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Learning visual-Semantic Hierarchical Attribute Space for Interpretable Open-Set Recognition

Learning Visual-Semantic Hierarchical Attribute Space for In...

引用

2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Xu, Zhuo Xiang, Xiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology National Key Lab of Multi-Spectral Information Intelligent Processing Technology Wuhan China Peng Cheng Lab China

ISBN: (纸本)9798331510831

In the field of open-set recognition, conventional models often focus on addressing challenges within a single hierarchical category, and these methods frequently lack inter-pretability. In this paper, we propose a novel solution that utilizes attributes and hierarchical relationships to achieve interpretable open-set recognition. Our method is centered around the visual-semantic attribute space. By leveraging hierarchy division, we can decompose the attributes into more granular components, thereby yielding additional performance improvements. When confronted with an unfamiliar object, our method not only classifies it as an unknown category but also provides insights into the broader category and its associated attributes. This capability enhances interpretability by offering valuable information regarding the potential category and characteristics of the object. Experimental results demonstrate great performance improvements compared to existing methods. © 2025 ieee.

关键词： Hierarchical systems

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Measurement of Video Camera Parameters for Video Recording of Fast Moving Objects

Measurement of Video Camera Parameters for Video Recording o...

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2025 Systems of Signals Generating and processing in the Field of on Board communications, SOSG 2025

作者： Kudryashov, V.V. Balobanov, A.V. Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

In this article, we explored ways to measure video camera parameters that are important for shooting video of fastmoving objects. Fast-moving objects introduce additional distortions into the video recording if the video camera is not adapted to such an operating mode. Geometric distortions, blurs and incorrect brightness transfer are formed on the video recording. There are several ways to avoid or reduce the negative effects introduced during video shooting. The first is to use a technically more advanced video camera with improved quality properties. The second is to collect additional information during video shooting and use it to restore the original image. For both cases, it is necessary to know the actual parameters of the video camera. The method described in the article is based on the Rolling Shater and Wagon Wheel effects. © 2025 ieee.

关键词： Video recording

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RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D Object Detection in Autonomous Driving

RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D ...

引用

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

作者： Cao, Xingjian Wang, Ping Zhang, Zhitao Tu, Huizhao Chen, Yong Liang, Zhenbao College of Electronic and Information Engineering Tongji University Shanghai China College of Transportation Tongji University Shanghai China Co. Ltd. Ningbo China

ISBN: (纸本)9798350368741

Accurate and reliable 3D object detection is crucial for autonomous driving, normally achieved using camera-only or camera-LiDAR fusion methods based on BEV (Bird's Eye View) perspective. However, visual perception through cameras alone faces significant challenges, such as ambiguous depth estimation and poor performance in low-light, while camera-LiDAR fusion, though robust, are expensive on deployment and have limitations in dust and fog weather conditions. To address these issues, we propose RAFDet (Radar-Assisted Fusion Detection), a novel multi-modality fusion framework integrating camera and radar data for enhanced 3D detection. RAFDet utilizes radar RF (Radio Frequency) images to enrich spatial details, boosting fusion effects, while radar point clouds provide precise depth information to rectify visual depth predictions. In addition, the Edge-Awareness Feature Enhancement mechanism compensates for the sparsity of radar points, further refining depth estimation and detection accuracy. Our extensive experiments demonstrate the superiority of RAFDet over current methods, highlighting its potential for autonomous driving systems. Key results show significant improvements in depth prediction and overall performance metrics, validating the effectiveness of our approach. Our data collection and testing in real traffic scenarios also reflects the robustness, accuracy, and cost-effectiveness of RAFDet for 3D object detection in autonomous driving. © 2025 ieee.

关键词： 3D object detection Autonomous driving Camera-radar fusion Depth-rectification view transformation Edge-awareness feature enhancement Radar RF image

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Santhuja, P. Suresh, A. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management. © 2025 ieee.

关键词： Electronic Waste

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SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

引用

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

作者： Wang, Yijun Wang, Yong Xu, Chendong Yao, Shuai Wu, Qisong Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350368741

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD. © 2025 ieee.

关键词： Fine-Tuning Human Activity Recognition Time-Doppler Vision Transformer

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IRSEnet: Differentially Private image Generation with Multi-Scale Feature Extraction and Residual Channel Attention 13

IRSEnet: Differentially Private Image Generation with Multi-...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Li, Jiahao Wang, Zhongshuai Ghazali, Kamarul Hawari Bin Yan, Suqing Lan, Rushi Sun, Xiyan Luo, Xiaonan Guangxi Key Lab. of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Centre for Advanced Industrial Technology University of Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Int. Joint Research Lab. of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9798331516147

Privacy-preserving image generation is particularly crucial in fields like healthcare, where data are both sensitive and limited. However, effective privacy preservation often compromises the visual quality and utility of the generated images due to privacy budget constraints. To address this issue, in this paper, We propose a novel network architecture, IRSEnet, which combines multi-scale feature extraction technology and residual channel attention mechanisms, aiming to enhance the visual quality of generated images and improve the performance of downstream classification tasks under differential privacy. The differential privacy mechanism ensures the security of sensitive data during training, while the multi-scale feature extraction module enhances feature extraction capabilities through parallel convolutional layers at multiple scales. Additionally, the channel attention module dynamically adjusts channel weights to focus on the most discriminative features. Experimental results demonstrate that this model significantly improves the utility of generated images and the accuracy of downstream classification tasks while preserving privacy. Future work will explore the application of this approach on larger datasets and across more diverse tasks. © 2025 ieee.

