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检索条件"任意字段=2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition, CVIPPR 2023"

共 327 条记录，以下是1-10 订阅

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Proceedings of the 2023 asia conference on computer vision, image processing and pattern recognition, cvippr 2023

Proceedings of the 2023 Asia Conference on Computer Vision, ...

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2023 asia conference on computer vision, image processing and pattern recognition, cvippr 2023

ISBN: (纸本)9798400700033

The proceedings contain 14 papers. The topics discussed include: improved mask R-CNN network method for PV panel defect detection;skeleton-based generative adversarial networks for font shape style transfer: learning text style from some characters and transferring the style to any unseen characters;3D ultrasound tomography image reconstruction algorithm by GPU;self-attention in U-Net for semantic segmentation of low resolution SAR images;medical image segmentation approach via transformer knowledge distillation;improving object detection robustness against natural perturbations through synthetic data augmentation;application of visible light-infrared image fusion technology in power system fault detection;deep object detection for complex architectural floor plans with efficient receptive fields;self-adaptive methods with flexible detection criteria of the battery cell dent defect;remaining useful life prediction via K-means clustering analysis and deep convolutional neural network;hierarchical attention based feature learning for interpretable social event prediction;and analysis of fuzzy network selection algorithm for heterogeneous wireless mobile networks.

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cvippr 2024 - 2nd asia conference on computer vision, image processing and pattern recognition

CVIPPR 2024 - 2nd Asia Conference on Computer Vision, Image ...

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2nd asia conference on computer vision, image processing and pattern recognition, cvippr 2024

ISBN: (纸本)9798400716607

The proceedings contain 59 papers. The topics discussed include: 3D point clouds simplification based on low-dimensional contour feature extraction;3D human pose estimation using pressure images on a smart chair;combining doses from internal and external radiotherapies for cervical cancer with successive image registration;attention mechanism-based feature fusion generative network for infrared-visible person re-identification;a vision-based remote assistance method and its application in object transfer;research on model-free 6D object pose estimation based on vision 3D matching;active exploration of modality complementarity for multimodal sentiment analysis;self-attention-based multi-scale feature fusion network for road ponding segmentation;and low light image enhancement algorithm based on edge and color information.

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8th National conference on computer vision, pattern recognition, image processing, and Graphics, NCVPRIPG 2023

8th National Conference on Computer Vision, Pattern Recognit...

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8th National conference on computer vision, pattern recognition, image processing, and Graphics, NCVPRIPG 2023

ISBN: (纸本)9789819752119

The proceedings contain 18 papers. The special focus in this conference is on computer vision, pattern recognition, image processing, and Graphics. The topics include: RGB to Chlorophyll Fluorescence image Reconstruction of Maize for Plant Phenotyping;novel Unsupervised Disease Segmentation Framework Based on Clustering;YOLOv5-MasselNet: In-Field Maize Tassel Detection and Density Estimation Using YOLOv5;MuSiC V1.0: A Software Solution for Automated Mustard Silique Count Using YOLOv5;taPaSe: Tanjore Paddy Seed Dataset;processing 3D Point Clouds for High-Throughput Plant Phenotyping;a Deep Learning-Driven Contactless Finger recognition System;deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?;unlocking Communication: Exploring Hand Gesture-Based Interaction in a Code-Mixed Conversational Speech-to-Indian-Sign-Language Translator;a Touchless Palm-Photo recognition System for Mobile and Handheld Devices;detection of Palmprint from Contactless Smartphone-Based Video Hand Dataset;improved Localization of Knuckle Regions for Contactless Acquisition;NCVPRIPG 2023 Summer Challenge on Writer Verification;C4MTS: Challenge on Categorizing Missing Traffic Signs from Contextual Cues;dePondFi’23 Challenge Summary;a Challenge on 3D Reconstruction and Restoration of Indian Heritage from Partial and Noisy Scans.

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Innovating Tourism: Personalized Recommendations through AI 2

Innovating Tourism: Personalized Recommendations through AI

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Palahan, Sirinda Arunthari, Santipat Univ Thai Chamber Commerce Sch Sci & Technol Bangkok Thailand

ISBN: (纸本)9798400716607

This paper delves into the development and deployment of an AI-powered chatbot aimed at enhancing tourism experiences. By integrating advanced natural language processing and machine learning techniques, the chatbot offers personalized travel recommendations and information, serving as a versatile tool for tourists. Through experimental evaluation involving intent classification and user satisfaction, the chatbot demonstrates significant accuracy in understanding user queries and a positive impact on user satisfaction. Intent classification using DistilBERT achieved a high F1-score, indicating superior understanding capabilities. User feedback highlighted satisfaction and likelihood of reuse, pointing to areas for further refinement in response relevance.

