LEADER 01446nam 2200337 n 450 001 996394564703316 005 20221108102250.0 035 $a(CKB)4940000000123275 035 $a(EEBO)2240925121 035 $a(UnM)9959279300971 035 $a(EXLCZ)994940000000123275 100 $a19930715d1627 uh 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aBy the King. A proclamation prohibiting the vse of snaffles, and commanding the vse of bittes for riding$b[electronic resource] 210 $aImprinted at London $cBy Bonham Norton and Iohn Bill, printers to the Kings most excellent Maiestie$dM.DC.XXVII. [1627] 215 $a1 sheet ([1] p.) 300 $aDated at end: White-Hall, the twentieth day of Nouember, in the third yeere of His Highnesse reigne .. 300 $aThis state has arms 23 (one leaf under the lion's leg); Steele notation: Princely Aduice vpon. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 606 $aBits (Bridles)$xLaw and legislation$zEngland$vEarly works to 1800 615 0$aBits (Bridles)$xLaw and legislation 701 $aCharles$cKing of England,$f1600-1649.$0793295 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 906 $aBOOK 912 $a996394564703316 996 $aBy the King. A proclamation prohibiting the vse of snaffles, and commanding the vse of bittes for riding$92307963 997 $aUNISA LEADER 06955nam 22007575 450 001 996630869503316 005 20250630101825.0 010 $a981-9784-87-5 024 7 $a10.1007/978-981-97-8487-5 035 $a(CKB)36527925500041 035 $a(DE-He213)978-981-97-8487-5 035 $a(EXLCZ)9936527925500041 100 $a20241103d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition and Computer Vision $e7th Chinese Conference, PRCV 2024, Urumqi, China, October 18?20, 2024, Proceedings, Part I /$fedited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (XIV, 569 p. 155 illus., 149 illus. in color.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15031 311 08$a981-9784-86-7 327 $aCluster center initialization for fuzzy K-modes clustering using outlier detection technique -- Few-Shot Class-Incremental Learning via Cross-Modal Alignment with Feature Replay -- Generalizing soft actor-critic algorithms to discrete action spaces -- LarvSeg: Exploring Image Classification Data For Large Vocabulary Semantic Segmentation via Category-wise Attentive Classifier -- Exploring Out-of-distribution Scene Text Recognition for Driving Scenes with Hybrid Test-time Adaptation -- PhaseNN: An Unsupervised and Spatial-Frequency Integrated Network for Phase Retrieval -- Sequential Transfer of Pose and Texture for Pose Guided Person Image Generation -- Balanced Clustering with Discretely Weighted Pseudo-Label -- Tensor Robust Principal Component Analysis with Hankel Structure -- Self-Distillation via Intra-class Compactness -- An Enhanced Dual-Channel-Omni-Scale 1DCNN for Fault Diagnosis -- Visual-Guided Reasoning Path Generation for Visual Question Answering -- FedGC: Federated Learning on Non-IID Data via Learning from Good Clients -- Inter-class Correlation-based Online Knowledge Distillation -- Accelerating Domain Adaptation with Cascaded Adaptive Vision Transformer -- Multistage Compression Optimization Strategies for Accelerating Diffusion Models -- Defending Adversarial Patches via Joint Region Localizing and Inpainting -- Multi-view Spectral Clustering Based on Topological Manifold Learning -- Client selection mechanism for federated learning based on class imbalance -- A New Paradigm for Enhancing Ensemble Learning through Parameter Diversification -- Adaptive Multi-Information Feature Fusion MLP with Filter Enhancement for Sequential Recommendation -- FedDCP: Personalized Federated Learning Based on Dual Classifiers and Prototypes -- AtomTool: Empowering Large Language Models with Tool Utilization Skills -- Making the Primary Task Primary: Boosting Few-Shot Classification by Gradient-biased Multi-task Learning -- Cascade Large Language Model via In-Context Learning for Depression Detection on Chinese Social Media -- TRAE : Reversible Adversarial Example with Traceability -- A Two-stage Active Domain Adaptation Framework for Vehicle Re-Identification -- FBR-FL: Fair and Byzantine-Robust Federated Learning via SPD Manifold -- SecBFL-IoV: A Secure Blockchain-Enabled Federated Learning Framework for Resilience against Poisoning Attacks in Internet of Vehicles -- Adapt and Refine: A Few-Shot Class-Incremental Learner via Pre-trained Models -- Learning Fully Parametric Subspace Clustering -- A Comprehensive Exploration on Detecting Fake Images Generated by Stable Diffusion -- Adaptive Margin Global Classifier for Exemplar-Free Class-Incremental Learning -- SACTGAN-EE imbalanced data processing method for credit default prediction -- FedHC: Learning Imbalanced Clusters via Federated Hierarchical Clustering -- Enhancing Time Series Classification with Explainable Time-frequency Features Representation -- Adaptive Unified Framework with Global Anchor Graph for Large-scale Multi-view Clustering -- SLRL: Structured Latent Representation Learning for Multi-view Clustering. 330 $aThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15031 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial intelligence 606 $aApplication software 606 $aComputer networks 606 $aComputer systems 606 $aMachine learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aArtificial Intelligence 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 606 $aComputer System Implementation 606 $aMachine Learning 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aComputer networks. 615 0$aComputer systems. 615 0$aMachine learning. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aArtificial Intelligence. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 615 24$aComputer System Implementation. 615 24$aMachine Learning. 676 $a006 702 $aLin$b Zhouchen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCheng$b Ming-Ming$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHe$b Ran$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aUbul$b Kurban$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSilamu$b Wushouer$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZha$b Hongbin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhou$b Jie$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLiu$b Cheng-Lin$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996630869503316 996 $aPattern recognition and computer vision$91972598 997 $aUNISA