关键词： Differential privacy

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Intelligent Computing System for Breast Cancer Identification utilizing U-Net for Digital image Segmentation 3

Intelligent Computing System for Breast Cancer Identificatio...

引用

3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Goli, Ranadheer Reddy Jadala, Vijaya Chandra Jamalpur, Bhavana Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep Learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This Intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis. © 2025 ieee.

关键词： Breast Cancer Intelligent System Segmentation U-Net

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Fusion of Sparse Direct Method and Feature-based Approach in Stereo Point-Line SLAM Algorithm

Fusion of Sparse Direct Method and Feature-based Approach in...

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2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Wang, Hao Xu, Zhen Leng, Kuang China Nanhu Academy of Electronics and Information Technology Zhejiang Jiaxing China School of Electronic Engineering and Computer Science University of Science and Technology of China Anhui Hefei China School of Management University of Science and Technology of China Anhui Hefei China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The development of autonomous driving and augmented reality has led to increased demands for accuracy and speed in visual SLAM. Traditional feature-based visual SLAM performs real-time image analysis with high complexity and computation requirements, necessitating devices with exceptionally high arithmetic power. This poses challenges for deployment on low-power devices. Additionally, relying solely on photometric errors makes the system too sensitive to the environment. To tackle these issues, our algorithm integrates the feature-based method and the sparse direct method. We employ the sparse joint method for pose estimation in the tracking thread, considering both accuracy and speed. In case of tracking failure, we extract feature point lines for tracking. Furthermore,to enhance system robustness in low-texture environments and fuse point-line features, we employ adaptive weighted extraction of point-line features, considering computational resources. Our experimental results, based on public datasets, demonstrate that this algorithm achieves faster operation speeds in simple environments and higher accuracy compared to other SLAM algorithms in complex environments and fast camera movement scenes. © 2025 SPIE.

关键词： Stereo image processing

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image Analysis for Historical Knowledge Discovery and Preservation

Image Analysis for Historical Knowledge Discovery and Preser...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Sakthivel, K. Ashwin, J. Poongodi, K. Oviya, S. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Digitized archival collections offer rich sources for cultural heritage research. Current systems, though, fail to effectively integrate heterogeneous data forms like images, textual descriptions, and metadata. This study offers a sophisticated framework that blends image recognition, natural language processing (NLP), and knowledge graph methods to facilitate access, enable discovery, and reveal concealed historical patterns. The system comprises several modules such as a data input module, preprocessing pipeline, image recognition, NLP analysis, knowledge graph generation, semantic search engine, and an interactive user interface. Testing the system on a large historical archive dataset proved high accuracy, efficiency, and usability for all modules. Major takeaways are 93.4% accuracy in object detection for images, 94.8% accuracy for named entity recognition, and 95.5% search accuracy in semantic search feature. © 2025 ieee.

关键词： Usability engineering

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Smart Sports Video Tracking and Safety Solutions Integrating GPS and Satellite communications 4

Smart Sports Video Tracking and Safety Solutions Integrating...

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

作者： Ying, Sui Tianjin Vocational College of Communications Tianjin300110 China

ISBN: (纸本)9798331523923

As the system of intelligent sensing and video surveillance continues to improve, its application in different fields has also emerged. In this study, the smart sports video tracking and safety solutions integrating the GPS and satellite communications is discussed. The core goal of this research is to address the needs of athlete tracking and safety in complex environments. The designed system consists of three core modules, namely, efficient video tracking module, high-precision GPS tracking module and stable satellite communication module. First, the proposed video tracking module uses a Laplace operator-based deblurring algorithm for video image processing. Subsequently, the human target in the moving video is accurately tracked through the two-dimensional flow field distribution. As a global optimization, the edge similarity feature segmentation is used to achieve dynamic marker tracking. The designed GPS tracking module uses the single difference observation method to improve positioning accuracy. This module combines the direct rounding fuzzy solution to effectively solve the coordinate conversion and measurement error problems. Finally, the satellite communication module used works through the coordination of the main control machine, main controller and signal switching module. This architecture realizes efficient data transmission and processing. Through experimental verification, the effectiveness of this tracking and assurance framework can be explored. © 2025 ieee.

关键词： Satellite communication systems

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