LEADER 00999nam0-22003371i-450- 001 990004216130403321 005 20080702095735.0 010 $a0-631-20487-3 035 $a000421613 035 $aFED01000421613 035 $a(Aleph)000421613FED01 035 $a000421613 100 $a19990604d1999----km-y0itay50------ba 101 0 $aeng 105 $aa-------001yy 200 1 $aAmerican english$edialects and variation$fWalt Wolfram and natalie Schilling-Estes 205 $aRepr. 210 $aOxford$cBlackwell$d1999 215 $aXVII, 398 p.$cill.$d23 cm 225 1 $aLanguage in society$v23 610 0 $aLingua inglese$aDialetti$aStati Uniti d'America 676 $a427.973 700 1$aWolfram,$bWalt$f<1941- >$0165932 701 1$aSchilling-Estes,$bNatalie$0165933 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004216130403321 952 $a427.973 WOL 1$bBibl.34049$fFLFBC 959 $aFLFBC 996 $aAmerican english$9482643 997 $aUNINA LEADER 01575nam 2200457 450 001 9910671080403321 005 20200611094215.0 035 $a(CKB)3810000000001319 035 $a(SSID)ssj0001130587 035 $a(PQKBManifestationID)12465823 035 $a(PQKBTitleCode)TC0001130587 035 $a(PQKBWorkID)11112471 035 $a(PQKB)11301872 035 $a(MiAaPQ)EBC3218536 035 $a(OCoLC)1105420711 035 $a(FlNmELB)ELB39057 035 $a(EXLCZ)993810000000001319 100 $a20140407d2012 uy 0 101 0 $aspa 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAcciones y reflexiones para la reconstruccio?n de la poli?tica social en Me?xico $euna mirada desde lo local /$fAdolfo Rogelio Cogco Caldero?n, Miriam Rodri?guez Vargas, Jorge Alberto Pe?rez Cruz (coordinadores) 210 1$aMe?xico DF :$cPlaza y Valde?s S. A. de C. V.,$d2012. 215 $a1 online resource (166 pa?ginas) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a607-402-408-1 607 $aMe?xico$xPoli?tica social 607 $aMexico$xSocial policy 676 $a307.1/2160972 702 $aCogco Caldero?n$b Adolfo Rogelio 702 $aRodri?guez Vargas$b Miriam 702 $aPe?rez Cruz$b Jorge Alberto 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910671080403321 996 $aAcciones y reflexiones para la reconstruccio?n de la poli?tica social en Me?xico$93049292 997 $aUNINA LEADER 02504nam 2200649Ia 450 001 9910789629303321 005 20230721013933.0 010 $a1-60344-508-0 035 $a(CKB)2670000000079080 035 $a(EBL)3037875 035 $a(SSID)ssj0000531078 035 $a(PQKBManifestationID)11300592 035 $a(PQKBTitleCode)TC0000531078 035 $a(PQKBWorkID)10588068 035 $a(PQKB)10208768 035 $a(OCoLC)298890737 035 $a(MiAaPQ)EBC3037875 035 $a(MdBmJHUP)muse1229 035 $a(Au-PeEL)EBL3037875 035 $a(CaPaEBR)ebr10447190 035 $a(EXLCZ)992670000000079080 100 $a20061005d2007 ub 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSpace and place in the Mexican landscape$b[electronic resource] $ethe evolution of a colonial city /$fby Fernando Nu?n?ez, Carlos Arvizu, Ramo?n Abonce ; edited by Malcolm Quantrill 205 $a1st ed. 210 $aCollege Station $cTexas A & M University Press$dc2007 215 $a1 online resource (196 p.) 225 1 $aStudies in architecture and culture ;$vno. 7 300 $aDescription based upon print version of record. 311 $a1-58544-583-5 320 $aIncludes bibliographical references (p. [163]-169) and index. 327 $aThe interaction of space and place : the Mexican mixture / by Fernando Nu?n?ez -- The urban evolution of the colonial city : Queretaro, 1531-1910 / by Carlos Arvizu -- From revolution to industrial society : Queretaro, 1910 to the modern age / by Carlos Arvizu and Ramo?n Abonce. 410 0$aStudies in architecture and culture ;$vno. 7. 606 $aCity planning$zMexico$zQuere?taro$xHistory 606 $aLandscape assessment$zMexico 606 $aPublic spaces$zMexico 606 $aUrbanization$zMexico$zQuere?taro$xHistory 615 0$aCity planning$xHistory. 615 0$aLandscape assessment 615 0$aPublic spaces 615 0$aUrbanization$xHistory. 676 $a307.1/2160972 700 $aNu?n?ez$b Fernando$g(Luis Fernando Nu?n?ez Urquiza)$01462795 701 $aAbonce$b Ramo?n$01462796 701 $aArvizu Garci?a$b Carlos$01462797 701 $aQuantrill$b Malcolm$f1931-2009.