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M-RAT: a Multi-grained Retrieval Augmentation Transformer for image Captioning 17th

M-RAT: a Multi-grained Retrieval Augmentation Transformer f...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Song, Jiayan Pan, Renjie Zhou, Jun Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai200240 China

ISBN: (纸本)9789819609079

Current encoder-decoder methods for image captioning mai-nly consist of an object detection module (two-stage), or rely on big models with large-scale datasets to improve the effectiveness, which leads to increasing computation costs and cannot introduce new external knowledge. In this paper, we propose a novel end-to-end method Multi-grained Retrieval Augmentation Transformer (M-RAT) that innovatively fuses retrieved text derived from a changeable datastore with input visual feature through a Multi-modal Aligned Encoder, and introduce a specialized attention mechanism, Multi-MSA, to exploit both local and global interactions for delicate fine-grained details. Additionally, we enhance the decoder generation ability by employing low-level and high-level fused embeddings. Experiments demonstrate that M-RAT achieves comparable performance to state-of-the-art baselines with remarkable accuracy and details, as well as showing excellent domain adaptability for novel objects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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A particle belief propagation algorithm based on track-before-detect for tracking low-observable and maneuvering targets using multiple sensor 16

A particle belief propagation algorithm based on track-befor...

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16th International conference on Signal processing Systems, ICSPS 2024

作者： Cao, Chenghu Huang, Haisheng Zhao, Yongbo Li, Xin Hui, Peiyuan Xi’an University of Posts and Communications Xi’an710121 China National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Xi’an Innovation College of Yan’an University Xi’an710100 China

ISBN: (纸本)9781510689251

It is notoriously challenging work to track an unknown number of low-observable maneuvering targets. In this paper, a sequential Bayesian inference method based on multiple-model dynamic model and track-before-detect measurement (TBD) model is proposed for tracking low-observable maneuvering targets using multiple sensors. Our proposed method is based on a new multi-sensor statistical model that allows targets to interact and contribute to more than one data cell for pixeled image TBD approach. Based on the factor graph representing multi-sensor statistical model, the marginal posterior densities are derived by performing the message passing equations of the proposed belief propagation (BP) algorithm for target detection and target state estimation. The simulation results validate that the computational complexity of our proposed multi-sensor BP-TBD algorithm scales in the number of sensor nodes and demonstrate that its performance is superior among the state-of-the-art multi-sensor TBD methods. © 2025 SPIE.

关键词： Sensor nodes

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18th Ibero-American conference on Artificial Intelligence, IBERAMIA 2024

18th Ibero-American Conference on Artificial Intelligence, I...

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18th Ibero-American conference on Artificial Intelligence, IBERAMIA 2024

ISBN: (纸本)9783031803659

The proceedings contain 36 papers. The special focus in this conference is on Artificial Intelligence. The topics include: Improving Assembly Lines with Digital Twins: A Synthetic Case Study;peak Ground Acceleration Prediction for Earthquake Early Warning with Multivariable Long Short-Term Memory Networks and Temporal Transformers;green Security Along Trails;identification of Dating Violence with Machine Learning Algorithms: Analysis and Results;capturing the Essence of Plankton: A Gradient-Weighted Class Activation Mapping Analysis;agent-Based Model and Machine Learning for the Analysis of Barter Regulated by Lotka-Volterra Equations in an Artificial Society;a Geometric Attention Mechanism to Classify Parkinsonism Smooth Pursuit Patterns;A Mixed Audio-Video SPD Network for Online Classification of Parkinsonian Speech Patterns;transformers for Genomic Prediction;AI-Assisted Bronchoscopy in the Intensive Care Unit: Corpus Development and an Application to Anatomic Position Identification;evaluation of Mares Uterine Health Based on Endometrial Biopsies Using image processing and Machine Learning Techniques;X-COVNet: Externally Validated Model for Computer-Aided Diagnosis of Pneumonia-Like Lung Diseases in Chest X-Rays;fairTrees: A Deep Learning Approach for Identifying Deforestation on Satellite images;ensembling Convolutional Neural Networks for Human Skin Segmentation;Risk Assessment for UAV Autonomous Landing in Urban Environments Using Semantic Segmentation;visual SLAM in Underground Environments: Preliminary Results;sound-Based Parakeets Detection System;AI-Based Medical Education: Coping with Clinical Decisions in GLARE-Edu;generating Contrastive Explanations from Gradual Semantics Rankings;automatic Classification of Secondary and High School Students Dropout Risk via Knowledge Graphs and Machine Learning;semi-supervised Hierarchical Bayesian Multi-label Classification;advancing Photovoltaic Forecasting with Neural Networks: Integrating N-Beats and Sequential M

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Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajababu, Durgam Veerraju, Sampenga Kumar, K. Suresh Gupta, Sonali Thurai Raaj, V.B. Joshi, Rahul Sr University School of Engineering Department of Eee Telangana Warangal India Dadi Institute of Engineering and Technology Department of Electronics and Communications Engineering Andhra Pradesh Anakapalli Visakhapatnam531002 India Panimalar Engineering College Mba Department Varadarajapuram Poonamallee Chennai600123 India J C Bose University of Science and Technology Ymca Faridabad India Madanapalle Institute of Technology & Science Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Manav Rachna International Institute of Research and Studies Department of Journalism & Mass Communication SMeH Haryana India

ISBN: (纸本)9798331508685

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries. © 2025 IEEE.

