PRICAI 2023: Trends in Artificial Intelligence : 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023, Jakarta, Indonesia, November 15–19, 2023, Proceedings, Part III / edited by Fenrong Liu, Arun Anand Sadanandan, Duc Nghia Pham, Petrus Mursanto, Dickson Lukose.

Format:

Book

Author/Creator:

Liu, Fenrong.

Contributor:

Sadanandan, Arun Anand.

Pham, Duc-Nghia.

Mursanto, Petrus.

Lukose, Dickson.

Series:

Lecture Notes in Artificial Intelligence, 2945-9141 ; 14327

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Computer engineering.

Computer networks.

Application software.

Image processing--Digital techniques.

Image processing.

Computer vision.

Artificial Intelligence.

Computing Milieux.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Local Subjects:

Artificial Intelligence.

Computing Milieux.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (514 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

This three-volume set, LNCS 14325-14327 constitutes the thoroughly refereed proceedings of the 20th Pacific Rim Conference on Artificial Intelligence, PRICAI 2023, held in Jakarta, Indonesia, in November 2023. The 95 full papers and 36 short papers presented in these volumes were carefully reviewed and selected from 422 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc.

Contents:

Intro

Preface

Organization

Contents - Part III

Vision and Perception

A Multi-scale Densely Connected and Feature Aggregation Network for Hyperspectral Image Classification

1 Introduction

2 Proposed Method

2.1 Spectral-Spatial Feature Extraction Module

2.2 Multi-scale Feature Extraction Module

2.3 Multi-level Feature Aggregation Module

3 Experiment and Analysis

3.1 Dataset Description and Experiment Setup

3.2 Experiment Results and Analysis

3.3 Parametric Analysis

3.4 Ablation Experiments

4 Conclusion

References

A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators

1 Introduction and Motivation

2 Related Work

2.1 GANs-Based Blind SR Methods

2.2 Discriminator Models

3 Method

4 Experiments

4.1 Implementation Details

4.2 Testsets and Experiment Settings

4.3 Comparing with the State-of-the-Arts

4.4 Attention Block Analysis

4.5 Multi-scale Discriminator Analysis

4.6 Ablation Study

5 Conclusions

AI-Based Intelligent-Annotation Algorithm for Medical Segmentation from Ultrasound Data

1.1 Contributions

1.2 Related Work

2 Methodology

2.1 Workflow

2.2 Adaptive Polygon Tracking (APT) Model

2.3 Historical Storage-Based Quantum-Inspired Evolutionary Network (HQIE)

2.4 Mathematical Model-Based Contour Detection

3 Experiment Setup and Results

3.1 Databases

3.2 Performance on the Testing Dataset Disturbed by Noise

3.3 Ablation Study

3.4 Comparison with State-Of-The-Art (SOTA) Models

An Automatic Fabric Defect Detector Using an Efficient Multi-scale Network

3 Proposed Model EMSD

3.1 LSC-Darknet

3.2 DCSPPF

3.3 LSG-PAFPN

3.4 Detection Head

4.1 Setup

4.2 Datasets.

4.3 Evaluation Metrics

4.4 Comparison Experiment Results

4.5 Ablation Experiments

4.6 Visualization of Detection Results

5 Conclusion

An Improved Framework for Pedestrian Tracking and Counting Based on DeepSORT

2 FR-DeepSort for Pedestrian Tracking and Counting

2.1 The FR-DeepSORT Framework

2.2 Pedestrian Tracking

2.3 Pedestrian Counting

3 Experiments

3.1 Analysis of Pedestrian Tracking Results

3.2 Analysis of Pedestrian Counting Results

Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement

2.1 Learning-Based Methods in LLIE

2.2 Diffusion Models

3 Methodology

3.1 Curve Estimation for High Resolution Image

3.2 Bootstrap Diffusion Model for Better Curve Estimation

3.3 Denoising Module for Real Low-Light Image

4.1 Datasets Settings

4.2 Comparison with SOTA Methods on Paired Data

4.3 Comparison with SOTA Methods on Unpaired Data

4.4 Ablation Study

5 Conclusion and Limitation

CoalUMLP: Slice and Dice! A Fast, MLP-Like 3D Medical Image Segmentation Network

2 Method

2.1 Overview

2.2 Multi-scale Axial Permute Encoder

2.3 Masked Axial Permute Decoder

2.4 Semantic Bridging Connections

3 Experiment

3.1 Dataset

3.2 Implement Details

3.3 Comparison with SOTA

3.4 Ablation Study

Enhancing Interpretability in CT Reconstruction Using Tomographic Domain Transform with Self-supervision

2.1 Radon Transform in CT Imaging

2.2 CT Reconstruction Using Tomographic Domain Transform with Self-supervision

3 Experimental Results

3.1 Datasets and Experimental Settings

3.2 Comparison Experiments

4 Conclusion.

References

Feature Aggregation Network for Building Extraction from High-Resolution Remote Sensing Images