$01462798 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910789629303321 996 $aSpace and place in the Mexican landscape$93671924 997 $aUNINA LEADER 13950nam 22009015 450 001 9910799221303321 005 20240828001755.0 010 $a9789819984299 010 $a9819984297 024 7 $a10.1007/978-981-99-8429-9 035 $a(CKB)29468291300041 035 $a(DE-He213)978-981-99-8429-9 035 $a(MiAaPQ)EBC31051380 035 $a(Au-PeEL)EBL31051380 035 $a(OCoLC)1416340886 035 $a(EXLCZ)9929468291300041 100 $a20231223d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition and Computer Vision $e6th Chinese Conference, PRCV 2023, Xiamen, China, October 13?15, 2023, Proceedings, Part I /$fedited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (XIV, 513 p. 159 illus., 152 illus. in color.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14425 311 08$a9789819984282 327 $aIntro -- Preface -- Organization -- Contents - Part I -- Action Recognition -- Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion Based Classification -- 1 Introduction -- 2 Related Work -- 3 Our Proposed Approach -- 3.1 Overview -- 3.2 Network Architecture -- 4 Experiment -- 4.1 Dataset and Evaluation Metric -- 4.2 Implementation Details -- 4.3 Comparison with Other SOTA Algorithms -- 4.4 Ablation Study -- 4.5 Parameter Analysis -- 5 Conclusion -- References -- Multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Related Works -- 2.1 Attention Mechanism -- 2.2 Lightweight Models -- 3 Method -- 3.1 Multi-Branch Fusion Module -- 3.2 Semantic Information -- 3.3 Graph Convolution Module -- 3.4 Time Convolution Module -- 4 Experiment -- 4.1 Dataset -- 4.2 Experimental Details -- 4.3 Ablation Experiment -- 4.4 Comparison with State-of-the-Art -- 5 Action Visualization -- 6 Conclusion -- References -- Auto-Learning-GCN: An Ingenious Framework for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 GCN-Based Skeleton Processing -- 3.2 The AL-GCN Module -- 3.3 The Attention Correction and Jump Model -- 3.4 Multi-stream Gaussian Weight Selection Algorithm -- 4 Experimental Results and Analysis -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Compared with the State-of-the-Art Methods -- 4.4 Ablation Study -- 4.5 Visualization -- 5 Conclusion -- References -- Skeleton-Based Action Recognition with Combined Part-Wise Topology Graph Convolutional Networks -- 1 Introduction -- 2 Related Work -- 2.1 Skeleton-Based Action Recognition -- 2.2 Partial Graph Convolution in Skeleton-Based Action Recognition -- 3 Methods -- 3.1 Preliminaries -- 3.2 Part-Wise Spatial Modeling -- 3.3 Part-Wise Spatio-Temporal Modeling. 327 $a3.4 Model Architecture -- 4 Experiments -- 4.1 Datasets -- 4.2 Training Details -- 4.3 Ablation Studies -- 4.4 Comparison with the State-of-the-Art -- 5 Conclusion -- References -- Segmenting Key Clues to Induce Human-Object Interaction Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Key Features Segmentation-Based Module -- 3.2 Key Features Learning Encoder -- 3.3 Spatial Relationships Learning Graph-Based Module -- 3.4 Training and Inference -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Implementation Results -- 4.3 Ablation Study -- 4.4 Qualitative Results -- 5 Conclusion -- References -- Lightweight Multispectral Skeleton and Multi-stream Graph Attention Networks for Enhanced Action Prediction with Multiple Modalities -- 1 Introduction -- 2 Related Work -- 2.1 Skeleton-Based Action Recognition -- 2.2 Dynamic Graph Neural Network -- 3 Methods -- 3.1 Spatial Embedding Component -- 3.2 Temporal Embedding Component -- 3.3 Action Prediction -- 4 Experiments and Discussion -- 4.1 NTU RGB+D Dataset -- 4.2 Experiments Setting -- 4.3 Evaluation of Human Action Recognition -- 4.4 Ablation Study -- 4.5 Visualization -- 5 Conclusion -- References -- Spatio-Temporal Self-supervision for Few-Shot Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Few-Shot Action Recognition -- 2.2 Self-supervised Learning (SSL)-Based Few-Shot Learning -- 3 Method -- 3.1 Problem Definition -- 3.2 Spatio-Temporal Self-supervision Framework -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Comparison with State-of-the-Art Methods -- 4.3 Ablation Studies -- 5 Conclusions -- References -- A Fuzzy Error Based Fine-Tune Method for Spatio-Temporal Recognition Model -- 1 Introduction -- 2 Related Work -- 2.1 Spatio-Temporal (3D) Convolution Networks -- 2.2 Clips Selection and Features Aggregation -- 3 Proposed Method -- 3.1 Problem Definition. 