关键词： Chatbot Tourism Natural Language processing Intent Classification

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Pixel Power: Harnessing image processing for Optimizing Few-Shot Malicious Traffic Detection 2

Pixel Power: Harnessing Image Processing for Optimizing Few-...

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Wang, Yihan Yan, Ruiyi Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798400716607

The rapid proliferation of cyber threats in an increasingly digitized world presents a formidable challenge to existing cybersecurity mechanisms of which malicious traffic is a noticeable issue. However, feature extraction of malicious traffic is often hindered by insufficient data, and thus training effectiveness and efficiency are compromised. To address the challenge of detecting malicious traffic with small sample sizes, we introduce a meta regularization network, MRN. Our method leverages meta-learning techniques alongside convolutional neural networks to capture intricate traffic patterns, so as to classify processed encrypted traffic data. Within MRN, we introduce a novel meta-learning regularization strategy that adjusts the training process's loss based on network parameters, in order to improve computing efficiency. Our comprehensive experimental evaluations reveal the model's exceptional capability in malicious traffic classification, achieving 95.11% accuracy averagely, which is higher than the baseline methods under various experimental setup. This underscores our framework's potential in advancing the field of cybersecurity through image-based analysis and small-sample classification techniques.

关键词： few-shot meta learning deep learning network traffic

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Judicial Text Relation Extraction Based on Prompt Tuning 24

Judicial Text Relation Extraction Based on Prompt Tuning

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Chen, Xue Li, Yi Fan, Shuhuan Hou, Mengshu Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798400716607

With the acceleration of the digital transformation in the judicial field, the need for automated processing and analysis of voluminous legal documents becomes increasingly imperative. Relationship extraction, a pivotal step in comprehending these documents, is crucial for automated case analysis, discovery of legal facts, and prediction of judgments. However, the idiosyncrasies of judicial texts, such as their intricate linguistic structures, specialized terminology, and implicit semantic relationships, present significant challenges to the efficacy of traditional text analysis methods. Traditional relation extraction techniques often fail to adapt well to professional terms, intricate linguistic structures and implicit semantics in judicial texts. Moreover, the general lack of training data tailored to the specific context of the legal domain hampers the ability of models to learn effectively and accurately extract relationships from judicial texts. This paper introduces a novel approach utilizing pre-trained models for prompt-based fine-tuning to achieve relationship extraction in judicial texts. Leveraging the robust language comprehension capabilities of large-scale pre-trained models, we have designed domain-specific prompt templates to guide the model in more effectively capturing and understanding key information within legal texts. This method not only enhances the accuracy of relationship extraction but also significantly reduces reliance on extensive annotated data. The effectiveness and superiority of our approach are corroborated by test results on a judicial text dataset.

关键词： relation extraction pre-training model prompt-tuning judicial text processing

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ELiFormer: A hierarchical Transformer based Model with Efficient Encoder and Lightweight Decoder for Semantic Segmentation 2

ELiFormer: A hierarchical Transformer based Model with Effic...

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Wu, Zixuan Zhou, Yue Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798400716607

Fully-convolutional networks have limited global information modeling, while Transformers can be computationally demanding. This paper introduces ELiFormer, a lightweight model combining Transformer and CNN components for semantic segmentation tasks. We start by using a lightweight inverted residual module to extract initial features, aiming to reduce computational complexity and improve model efficiency. Within encoder module, we adopt depth-wise separable convolution for flexible position embedding, notably improving segmentation accuracy for small objects. We also employs two self-attention mechanisms: local window multi-head self-attention and global window multi-head self-attention, which generate multi-scale feature maps, enhancing the encoder's ability to capture complex relationships. Dropout layers are introduced to reduce computational demand during training. To address semantic disparity between CNN and visual Transformer, we introduce fusion block. Finally, we utilize a lightweight MLP decoder to streamline the model and ensure robust segmentation performance. Our extensive ablation study shows that ELiFormer attains significant results for semantic segmentation, which is competitive on Cityscapes datasets.

