-frequency component according to coefficient weight and bias. Experimental results indicate that our ALIC outperforms other learned image compression algorithms. Our ALIC saves 5.89% bit-rate compared with VVC (4:4:4) on Kodak dataset. © 2025 IEEE.

关键词： Entropy model Learned image compression Variable-rate

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MixSense : Mixture of Vision Sense

MixSense : Mixture of Vision Sense

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

作者： Jian Lin Zhuoran Wang Qibo Qiu Jianzhong Chen Zixian Ge Weizhong Jin Yuchao Yan Li Yu Research & Innovation Institute China Mobile (Zhejiang) Hangzhou China School of Computing and Information University of Pittsburgh Pittsburgh The United States

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

It is a new trend to fine-tune Large Multimodal Models (LMMs) to adapt to specific visual tasks through task-related conversation data. This approach provides a new paradigm for solving various vision-language tasks, however, it still faces two problems: (1) the global visual features input to the backbone Large Language Model (LLM) lack the focus on task-specific information, which may lead a sub-optimal performance on specific visual tasks; (2) The hallucinations on specific tasks are underestimated, which makes the model prone to factual errors during inference. In order to solve these two problems, we propose MixSense, a new LMM paradigm that aligns the visual features of specilized models and general models to the backbone LLM together, increasing the focus on specific task information while maintaining the vanilla capabilities of the model; evaluated and fine-tuned with the constructed task-specific negative samples, we can further assess the degree of hallucinations of the model on specific visual tasks and reduce it. We validate our method on a popular Object Detection (OD) task, Referring Expression Comprehension (REC), and extensive experiments demonstrate the effectiveness of our proposal.

关键词： Training visualization Pipelines Oral communication Object detection Signal processing Lead Market research Proposals Speech processing

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Structural Enhancements and Challenges from FINFET to 2D Material-Based GAANSFET: A Review 6th

Structural Enhancements and Challenges from FINFET to 2D Mat...

引用

6th International conference on Intelligent communication, Control and Devices, ICICCD 2024

作者： Bhuvaneshwari, S. Pandey, Archana Department of Electronics and Communication Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9789819783281

The rising demand for portable and high-speed devices in the semiconductor industry leads to scaling down of transistor size. Scaling of transistors from the micro- to nanometre range is achieved by Moore’s law, which consequences in many short-channel effects. To overcome these problems, various unique structures along with different materials have been proposed and executed. The primary area of concentration for scientific research and business has been the advancement of semiconductor technology using novel ideas and materials. In the microscale, MOSFETs are significant component of an integrated circuit, but as scaling moves to nanoscale, the leakage becomes higher, which degrades the performance. FINFET replaces MOSFET, which offers high performance and reliability by reducing the leakage. At 14 nm and beyond, FINFET also has certain processing limitations. Hence, another device known as the GAANSFET, that currently dominates the semiconductor industry, emerged to enhance the performance when scaling is beyond 5 nm. In the future, increasing the boundaries of transistor scaling may be possible using the two-dimensional (2D) material. 2D materials are a desirable starting point for reconfigurable technology because of their less thickness and high sensitivity to external electrical fields. They also have excellent electrical qualities. This paper provides the review about the structural/2D material enhancements and their challenges faced during the transition of MOS technology from conventional MOSFET to GAANSFET up to 3 nm, and also brief overview is presented on the scaling of current research proposal beyond 3 nm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： iii-V semiconductors

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Towards Dynamic Neural communication and Speech Neuroprosthesis Based on Viseme Decoding

Towards Dynamic Neural Communication and Speech Neuroprosthe...

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

作者： Ji-Ha Park Seo-Hyun Lee Soowon Kim Seong-Whan Lee Dept. of Artificial Intelligence Korea University Seoul Republic of Korea Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. Although neural signals contain various information on speech intentions, movements, and phonetic details, generating informative outputs from them remains challenging, with mostly focusing on decoding short intentions or producing fragmented outputs. In this study, we developed a diffusion model-based framework to decode visual speech intentions from speech-related non-invasive brain signals, to facilitate face-to-face neural communication. We designed an experiment to consolidate various phonemes to train visemes of each phoneme, aiming to learn the representation of corresponding lip formations from neural signals. By decoding visemes from both isolated trials and continuous sentences, we successfully reconstructed coherent lip movements, effectively bridging the gap between brain signals and dynamic visual interfaces. The results highlight the potential of viseme decoding and talking face reconstruction from human neural signals, marking a significant step toward dynamic neural communication systems and speech neuroprosthesis for patients.

关键词： visualization communication systems Lips Neuroprostheses Signal processing Phonetics Brain modeling Decoding Speech processing Image reconstruction

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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Skin Cancer Classifier: Performance Enhancement Using Deep Learning Models

Skin Cancer Classifier: Performance Enhancement Using Deep L...

引用

International conference on Signal processing and communication (ICSC)

作者： Swati Mishra Megha Agarwal Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Skin cancer ranks among the most prevalent human malignancies, primarily diagnosed through visual methods, commencing with clinical screening, followed by dermoscopic analysis. The intricate alterations in the visual appearance of skin lesions pose a considerable challenge for automated classification through images. Deep learning techniques have been harnessed to support dermatologists in enhancing diagnostic accuracy. To address this, deep convolutional neural networks (CNNs) are employed to achieve highly discriminating and potentially generalizable results for image classification problems. This research introduces a prediction model designed to classify skin lesions into seven categories using the HAM10000 skin cancer dataset. The MobileNetV2, Resnet50, and DenseNet121 models were used in this study. The MobileNetV2 outperforms with 99.72 % accuracy and achieves 0.88, 0.89, and 0.88 values of precision, recall, and F1 score.

关键词： Deep learning visualization Analytical models Accuracy Computational modeling Skin Real-time systems Lesions Skin cancer Residual neural networks

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A Critical Assessment of visual Sound Source Localization Models Including Negative Audio

A Critical Assessment of Visual Sound Source Localization Mo...

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

作者： Xavier Juanola Gloria Haro Magdalena Fuentes Universitat Pompeu Fabra Barcelona Spain MARL-IDM New York University New York USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The task of visual Sound Source Localization (VSSL) involves identifying the location of sound sources in visual scenes, integrating audio-visual data for enhanced scene understanding. Despite advancements in state-of-the-art (SOTA) models, we observe three critical flaws: i) The evaluation of the models is mainly focused in sounds produced by objects that are visible in the image, ii) The evaluation often assumes a prior knowledge of the size of the sounding object, and iii) No universal threshold for localization in real-world scenarios is established, as previous approaches only consider positive examples without accounting for both positive and negative cases. In this paper, we introduce extended test sets and new metrics designed to complete the current standard evaluation of VSSL models by testing them in scenarios where none of the objects in the image corresponds to the audio input, i.e. a negative audio. We consider three types of negative audio: silence, noise and offscreen. Our analysis reveals that numerous SOTA models fail to appropriately adjust their predictions based on audio input, suggesting that these models may not be leveraging audio information as intended. Additionally, we provide a comprehensive analysis of the range of maximum values in the estimated audio-visual similarity maps, in both positive and negative audio cases, and show that most of the models are not discriminative enough, making them unfit to choose a universal threshold appropriate to perform sound localization without any a priori information of the sounding object, that is, object size and visibility.

关键词： Location awareness Measurement visualization Analytical models Noise Predictive models Speech processing Standards Testing Videos

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