关键词： Convolution

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SCD: Statistical Color Distribution-Based Objective image Colorization Quality Assessment 41st

SCD: Statistical Color Distribution-Based Objective Image Co...

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41st Computer Graphics International conference, CGI 2024

作者： Lyu, Hongjin Elangovan, Hareeharan Rosin, Paul L. Lai, Yu-Kun School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783031818059

Colorization research has long been a focal point in computer vision and image processing. However, due to its inherently ill-posed nature, a reasonable assessment of the quality of their outcomes remains a challenge. Subjective evaluations are often restricted to a limited number of participants due to the high costs. This along with the existence of individual differences and subjective biases makes it difficult to derive convincing conclusions. Despite no need for participants in objective evaluation metrics, the currently widely applied objective metrics fail to accurately reflect the quality of colorization results, thereby impeding the attainment of consistency with subjective user opinions. Facing the above problems, we propose a novel Statistical Color Distribution-based Objective Evaluation Metric (SCD) for better consistency with human opinions. We first segment images into semantic regions. For each semantic type, a novel two-dimensional natural color distribution w.r.t. hue and saturation is collected to better align with human perceptual observations during image assessment. An adjacency weighted matrix considering surrounding neighboring regions smooths the color distribution table, enabling a more reliable quality assessment. The application of probability density eliminates the issue of frequency anomalies caused by human visual insensitivity, ensuring more accurate *** extensive and comprehensive experiments involving two distinct datasets with the participation of 1321 volunteers, this paper demonstrates that the proposed SCD is more consistent with subjective user opinions compared with current objective metrics for evaluating colorization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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StableTalk: Advancing Audio-to-Talking Face Generation with Stable Diffusion and Vision Transformer 27th

StableTalk: Advancing Audio-to-Talking Face Generation with...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Nazarieh, Fatemeh Kittler, Josef Rana, Muhammad Awais Kanojia, Diptesh Feng, Zhenhua School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Institute for People-Centred AI University of Surrey Guildford United Kingdom School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9783031781711

Audio-to-talking face generation stands at the forefront of advancements in generative AI. It bridges the gap between audio and visual representations by generating synchronized and realistic talking faces. Despite recent progress, the lack of realism in animated faces, asynchronous audio-lip movements, and computational burden remain key barriers to practical applications. To address these challenges, we introduce a novel approach, StableTalk, leveraging the emerging capabilities of Stable diffusion models and vision Transformers for Talking face generation. We also integrate the Re-attention mechanism and adversarial loss to improve the consistency of facial animations and synchronization with a given audio input. More importantly, the computational efficiency of our method has been notably enhanced by optimizing operations within the latent space and dynamically adjusting the focus on different parts of the visual content based on the provided conditions. Our experimental results demonstrate the superiority of StableTalk over the existing approaches in image quality, audio-lip synchronization, and computational efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Animation

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Vitiligo Skin Segmentation: An image-Based Unsupervised Learning Approach 5th

Vitiligo Skin Segmentation: An Image-Based Unsupervised Lear...

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5th International conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Tziomaka, Melina Kallipolitis, Athanasios Andreadis, Stelios Matonaki, Anastasia Stavropoulos, Thanos G. Maglogiannis, Ilias Department of Digital Systems University of Piraeus Piraeus Greece Pfizer Center for Digital Innovation Thessaloniki Greece

ISBN: (纸本)9789819638628

Diagnosing and monitoring vitiligo traditionally depend on visual assessments and manual tracing of depigmented patches on skin by medical professionals. This process, while widely adopted, is labor-intensive and challenging, especially when it comes to tracking disease progression or assessing treatment efficacy over time. This paper introduces an unsupervised learning approach for the automated segmentation of vitiligo-affected areas from skin images, circumventing the necessity for dataset-based training. We leverage a multi-stage processing pipeline that includes background exclusion, feature extraction, and image-based clustering. Qualitative analysis demonstrates the method's effectiveness and adaptability, with visual evidence highlighting the segmentation's robustness. Additionally, quantitative evaluation on 150 manually annotated images yields an average Intersection over Union (IoU) score of 0.47 and a Dice coefficient (DSC) score of 0.62, highlighting the method’s performance. The research underscores the potential of image-based unsupervised learning in dermatological imaging, paving the way for accelerated and accessible treatment planning and monitoring in vitiligo care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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Learning Features by Minimizing the Interframe Differences 19th