关键词： Semantic segmentation Hierarchical vision Transformer Lightweight

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Real-Time 3D Skeleton Reconstruction: A Comprehensive Approach from Multiple Views 24

Real-Time 3D Skeleton Reconstruction: A Comprehensive Approa...

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Wilser, Nicola Cordier, Frederic Maillot, Yvan Souchet, Philippe Univ Haute Alsace Bry Sur Marnes France XD Prod Bry Sur Marnes France Univ Haute Alsace Mulhouse France

ISBN: (纸本)9798400716607

This paper presents a comprehensive method for real-time 3D human skeleton reconstruction from calibrated camera sets, addressing challenges in scenes with multiple individuals. Accurate 3D pose estimation is crucial for various applications such as 3D model animation, augmented reality, and human-computer interaction. The approach involves initial 2D skeleton estimation, followed by skeleton identification through a matching algorithm and reconstruction via triangulation. Three key enhancements were implemented: refining the matching algorithm using 3D reconstruction reprojection, accelerating execution with skeleton tracking, and validation on a diverse dataset with over 9,000 frames. The method achieves accurate 3D reconstruction and robust performance in multi-individual scenarios, making it suitable for real-world applications. Project page: https://instant- ***.

关键词： 3D Human Pose Estimation Temporal Coherence in 3D Reconstruction Real-time 3D Skeleton Reconstruction Multi-view image processing

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Sitting Posture recognition Based on the computer's Camera 2

Sitting Posture Recognition Based on the Computer's Camera

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Zhao, Songyin Su, Yang Engn Univ PAP Xian Peoples R China

ISBN: (纸本)9798400716607

The popularity of computers has made sitting a normal part of people's lives. Since unhealthy sitting postures not only harm the eyes but also increase the risk of various musculoskeletal diseases, sitting posture detecting and warning is very necessary to help people develop healthy sitting habits. Thus, a cheaper and more convenient method using an ordinary camera is proposed in this paper for 8 kinds of sitting posture. Restricted by the view of camera, the positions of nose, eyes, ears and shoulders are extracted using MoveNet, as the input of a Multi-layer Perceptron (MLP) classifier. To train the classifier, the user needs to record a video for each sitting posture. In the process of recording, guidelines are provided to improve the quality of training data. For this study, a small dataset including 12 groups of videos recorded using 2 sets of table and chair was collected. The relatively good MLP with (64,64) as hidden layer sizes and 0.162378 as alpha are found by GridSearchCV, using Leave P Group(s) Out cross-validator as the cross-validation splitting strategy. Overall, the average accuracy of this method for the dataset is 95.8%, while 97.3% for the same set of table and chair and 94.1% for the different set. These results mean that is feasible to recognize the sitting postures using ordinary camera, which implies a more easily available sitting posture warning technology.

关键词： Posture detection image classification MoveNet Multi-layer Perceptron

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Self-Attention-based Multi-Scale Feature Fusion Network for Road Ponding Segmentation 2

Self-Attention-based Multi-Scale Feature Fusion Network for ...

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Yang, Shangyu Zhang, Ronghui Sun, Wencai Chen, Shengru Ye, Cong Wu, Hao Li, Mengran Sun Yat Sen Univ Sch Intelligent Syst Engn Guangdong Prov Key Lab Intelligent Transport Syst Guangzhou 510275 Peoples R China Jilin Univ Coll Transportat Changchun 130025 Peoples R China

ISBN: (纸本)9798400716607

Road ponding, an omnipresent anomaly in traffic scenarios, poses a substantial hazard to road safety, manifesting as potential vehicular loss of control, minor abrasions, or severe collisions. Its transparency renders road ponding susceptible to environmental influences, presenting a formidable challenge for precise edge detection. Existing methodologies exhibit shortcomings in accurately discerning road ponding. In response, we propose an innovative approach called Fusformer (Self-Attention-based Multi-Scale Feature Fusion Network). Representing the pioneering application of Transformer architecture in road ponding detection, Fusformer integrates an enhanced self-attention mechanism derived from Segformer. This mechanism adeptly captures highly non-local contextual attention, thereby facilitating improved identification of ponding edges. Augmenting this approach, we incorporate a multi-scale feature fusion module to mitigate semantic disparities between high-resolution coarse features and low-resolution fine features. This strategy enhances feature interactions, preventing the loss of intricate ponding details and yielding more precise segmentation results. This work contributes valuable insights to proactive warning research in the realm of road traffic safety.

关键词： vision-based Self-attention Feature Fusion Proactive traffic safety

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