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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2177 条记录，以下是91-100 订阅

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PulmoNetX: A Hybrid vision Transformer Approach for Multi-scale Spatial Feature Reduction in Pneumonia Classification 27th

PulmoNetX: A Hybrid Vision Transformer Approach for Multi-sc...

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27th International conference on pattern recognition, ICPR 2024

作者： Lasker, Asifuzzaman Ghosh, Mridul Obaidullah, Sk Md Chakraborty, Chandan Roy, Kaushik Pal, Umapada Department of Computer Science and Engineering Aliah University Kolkata700160 India Department of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah711312 India Department of Computer Science and Engineering National Institute of Technical Teachers’ Training and Research Kolkata700106 India Department of Computer Science West Bengal State University Barasat700126 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783031781650

An innovative deep learning structure, PulmoNetX, integrates the capabilities of Convolutional Neural Networks (CNNs) and vision Transformers (ViTs) to enhance pneumonia detection in chest X-ray imagery. During preprocessing, images are normalized in size, converted to grayscale, and subjected to contrast amplification to emphasize essential features. PulmoNetX employs a hybrid methodology to capture both the local and global characteristics of images, leading to significant advancements in diagnosing different pneumonia types, such as COVID-19-induced, viral, and bacterial pneumonia. Comparative studies reveal that PulmoNetX surpasses leading vision Transformer models in terms of precision, recall, F1-score, and overall accuracy, highlighting its advanced processing abilities and its promise as an effective diagnostic tool in X-ray lung disease detection. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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7th International conference on Wireless Communications, Networking and Applications, WCNA 2023

7th International Conference on Wireless Communications, Net...

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7th International conference on Wireless Communications, Networking and Applications, WCNA 2023

ISBN: (纸本)9789819624089

the proceedings contain 38 papers. the special focus in this conference is on Wireless Communications, Networking and Applications. the topics include: EffiNet: An Efficient, Low-Complexity Neural Network for Monocular Depth Estimation on Embedded Devices;adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection;a Cache Scheduling Method Based on Adaptive Expiration for Data Process System;Research on Airport Communication and Navigation Equipment Inspection Assistance System Based on AR Technology;application of Quality Function Deployment in Assessing Unmanned Assault Capabilities for Mountain Counter-Terrorism Operations;research on the Application of Internet of things Technology in Power Grid Limited Space Security Monitoring;research on the Application of computer vision Based on Deep Learning in Autonomous Driving Technology;research on Cybersecurity System of Scientific Research Institutions Based on National Cybersecurity Classified Protection 2.0: Take the chinese Academy of Agricultural Sciences as an Example;Research on DOA Estimation Method of Underwater Acoustic Signal Based on Machine Learning;Design and Implementation of Smart Home Control System Based on STM32;communication Anti-jamming System Based on Deep Reinforcement Learning;fault Diagnosis of Infrared Sensor Based on Convolutional Neural Network;graph Neural Network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information;an Approach to Microenvironment-Based Particle Swarm Optimization Algorithm;a Verifiable Ciphertext Retrieval Scheme for Smart Grids;panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap;An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN;recognition and Calculation of Fish Rafts in Mariculture on the Basis of Artificial Intelligence;digital Twin for Power Load Forecasting.

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PCGAUNet: Pixel Correlation and Gaussian Attention Driven Network for Text Segmentation 27th

PCGAUNet: Pixel Correlation and Gaussian Attention Driven Ne...

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27th International conference on pattern recognition, ICPR 2024

作者： Roy, Ayush Palaiahnakote, Shivakumara Pal, Umapada Antonacopoulos, Apostolos Ramachandra, Raghavendra Computer Vision and Pattern Recognition Indian Statistical Institute Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783031784460

Text-line segmentation is still considered challenging for complex background scene images. the success of text detection and recognition depends on the success of the text segmentation. this study presents a new method for text segmentation to facilitate reliable detection and recognition. therefore, we introduce a new model called Pixel Correlation and Gaussian Attention Driven Network (PCGAUNet) for text segmentation. To extract pixel correlation, we modified the MultiResUnet architecture, which leverages pixel-wise correlation to effectively highlight foreground pixels. In addition, the proposed model utilizes the prior spatial statistics of bottleneck features to create a learnable Gaussian distribution, which guides the decoder for accurate text segmentation. Experimental results on three standard scene text segmentation datasets, ICDAR13 FST, Total Text, and COCO-TS, show that the proposed model outperforms existing methods. Furthermore, the results for the underwater dataset UTS-55 show that our model is robust and generic. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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MSAOT: Enhancing Video Object Segmentation with Memory-Selective Updates and High-Quality Mask Regeneration 24

MSAOT: Enhancing Video Object Segmentation with Memory-Selec...

