Liu / Wang / Ma Pattern Recognition and Computer Vision

6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IV
1. Auflage 2024
ISBN: 978-981-99-8462-6
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 14428, 504 Seiten, eBook

Reihe: Lecture Notes in Computer Science

The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023.

The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis

Zielgruppe

Research

Autoren/Hrsg.

Liu, Qingshan

Wang, Hanzi

Ma, Zhanyu

Zheng, Weishi

Zha, Hongbin

Chen, Xilin

Wang, Liang

Ji, Rongrong

Inhaltsverzeichnis

Shared Nearest Neighbor Calibration For Few-Shot Classification.- Prototype Rectification with Region-Wise Foreground Enhancement for Few-Shot Classification.- Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis.-  Nonconvex Tensor Hypergraph Learning for Multi-view Subspace Clustering.-  A Novel Method for Identifying Bipolar Disorder based on Diagnostic Texts.-  Deep Depression Detection based on Feature Fusion and Result Fusion.-  Adaptive Cluster Assignment for Unsupervised Semantic Segmentation.- Confidence-Guided Open-World Semi-Supervised Learning.- SSCL: Semi-supervised Contrastive Learning for Industrial Anomaly Detection.- One Step Large-scale Multi-view Subspace Clustering based on Orthogonal Matrix Factorization with Consensus Graph Learning.- Deep Multi-Task Image Clustering with Attention-guided Patch Filtering and Correlation Mining.- Deep Structure and Attention Aware Subspace Clustering.- Broaden Your Positives: A General Rectification Approach for Novel Class Discovery.- CE2: A Copula Entropic Mutual Information Estimator for Enhancing Adversarial Robustness.- Two-step projection of sparse discrimination between classes for unsupervised domain adaptation.- Enhancing Adversarial Robustness via Stochastic Robust Framework.- Pseudo Labels Refinement with Stable Cluster Reconstruction for Unsupervised Re Identification.- Ranking Variance Reduced Ensemble Attack with Dual Optimization Surrogate Search.- PCR: A Large-Scale Benchmark for Pig Counting in Real World.- A Hierarchical Theme Recognition Model for Sandplay Therapy.- Change-Aware Network for Damaged Roads Recognition and Assessment Based on Multi-temporal Remote Sensing Imageries.- UAM-Net: An Attention-Based Multi-Level Feature Fusion UNet for Remote Sensing Image Segmentation.- Improved Conditional Generative Adversarial Networks for SAR-to-Optical Image Translation.- A Novel Cross Frequency-domain Interaction Learning for Aerial Oriented Object Detection.- DBDAN: Dual-Branch Dynamic Attention Network for Semantic Segmentation of Remote Sensing Images.- Multi-scale Contrastive Learning  for Building Change Detection in Remote Sensing ImagesShadow Detection of Remote Sensing Image by Fusion of Involution and Shunted Transformer.- Few-shot Infrared Image Classification with Partial Concept Feature.- AGST-LSTM: The ConvLSTM model combines attention and gate structure for spatiotemporal sequence prediction learning.- A Shape-based Quadrangle Detector for Aerial Images.- End-to-end Unsupervised Style and Resolution Transfer Adaptation Segmentation Model for Remote Sensing ImagesA physically feasible counter-attack method for remote sensing imaging point clouds.- Adversarial Robustness via Multi-experts framework for SAR recognition with Class Imbalanced.- Recognizer Embedding Diffusion Generation for Few-shot SAR Recognization.- A Two-Stage Federated Learning Framework for Class Imbalance in Aerial Scene ClassificationSAR Image Authentic Assessment with Bayesian Deep Learning and Counterfactual Explanations.- Circle Representation Network for Specific Target Detection in Remote Sensing Image.- A Transformer-Based Adaptive Semantic Aggregation Method for UAV Visual Geo-Localization.- Lightweight Multiview Mask Contrastive Network for Small-sample Hyperspectral Image Classification.- Dim moving target detection based on imaging uncertainty analysis and hybrid entropy