Neural Information Processing : 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part III / / edited by Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt

• Record Nr.: UNINA9910698649403321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
• ISBN: 9783031301117; 9783031301100
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (756 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 13625
• Disciplina: 006.3
• Soggetti: Pattern recognition systems; Data mining; Machine learning; Social sciences - Data processing; Automated Pattern Recognition; Data Mining and Knowledge Discovery; Machine Learning; Computer Application in Social and Behavioral Sciences
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Applications -- A Comparative Analysis of Loss Functions for Handling Foreground-Background Imbalance in Image Segmentation -- Electron Microscopy Image Registration with Transformers -- Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network -- A Measurement-Based Quantum-Like Language Model for Text Matching -- Virtual Try-On via Matching Relation with Landmark -- WINMLP:Quantum&Involution Inspire False Positive Reduction In Lung Nodule Detection -- Incorporating Generation Method and Discourse Structure to Event Coreference Resolution -- CCN: Pavement Crack Detection With Context Contrasted Net -- Spatial and Temporal Guidance for Semi-supervised Video Object Segmentation -- A Hybrid Framework based on Classifier Calibration for Imbalanced Aerial Scene ; Recognition -- Enhancing BERT for Short Text Classification with Latent Information -- Unsupervised Anomaly Segmentation for Brain Lesions using Dual Semantic-Manifold Reconstruction -- Transformer Based High-frequency Predictive Model for Visual-haptic Feedback of Virtual Surgery Navigation -- Hierarchical Multimodal Attention Network Based on Semantically Textual Guidance for Video Captioning -- Autism Spectrum Disorder Classification of Facial Images using Xception Model and Transfer Learning with Image Augmentation -- A Comprehensive Vision-based Model for Commercial Truck Driver Fatigue Detection -- Automatic Identification of Class Level Refactoring using Abstract Syntax Tree and Embedding Technique -- Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning -- Detecting and Mitigating Backdoor Attacks with Dynamic and Invisible Triggers -- NAS-StegNet: Lightweight Image Steganography Networks via Neural Architecture Search -- FIT: Frequency-based Image Translation for Domain Adaptive Object Detection -- Single Image Dehazing Using Frequency Attention -- A Recurrent Point Clouds Selection Method for 3D Dense Captioning -- Multi-domain Feature Fusion Neural Network for Electrocardiogram Classification -- Graph-based Contextual Attention Network for Single Image Deraining -- ADTR: Anomaly Detection Transformer with Feature Reconstruction -- SCIEnt: A Semantic-feature-based Framework for Core Information Extraction from Web Pages -- Hierarchical down-sampling based ultra high-resolution image inpainting -- Vision Transformer With Depth Auxiliary Information For Face Anti-spoofing -- Dynamically Connected Graph Representation For Object Detection -- Multi-Class Anomaly Detection -- Understanding Graph and Understanding Map and their Potential Applications -- BBSN: Bilateral-Branch Siamese Network for Imbalanced Multi-label Text Classification -- Deep Hierarchical Semantic Model for Text Matching -- Multimodal Neural Network For Demand Forecasting -- Image Super-Resolution Based on Adaptive Feature Fusion Channel Attention -- SGFuion:Camera-LiDAR Semantic and Geometric Fusion for 3D Object Detection -- SATNet: Captioning with Semantic Alignment and Feature Enhancement -- Halyomorpha Halys Detection Using Efficient Neural Networks -- HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images -- In Situ Augmentation for Defending Against Adversarial Attacks on Text Classifiers -- Relation-guided Dual Hash Network for Unsupervised Cross-Modal Retrieval -- Prompt-Based Learning for Aspect-Level Sentiment Classification -- Multi-Knowledge Embeddings Enhanced Topic Modeling for Short Texts -- Adaptive early classification of time series using deep learning -- Introducing Multi-modality in Persuasive Task Oriented Virtual Sales Agent -- Low Dose CT Image Denoising Using Efficient Transformer With SimpleGate Mechanism -- iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation -- Evolutionary Action Selection for Gradient-based Policy Learning -- Building Conversational Diagnosis Systems for Fine-grained Diseases using Few Annotated Data -- Towards Improving EEG-based Intent Recognition in Visual SearchTasks -- RVFL Classifier based Ensemble Deep Learning for Early Diagnosis of Alzheimer’s Disease -- Anatomical Landmarks Localization for 3D Foot Point Clouds -- Impact of the composition of feature extraction and class sampling in medicare fraud detection -- A Hybrid Feature Selection Approach for Data Clustering Based on Ant Colony Optimization -- FaceMix: Transferring local regions for data augmentation in face recognition -- Permissioned Blockchain-based XGBoost for Multi Banks Fraud Detection -- Rethinking Image Inpainting with Attention Feature Fusion -- Towards Accurate ; Alignment and Sufficient Context in Scene Text Recognition.
• Sommario/riassunto: The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.