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13th International conference on Computing and pattern recognition, ICCPR 2024

作者： Du, Yinglong Luo, Yifei Zhao, Xu Yu, Tao Zhu, Guibo Wang, Jinqiao Foundation Model Research Center Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400717482

Video Object Segmentation (VOS) is an important computer vision task that involves tracking and segmenting objects across video frames. the mainstream VOS method typically employs long and short-term memory to aid the model in retaining the object's feature representation. However, current state-of-the-art methods do not adequately address the issue of information loss and contamination when updating long-term memory. To address these issues, we propose an innovative method MSAOT, including an object tracker with a memory-selective update strategy and high-quality mask regeneration for post-process. this thereby assists in subsequent sequence predictions and enhances model accuracy. MSAOT employs an object tracker with the mutual similarity evaluation for long-term memory management and leverages HQ-SAM for high-quality mask generation. Extensive experiments show that our method's long-term memory successfully preserves information from all stages of the video sequence, particularly during the mid to late phases of prediction. the richness of the stored memory information is instrumental in improving the model's performance. the experimental results on the VOT2020 benchmark highlight that MSAOT outperforms the current state-of-the-art by a notable margin of 1.1% in Expected Average Overlap (EAO). © 2024 Copyright held by the owner/author(s).

关键词： Memory management

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BridgeCLIP: Automatic Bridge Inspection by Utilizing vision-Language Model 27th

BridgeCLIP: Automatic Bridge Inspection by Utilizing Vision...

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27th International conference on pattern recognition, ICPR 2024

作者： Liao, Powei Nakano, Gaku Waseda University Tokyo Japan NEC Corporation Tokyo Japan

ISBN: (纸本)9783031784460

We propose BridgeCLIP, an innovative framework designed to harness the power of vision-language models for bridge inspection from images. BridgeCLIP is a CLIP-based multi-label classifier that finds multiple damages in a single bridge image. Pre-trained vision-language models learn the relationships between general objects by millions of text-image pairs, of which descriptions are not precise enough for domain-specific problems. Following the concept that humans normally learn the visual appearance of bridge damage by reading a manual, we introduce a novel Description Attention Module (DAM) to incorporate the domain-specific knowledge extracted from the professional descriptions in bridge inspection manuals. By utilizing both general knowledge of the pre-trained CLIP and professional knowledge of bridge inspection, BridgeCLIP comprehensively learns the inter-class relationships of different damages. Experimental results on bridge inspection datasets show that BridgeCLIP outperforms the state-of-the-art multi-label classifiers. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Visual languages

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DualViT: A Hierarchical vision Transformer for Broad and Fine Class Embeddings 27th

DualViT: A Hierarchical Vision Transformer for Broad and F...

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27th International conference on pattern recognition, ICPR 2024

作者： Chatterjee, Ankita Dutta, Sandip Mukhopadhyay, Jayanta Das, Partha Pratim Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science Ashoka University Haryana Sonipat India

ISBN: (纸本)9783031781650

Human brains are natural learning systems which inherently recognise image objects in a hierarchical pattern. Similar association exists among different categories of images which interact while training a deep learning model, leading to misclassification and indistinct features. As semantically related classes form clusters in the embedding space, we can list these classes under a single aggregate class, and make the learning faster by leveraging this knowledge while training a model. In this paper, we introduce a hierarchical architecture that simultaneously learns both broad-class and fine-class embeddings using transformer-based encoders. these encoders are trained alternately, and the hierarchical knowledge is encoded through tensor product between the coarse and fine class representations. Our approach has been able to achieve state-of-the-art performance on benchmark datasets with very less epochs. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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SwiftFaceFormer: An Efficient and Lightweight Hybrid Architecture for Accurate Face recognition Applications 27th

SwiftFaceFormer: An Efficient and Lightweight Hybrid Archit...

