The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.
The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions.
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.
Applications.- PBTR: Pre-training and Bidirectional Semantic Enhanced
Trajectory Recovery.- Event-aware Document-level Event Extraction via
Multi-granularity Event Encoder.- Curve Enhancement: A No-Reference Method
for Low-light Image Enhancement.- A deep joint model of Multi-Scale
intent-slots Interaction with Second-Order Gate for SLU.- Instance-aware and
Semantic-guided Prompt for Few-shot Learning in Large Language Models.- Graph
Attention Network Knowledge Graph Completion Model Based on Relational
Aggregation.- SODet: A LiDAR-based Object Detector in Birds-Eye
View.- Landmark-assisted Facial Action Unit Detection with Optimal Attention
and Contrastive Learning.- Multi-Scale Local Region-Based Facial Action Unit
Detection with Graph Convolutional Network.- CRE: An Efficient Ciphertext
Retrieval Scheme based on Encoder.- Sentiment Analysis Based on Pre-trained
Language Models: Recent Progress.- Improving Out-of-Distribution Detection
with Margin-Based Prototype Learning.- Text-to-Image Synthesis With
Threshold-Equipped Matching-Aware GAN.- Joint Regularization Knowledge
Distillation.- Dual-Branch Contrastive Learning for Network Representation
Learning.- Multi-Granularity Contrastive Siamese Networks for Abstractive
Text Summarization.- Joint Entity and Relation Extraction for Legal Documents
based on Table Filling.- Dynamic Knowledge Distillation for Reduced Easy
Examples.- Fooling Downstream Classifiers via Attacking Contrastive Learning
Pre-trained Models.- Feature Reconstruction Distillation with
Self-attention.- DAGAN: Generative Adversarial Network with Dual
Attentionenhanced GRU for Multivariate Time Series
Imputation.- Knowledge-Distillation-Warm-Start Training Strategy for
Lightweight Super-Resolution Networks.- SDBC: A Novel and Effective
Self-Distillation Backdoor Cleansing Approach.- An Alignment and Matching
Network with Hierarchical Visual Features for Multimodal Named Entity and
Relation Extraction.- Multi-view Consistency View Synthesis.- A reinforcement
learning-based controller designed for Intersection signal suffering from
Information Attack.- Dual-Enhancement Model of Entity Pronouns and Evidence
Sentence for Document-level Relation Extraction.- Nearest Memory Augmented
Feature Reconstruction for Unified Anomaly Detection.- Deep Learning Based
Personalized Stock Recommender System.- Feature-Fusion-Based Haze Recognition
in Endoscopic Images.- Retinex Meets Transformer: Bridging Illumination and
Reflectance Maps for Low-light Image Enhancement.- Make Spoken Document
Readable: Leveraging Graph Attention Networks for Chinese Document-Level
Spoken-to-Written Simplification.- MemFlowNet: A Network for Detecting Subtle
Surface Anomalies with Memory Bank and Normalizing Flow.- LUT-LIC: Look-up
Table-Assisted Learned Image Compression.- Oil and GasAutomatic
Infrastructure Mapping: Leveraging HighResolution Satellite Imagery through
fine-tuning of object detection models.- AttnOD: An Attention-based OD
Prediction Model with Adaptive Graph Convolution.- CMMix: Cross-Modal Mix
Augmentation between Images and Texts for Visual Grounding.- A
Relation-oriented Approach for Complex Entity Relation Extraction.- A
Revamped Sparse Index Tracker leveraging $K$\,Sparsity and Reduced Portfolio
Reshuffling.- Anomaly detection of fixed-wing unmanned aerial vehicle (UAV)
based on cross-feature-attention LSTM network.- Spatial and Frequency Domains
Inconsistency Learning for Face Forgery Detection.- Enhancing Camera Position
Estimation by Multi-View Pure Rotation Recognition and Automated Annotation
Learning.- Detecting Adversarial Examples Via Classification Difference of a
Robust Surrogate Model.- Minimizing Distortion in Linguistic Steganography
via Adaptive Language Model Tuning.- Efficient Chinese Relation Extraction
with Multi-entity Dependency Tree Pruning and Path-Fusion.- A lightweight
text classification model based on Label Embedding Attentive mechanism.
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