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[null,"1.","Record Nr.",null,"UNINA9910878065803321"]
[null,null,"Autore",null,"Huang De-Shuang"]
[null,null,"Titolo",null,"Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part XI / / edited by De-Shuang Huang, Yijie Pan, Qinhu Zhang"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9756-12-X"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (513 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14872"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part XI -- Intelligent Computing in Computer Vision -- Priority Intra-model Adaptation for Traffic Sign Detection and Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Priority Intra-model Adaptation -- 3.2 TSDR Models -- 3.3 TT100K-FineNet and GTSDB-FineNet -- 4 Experiment -- 4.1 Datasets -- 4.2 Evaluation Metrics and Implementation Details -- 4.3 Experimental Results -- 5 Discussion -- 6 Conclusion -- References -- Adaptive Swin Transformers for Few-Shot Cross-Domain Silent Face Liveness Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Network Architecture -- 3.3 Feature-Wise Transformation -- 4 Experiment -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Cross-Domain Performance -- 4.4 Ablation Study -- 5 Conclusion -- References -- DSFormer: Leveraging Transformer with Cross-Modal Attention for Temporal Consistency in Low-Light Video Enhancement -- 1 Introduction -- 2 Related Work -- 2.1 Low-Light Video Enhancement -- 3 Method -- 3.1 "]
[{"$nulls":4},"DSFormer Architecture -- 3.2 Flow Cross-Attention (FCA) -- 3.3 Spatial-Channel Multi-head Self-Attention (SCMA) -- 3.4 Dual Path Feed-Forward Network (DPFN) -- 4 Experiment -- 4.1 Implementation Detail -- 4.2 Static Video Evaluation -- 4.3 Dynamic Video Evaluation -- 4.4 Ablation Study -- 5 Conclusion -- References -- Robot Control Using Hand Gestures of the Mexican Sign Language -- 1 Introduction -- 2 Proposed Method -- 2.1 Segmentation Techniques -- 2.2 Feature Extraction -- 2.3 Feature Selection -- 2.4 Classification Techniques -- 2.5 Dataset -- 3 Experimental Results -- 4 Control Robot Method -- 4.1 Movement Orders Selection and Implementation -- 5 Conclusions -- References -- Improved Channel-Wise Semantic Alignment for Few-Shot Object Detection -- 1 Introduction."]
[{"$nulls":4},"2 Related Work -- 3 Problem Definition -- 3.1 Few-Shot Object Detection -- 3.2 Channel Attention -- 4 Our Method -- 4.1 Feature Purification -- 4.2 Sparse Channel Relation Distillation -- 5 Experiments -- 5.1 Datasets -- 5.2 Implementation Details -- 5.3 Comparison with the State-of-the-Arts -- 5.4 Ablation Study -- 6 Conclusion -- References -- Adapting Depth Distribution for 3D Object Detection with a Two-Stage Training Paradigm -- 1 Introduction -- 2 Related Work -- 2.1 Camera-Only 3D Object Detection -- 2.2 Depth Estimation -- 3 Preliminary -- 3.1 3D Object Detection -- 3.2 Multi-View Depth Estimation -- 3.3 LSS-Based 3D Object Detection Framework -- 4 Method -- 4.1 Two-Stage Training Paradigm -- 4.2 Depth Distribution Adaption -- 5 Experiment -- 5.1 Experimental Setup -- 5.2 Main Results -- 5.3 Ablation Study -- 5.4 Validation Test: The Impact of Depth Accuracy on Detection -- 6 Conclusion -- 6.1 Limitations -- References -- Domain Adaptive Object Detection with Dehazing Module -- 1 Introduction -- 2 Related Work -- 2.1 Image Dehazing -- 2.2 Object Detection -- 2.3 Domain Adaptive Object Detection -- 3 Methods -- 3.1 Network Overview -- 3.2 Dehazing Module -- 3.3 Domain Adaptation -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Experiment of Fog Removal Module -- 4.3 Experiment of DefogDA-FasterRCNN -- 5 Conclusions -- References -- Improving Dynamic 3D Gaussian Splatting from Monocular Videos with Object Motion Information -- 1 Introduction -- 2 Related Work -- 2.1 Dynamic Scene Reconstruction -- 2.2 Depth Estimation -- 3 Preliminary -- 3.1 Problem Definition -- 3.2 3D Gaussian Splatting -- 3.3 Deformation Field -- 4 Method -- 4.1 Overview -- 4.2 Motion Segmentation -- 4.3 Three-Stage Training Strategy -- 4.4 Synthetic View Augmentation -- 5 Experiment -- 5.1 Setting -- 5.2 Comparisons -- 5.3 Ablation Study -- 6 Conclusion -- References."]
