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E-grāmata: Pattern Recognition and Computer Vision: 7th Chinese Conference, PRCV 2024, Urumqi, China, October 18-20, 2024, Proceedings, Part VII

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This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 1820, 2024.





The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.
Scene Text Recognition via k-NN Attention-based Decoder and Margin-based
Softmax LossReal-Time Text Detection with Multi-Level  Feature Fusion and
Pixel ClusteringREFINED AND LOCALITY-ENHANCED FEATURE FOR HANDWRITTEN
MATHEMATICAL EXPRESSION RECOGNITIONLearning Fine-grained and Semantically
Aware Mamba Representations for Tampered Text Detection in ImagesDual Feature
Enhanced Scene Text Recognition Method for Low-Resource
UyghurSegmentation-free Todo Mongolian OCR and Its  Public DatasetHybrid
Encoding Method for Scene Text Recognition in Low-Resource UyghurROBC: a
Radical-Level Oracle Bone Character DatasetIntegrated Recognition of
Arbitrary-Oriented Multi-Line Billet NumberImproving Scene Text Recognition
with Counting Aware Contrastive Learning and Attention AlignmentGridMask: An
Efficient Scheme for Real Time Curved Scene Text DetectionTibetan Handwriting
Recognition Method based on Structural Re-parameterization ViT and Vertical
AttentionMFH: Marrying Frequency Domain with Handwritten Mathematical
Expression RecognitionLeveraging Structure Knowledge and Deep Models for the
Detection of Abnormal Handwritten Text.- OCR-aware Scene Graph Generation via
Multi-modal Object Representation Enhancement and Logical Bias Learning.-
Enhancing Transformer-based Table Structure Recognition for Long Tables.-
Show Exemplars and Tell Me What You See: In-context Learning with Frozen
Large Language Models for Text.- VQAMLR-NET: an arbitrary skew angle
detection algorithm for complex layout document images.- TextViTCNN
Enhancing Natural Scene Text Recognition with Hybrid Transformer and
Convolutional NetworksEnhancing Visual Information Extraction with Large
Language Models through Layout-aware Instruction Tuning.- SFENet: Arbitrary
Shapes Scene Text Detection with Semantic Feature ExtractorImproving
Zero-Shot Image Captioning Efficiency with Metropolis-Hastings Sampling.-
Improving Text Classification Performance through Multimodal Representation.-
A Multi-feature Fusion Approach for Words Recognition of Ancient Mongolian
Documents.- TableRocket: An Efficient and Effective Framework for Table
Reconstruction.- Not All Texts Are the Same: Dynamically Querying Texts for
Scene Text Detection.- Multi-Modal Attention based on 2D Structured Sequence
for Table Recognition.- A Two-stream Hybrid CNN-Transformer Network for
Skeleton-based Human Interaction Recognition.- Skeleton-Language Pre-training
to Collaborate with Self-Supervised Human Action Recognition.-
Spatio-Temporal Contrastive Learning for Compositional Action
RecognitionPath-Guided Motion Prediction with Multi-View Scene Perception.-
Privacy-preserving Action Recognition: A Survey.- Attention-based
Spatio-temporal modeling with 3D Convolutional Neural Networks for Dynamic
Gesture Recognition.- MIT: Multi-cue Injected Transformer for Two-stage HOI
Detection.- DIDA: Dynamic Individual-to-integrated Augmentation for
Self-Supervised Skeleton-Based Action Recognition.- Multi-scale Spatial and
Temporal Feature Aggregation Graph Convolutional Network for Skeleton-Based
Action Recognition.- Improving Video Representation of Vision-Language Model
with Decoupled Explicit Temporal Modeling.- KS-FuseNet: An efficient action
recognition method based on keyframe selection and feature fusion.- Dynamic
Skeleton Association Transformer for dyadic Interaction Action
RecognitionSpecies-Aware Guidance for Animal Action Recognition with
Vision-Language Knowledge.