3.1 Transformer Encoder

3.2 Feature Aggregation Module

3.3 Feature Refinement via Difference Elimination Module and Receptive Field Block

3.4 Dual Attention Module for Enhanced Feature Interactions

3.5 Fusion Decoder and Loss Function

4.1 Datasets

4.2 Implementation Details

4.3 Comparison with Other State-of-the-Art Methods

Image Quality Assessment Method Based on Cross-Modal

2.1 Deep Learning-Based Image Quality Assessment

2.2 Cross-Modal Techniques

3 Methods

3.1 Exploring the Feasibility of Cross-Modal Models

3.2 Image Quality Score Assessment Based on Cross-Modality

4.2 Experimental Details

4.3 Evaluation Metrics

4.4 Feasibility Research

4.5 Comparison Experiments

4.6 Ablation Experiments

6 Outlook

KDED: A Knowledge Distillation Based Edge Detector

2.1 Label Problems in Edge Detection

2.2 Knowledge Distillation

3.1 Compact Twice Fusion Network for Edge Detection

3.2 Knowledge Distillation Based on Label Correction

3.3 Sample Balance Loss

4.1 Datasets and Implementation

4.2 Comparison with the State-of-the-Art Methods

4.3 Ablation Study

Multiple Attention Network for Facial Expression Recognition

2.1 Real-Time Classification Networks

2.2 Attention Mechanism

3.1 Multi-branch Stack Residual Network

3.2 Transitional Attention Network

3.3 Appropriate Cascade Structure.

4 Experiments

4.2 Ablation Studies

4.3 Comparision with Previous Results

PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment

2 Related Works

3.1 Overview of the Proposed Model

3.2 Multi-scale Semantic Feature Extraction

3.3 Progressive Multi-Task Image Quality Assessment

4 Experiment

4.1 Experimental Setup

4.2 Performance Evaluation

Reduced-Resolution Head for Object Detection

3.1 Motivation and Analysis

3.2 Reduced-Resolution Head for Object Detection

4.1 Ablation Study

4.2 Applied to Other Detectors

Research of Highway Vehicle Inspection Based on Improved YOLOv5

2.1 YOLOv5 Model

2.2 The Improvement of YOLOv5

3.1 Ghostnet-C

3.2 GSConv+Slim-Neck

3.3 CAS Attention Mechanism

4 Experiment and Metrics

4.1 Experimental Environment and Data Set

4.2 Metrics

4.3 Experiment and Experimental Analysis

STN-BA: Weakly-Supervised Few-Shot Temporal Action Localization

3.1 Feature Extractor

3.2 Similarity Generator

3.3 Video-Level Classifier

3.4 Localization and Boundary-Check Algorithm

4.1 Experiment Setup

4.2 Main Experimental Results

4.3 Ablation Experiment

4.4 Generalization Test

SVFNeXt: Sparse Voxel Fusion for LiDAR-Based 3D Object Detection

2.1 Voxel-Based 3D Detectors

2.2 Fusion-Based 3D Detectors

2.3 Transformer-Based 3D Detectors

3 SVFNeXt for 3D Object Detection.

3.1 Dynamic Distance-Aware Cylindrical Voxelization

3.2 Foreground Centroid-Voxel Selection-Query-Fusion

3.3 Object-Aware Center-Voxel Transformer

3.4 Loss Functions

4.3 Main Results

Traffic Sign Recognition Model Based on Small Object Detection

2.1 Data Augmentation

2.2 Loss Function

2.3 Deep Learning For Small Object Detection

3.1 FlexCut Data Augmentation

3.2 Keypoint-Based PIoU Loss Function

3.3 The Proposed YOLOv5T

4.1 Dataset

4.2 Experimental Analysis

A Multi-scale Multi-modal Multi-dimension Joint Transformer for Two-Stream Action Classification

2 The Proposed Method

2.1 Training Schemes

3.1 Experimental Setups

3.2 Results and Discussions

3.3 Visualizations

4 Conclusions

Adv-Triplet Loss for Sparse Attack on Facial Expression Recognition

2.1 Problem Definition

2.2 Adv-Triplet Loss Function

2.3 Adv-Triplet Loss Search Attack

3 Experiments and Results

3.1 Sparsity Evaluation

3.2 Invisibility Evaluation

Credible Dual-X Modality Learning for Visible and Infrared Person Re-Identification

2.2 Dual-X Module

2.3 Uncertainty Estimation Algorithm

3.1 Experimental Settings

3.2 Ablation Study

3.3 Comparison with State-of-the-Art Methods

Facial Expression Recognition in Online Course Using Light-Weight Vision Transformer via Knowledge Distillation

4 Experiments Results

5 Conclusion.

References.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Liu, Fenrong PRICAI 2023: Trends in Artificial Intelligence

ISBN:

9789819970254

9819970253

OCLC:

1409702660