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27th International conference on pattern recognition, ICPR 2024

作者： Luevano, Luis S. Martínez-Díaz, Yoanna Méndez-Vázquez, Heydi González-Mendoza, Miguel Frey, Davide Univ Rennes Inria CNRS IRISA Rennes France 7A #21406 Siboney Havana Playa12200 Cuba School of Engineering and Sciences Tecnologico de Monterrey Nuevo León Monterrey64849 Mexico

ISBN: (纸本)9783031783401

With the growing breakthrough of deep learning-based face recognition, the development of lightweight models that achieve high accuracy with computational and memory efficiency has become paramount, especially for deployment on embedded domains. While vision Transformers have shown significant promising results in various computer vision tasks, their adaptability to resource-constrained devices remains a significant challenge. this paper introduces SwiftFaceFormer, a new efficient, and lightweight family of face recognition models inspired by the hybrid SwiftFormer architecture. Our proposal not only retains the representational capacity of its predecessor but also introduces efficiency improvements, enabling enhanced face recognition performance at a fraction of the computational cost. We also propose to enhance the verification performance of our original most lightweight variant by using a training paradigm based on Knowledge Distillation. through extensive experiments on several face benchmarks, the presented SwiftFaceFormer demonstrates high levels of accuracy compared to the original SwiftFormer model, and very competitive results with respect to state-of-the-art deep face recognition models, providing a suitable solution for real-time, on-device face recognition applications. Our code is available at https://***/Inria-CENATAV-Tec/SwiftFaceFormer. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Efficient Face Transformer Efficient vision Transformer Knowledge Distillation Lightweight Face recognition

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4th chinese conference on pattern recognition and computer vision, PRCV 2021

4th Chinese Conference on Pattern Recognition and Computer V...

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4th chinese conference on pattern recognition and computer vision, PRCV 2021

ISBN: (纸本)9783030880064

the proceedings contain 201 papers. the special focus in this conference is on pattern recognition and computer vision. the topics include: Relation-Guided Actor Attention for Group Activity recognition;MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-identification;joint Attention Mechanism for Unsupervised Video Object Segmentation;foreground Feature Selection and Alignment for Adaptive Object Detection;exploring Category-Shared and Category-Specific Features for Fine-Grained Image Classification;deep Mixture of Adversarial Autoencoders Clustering Network;SA-InterNet: Scale-Aware Interaction Network for Joint Crowd Counting and Localization;conditioners for Adaptive Regression Tracking;attention Template Update Model for Siamese Tracker;3D Multi-object Detection and Tracking with Sparse Stationary LiDAR;insight on Attention Modules for Skeleton-Based Action recognition;AO-AutoTrack: Anti-occlusion Real-Time UAV Tracking Based on Spatio-temporal Context;two-Stage recognition Algorithm for Untrimmed Converter Steelmaking Flame Video;scale-Aware Multi-branch Decoder for Salient Object Detection;densely End Face Detection Network for Counting Bundled Steel Bars Based on YoloV5;POT: A Dataset of Panoramic Object Tracking;DP-YOLOv5: computer vision-Based Risk Behavior Detection in Power Grids;distillation-Based Multi-exit Fully Convolutional Network for Visual Tracking;handwriting Trajectory Reconstruction Using Spatial-Temporal Encoder-Decoder Network;scene Semantic Guidance for Object Detection;CRNet: Centroid Radiation Network for Temporal Action Localization;training Person Re-identification Networks with Transferred Images;ACFIM: Adaptively Cyclic Feature Information-Interaction Model for Object Detection;research of Robust Video Object Tracking Algorithm Based on Jetson Nano Embedded Platform;classification-IoU Joint Label Assignment for End-to-End Object Detection;joint Learning Appearance and Motion Models for Visual Tracking;reFl

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4th chinese conference on pattern recognition and computer vision, PRCV 2021

4th Chinese Conference on Pattern Recognition and Computer V...

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4th chinese conference on pattern recognition and computer vision, PRCV 2021

ISBN: (纸本)9783030880033

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4th chinese conference on pattern recognition and computer vision, PRCV 2021

4th Chinese Conference on Pattern Recognition and Computer V...

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4th chinese conference on pattern recognition and computer vision, PRCV 2021

ISBN: (纸本)9783030880095

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