Learning Features by Minimizing the Interframe Differences

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19th Chinese conference on image and Graphics Technologies and Applications, IGTA 2024

作者： Zhao, Dong Zhang, Dan School of Computer Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China

ISBN: (纸本)9789819799183

In this paper, we present a novel yet intuitive unsupervised feature learning approach, referred to as Minimizing Interframe Differences (MID). The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of the frames, the differences between the before and after frames can be minimally encoded. MID is implemented with a difference encoding module and a frame reconstruction module. The former tries to minimize the difference between frames, while the latter first maps the difference encoding to optical flow and then realizes frame reconstruction based on the learned optical flow. All the modules are jointly learned in an end-to-end way through the reconstruction loss. We verify the feasibility of MID in the image datasets by replacing the natural transformation of videos to artificially parameterized transformation for images. Experimental results show that MID is able to predict optical flow accurately based on the minimum difference coding and learn a powerful visual representation that greatly enhances other visual tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Text Data Security Through Hybrid Method Using visual Cryptography and image Steganography Algorithms 6th

Text Data Security Through Hybrid Method Using Visual Crypto...

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6th International conference on Computational Intelligence in communications and Business Analytics, CICBA 2024

作者： Bhawna Malik, Sanjay Kumar Department of Computer Science and Engineering S R M University Haryana Sonipat India

ISBN: (纸本)9783031813351

Modern society puts a great deal of importance on digital communication, thus keeping it hidden is crucial. Many modern softwares are web-based, and users increasingly rely on social networking sites like Facebook, Twitter, and others. The data must be secured against unauthorized access and usage since it is distributed over unsecured channels. Since the information is sent across unsecured channels, it is crucial to prevent unauthorized access to and use of the data. Although there has been a significant amount of research done in the past, most of it has not taken into account the need for strong hybrid security concerning secret text. The two techniques most commonly used to prevent unwanted access to sensitive text data are visual cryptography and image steganography. These two techniques’ unique merits, nevertheless, are not secure enough for the contemporary era. There may be some vulnerability as a result. If visual cryptography is combined with image steganography, it is feasible to use both algorithms together to obtain high levels of information security. This study presented a hybrid security method for hiding secret text that combines visual cryptography and LSB image steganography. Colour image steganography is used to hide shares in other cover images and cover up secret text in cover images. For splitting the cover image into two shares, use visual cryptography. The secrecy, trustworthiness, and effectiveness of covert communications are considerably improved by these security protocols. Even though there isn’t a parameter to represent the level of security reached by employing cutting-edge technologies, the accuracy of the received text data is calculated in terms of MSE and correlation coefficient by comparing the transmitted and received text data. The time required at the transmitter and receiver ends is also assessed in order to gauge how effective the recommended methodology is. The implementation demonstrates the enhanced resilience of the sug

关键词： Cryptography Decryption Encryption Steganography

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Realistic Virtual Try-on with Multi-Loss Geometric Alignment and Attention-Augmented image Synthesis

Realistic Virtual Try-on with Multi-Loss Geometric Alignment...

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2025 International conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Hyebean Jeong, Dasol Han, Seunghee Park, Jiwon Paik, Joonki Graduate School of Advanced Imaging Science Multimedia Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9798331510756

We present a two-stage virtual try-on framework that combines complementary loss functions with an attention-augmented U-Net to enhance clothing detail preservation and garment-body interaction modeling. In the first stage, the Geometric Matching Module (GMM) introduces complementary loss functions: shape loss for maintaining the structural integrity of the garment, texture loss for preserving detailed appearance and pattern consistency, edge loss for boundary detail and structural line clarity, and grid loss to prevent pattern distortion during garment deformation. In the second stage, the Try-On Module (TOM) augments the U-Net with encoder-based self-attention for global context capture, skip connection-based cross-attention for selective spatial feature refinement, and positional encoding to establish accurate garment-body spatial correlations. This integration improves both the visual quality and geometric accuracy of generated virtual try-on images, enabling realistic and detailed garment synthesis suitable for practical applications such as online retail platforms, virtual fitting rooms, and personalized styling recommendations. Our approach allows consumers to visualize apparel fit and style on a digital self-representation, offering a seamless, interactive experience that can reduce return rates and enhance customer satisfaction. Experiments show that our method outperforms existing virtual try-on systems, marking a notable advancement in the development of practical, consumer-focused virtual try-on technology. © 2025 IEEE.

关键词： Customer satisfaction

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