[{"$nulls":4},"Segmentation and Quality Assessment of Continuous Fitness Movements Based on Vision -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 WaveOptiSeg -- 3.2 TimeTransMLP -- 4 Experiments -- 4.1 Squat-Score Dataset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Performance Comparison -- 5 Conclusion -- References -- Diagonal-Angle-Foreground IoU Loss Function for Small Object Detection -- 1 Introduction -- 2 Related Work -- 2.1 IoU Series Loss Functions for Bounding Box Regression -- 2.2 Summary -- 3 DAFIoU Loss Function -- 3.1 Angle-Based Loss Term -- 3.2 Diagonal-Based Loss Term -- 3.3 Foreground-Based Loss Term -- 3.4 DAFIoU (Diagonal, Angle, and Foreground Loss Function) -- 4 Experimental Results -- 4.1 Simulated Experiment -- 4.2 Ablation Experiment -- 4.3 YOLOv8s on Visdrone2019 -- 4.4 YOLOv8s on SODA-D10 -- 4.5 Faster R-CNN on Visdrone2019 -- 4.6 Visualization of Detection Results -- 5 Conclusion -- References -- Enhancing Dense Object Counting in Occlusion with a Dual-Branch Network -- 1 Introduction -- 2 Related Works -- 2.1 Neural Networks "]
[{"$nulls":4},"for Counting -- 2.2 Optimization Method of Dense Object Counting -- 3 Bilateral Counting Network -- 3.1 Density Region Extraction -- 3.2 Multi-lateral Collaborative Counting Network -- 4 Experiments -- 4.1 Datasets -- 4.2 Experiment Settings -- 4.3 Experiment Results -- 5 Analysis -- 5.1 Ablation Studies -- 6 Limitations -- 7 Conclusion -- References -- Street Block Classification Based on Urban Satellite Images -- 1 Introduction -- 2 Dataset Building and Reprocessing -- 2.1 Dataset Building -- 2.2 Preprocessing of Public Datasets -- 3 Our Network Architecture -- 3.1 Feature Extractor -- 3.2 Adaptive Pyramid Pooling -- 3.3 Classifier -- 4 Experiments -- 4.1 Overall Accuracy -- 4.2 F1 Score -- 5 Conclusion -- References."]
[{"$nulls":4},"SRCFT: A Correlation Filter Tracker with Siamese Super-Resolution Network and Sample Reliability Awareness for Thermal Infrared Target Tracking -- 1 Introduction -- 2 Methodology -- 2.1 Algorithm Overview -- 2.2 Siamese Super-Resolution Network -- 2.3 Sample Reliability Awareness -- 3 Experiment -- 3.1 Implementation Details -- 3.2 Performance Comparison with State-of-the-Arts -- 4 Conclusions -- References -- Traffic Sign Detection and Recognition Using Gradient Training with an Improved YOLO Network -- 1 Introduction -- 2 Tri-modal Gradient Based Dataset Processing -- 2.1 First Gradient Dataset -- 2.2 Second Gradient Dataset -- 2.3 Third Gradient Dataset -- 3 IYOLO-TS -- 4 Experiments and Results -- 5 Summary and Outlook -- References -- Neural Radiation Fields via Accelerated and High Quality Parallel for Novel View Synthesis -- 1 Introduction -- 2 Related Work -- 2.1 Novel View Synthesis -- 2.2 Neural Radiance Fields -- 2.3 NeRFs with Explicit Volumetric Representations -- 3 Background and Motivation -- 4 Method -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Evaluation on Quality and Efficiency -- 5.3 Training on Consumer Devices -- 5.4 Comparison of Ablation Experiments -- 6 Conclusion -- References -- IOCSegFormer: Enhancing Wheat Ears Counting in Field Conditions Through Augmented Local Features -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 The Architecture -- 3.2 Local Segmentation Branch -- 3.3 Loss Function -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Datasets -- 4.3 Data Preprocessing -- 4.4 Results and Analysis -- 4.5 Ablation Studies -- 4.6 Visualizations -- 5 Conclusion -- References -- Stroke-Based Few-Shot Chinese Character Style Transfer -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Overall Pipeline -- 2.3 Cross-attention Module -- 2.4 Loss Functions -- 3 Result and Discussions -- 3.1 Evaluation Metrics."]
[{"$nulls":4},"3.2 Generated Chinese Character Images Results -- 4 Conclusion -- References -- Computer Vision Drives the New Quality Productive Forces in Agriculture: A Method for Recognizing Farming Behavior on Edge Computing Devices -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Employee Detection -- 3.2 Behavior Classification -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Experimental Results -- 5 Conclusions and Future Works -- References -- PS-DeiT: A Part-Selection Based DeiT for Fine-Grained Classification -- 1 Introduction -- 2 Related Work -- 3 Part-Selection Based DeiT -- 3.1 DeiT Based Feature Extractor -- 3.2 Knowledge Distillation Model -- 3.3 Part Selection Module -- 3.4 Loss Function Design -- 4 Experimental Results and Analysis -- 4.1 Implementation Details -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 5 Conclusion -- References -- Text-Guided Multi-region Scene Image Editing Based on Diffusion Model -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Mask Dilation Based Object Editing -- 2.3 OutwardLPF Based Background Coordination -- 3 Experimental Evaluation -- 3.1 Implementation "]
[{"$nulls":4},"Details -- 3.2 Main Results -- 3.3 Ablation Study -- 3.4 Scene Iterative Editing -- 4 Conclusion -- References -- MFANet: Multi-feature Aggregation Network for Domain Generalized Stereo Matching -- 1 Introduction -- 2 Related Work -- 2.1 Deep Stereo Matching -- 2.2 Domain Generalization -- 3 Method -- 3.1 Network Architecture -- 3.2 Multi-scale Adaptive Semantic Feature Aggregation -- 3.3 Multi-scale Texture Feature Aggregation -- 3.4 Recurrent Hourglass Aggregation Network -- 3.5 Disparity Regression and Loss Function -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Studies -- 4.3 Comparison with State-of-the-Art -- 4.4 Cross-Domain Generalization -- 5 Conclusion."]
[{"$nulls":4},"References."]
[null,null,null,"Sommario/riassunto","This 13-volume set LNCS 14862-14874 constitutes - in conjunction with the 6-volume set LNAI 14875-14880 and the two-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was \"Advanced Intelligent Computing Technology and Applications\". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology. ."]