327 $a3.2 Fuzzy Target -- 3.3 Fine Tune Loss Function -- 4 Experiment -- 4.1 Datasets and Implementation Details -- 4.2 Performance Comparison -- 4.3 Discussion -- 5 Conclusion -- References -- Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Proposed Method -- 2.1 Network Architecture -- 2.2 Temporal-Channel Focus Module -- 2.3 Dynamic Channel Topology Attention Module -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Ablation Study -- 3.3 Comparison with the State-of-the-Art -- 4 Conclusion -- References -- HFGCN-Based Action Recognition System for Figure Skating -- 1 Introduction -- 2 Figure Skating Hierarchical Dataset -- 3 Figure Skating Action Recognition System -- 3.1 Data Preprocessing -- 3.2 Multi-stream Generation -- 3.3 Hierarchical Fine-Grained Graph Convolutional Neural Network (HFGCN) -- 3.4 Decision Fusion Module -- 4 Experiments and Results -- 4.1 Experimental Environment -- 4.2 Experiment Results and Analysis -- 5 Conclusion -- References -- Multi-modal Information Processing -- Image Priors Assisted Pre-training for Point Cloud Shape Analysis -- 1 Introduction -- 2 Proposed Method -- 2.1 Problem Setting -- 2.2 Overview Framework -- 2.3 Multi-task Cross-Modal SSL -- 2.4 Objective Function -- 3 Experiments and Analysis -- 3.1 Pre-training Setup -- 3.2 Downstream Tasks -- 3.3 Ablation Study -- 4 Conclusion -- References -- AMM-GAN: Attribute-Matching Memory for Person Text-to-Image Generation -- 1 Introduction -- 2 Related Work -- 2.1 Text-to-image Generative Adversarial Network -- 2.2 GANs for Person Image -- 3 Method -- 3.1 Feature Extraction -- 3.2 Multi-scale Feature Fusion Generator -- 3.3 Real-Result-Driven Discriminator -- 3.4 Objective Functions -- 4 Experiment -- 4.1 Dataset -- 4.2 Implementation -- 4.3 Evaluation Metrics -- 4.4 Quantitative Evaluation. 327 $a4.5 Qualitative Evaluation -- 4.6 Ablation Study -- 5 Conclusion -- References -- RecFormer: Recurrent Multi-modal Transformer with History-Aware Contrastive Learning for Visual Dialog -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries -- 3.2 Model Architecture -- 3.3 Training Objectives -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Baselines -- 4.3 Evaluation Metric -- 4.4 Implementation Details -- 5 Results and Analysis -- 5.1 Main Results -- 5.2 Ablation Study -- 5.3 Attention Visualization -- 6 Conclusion -- References -- KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing -- 1 Introduction -- 2 Background -- 2.1 Text-to-Image Generation and Editing -- 2.2 Stable Diffusion Model -- 3 KV Inversion: Training-Free KV Embeddings Learning -- 3.1 Task Setting and Reason of Existing Problem -- 3.2 KV Inversion Overview -- 4 Experiments -- 4.1 Comparisons with Other Concurrent Works -- 4.2 Ablation Study -- 5 Limitations and Conclusion -- References -- Enhancing Text-Image Person Retrieval Through Nuances Varied Sample -- 1 Introduction -- 2 Relataed Work -- 2.1 Text-Image Retrieval -- 2.2 Text-Image Person Retrieval -- 3 Method -- 3.1 Feature Extraction and Alignment -- 3.2 Nuanced Variation Module -- 3.3 Image Text Matching Loss -- 3.4 Hard Negative Metric Loss -- 4 Experiment -- 4.1 Datasets and Evaluation Setting -- 4.2 Comparison with State-of-the-Art Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- Unsupervised Prototype Adapter for Vision-Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Large-Scale Pre-trained Vision-Language Models -- 2.2 Adaptation Methods for Vision-Language Models -- 2.3 Self-training with Pseudo-Labeling -- 3 Method -- 3.1 Background -- 3.2 Unsupervised Prototype Adapter -- 4 Experiments -- 4.1 Image Recognition -- 4.2 Domain Generalization. 327 $a4.3 Ablation Study -- 5 Conclusion -- References -- Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Overview -- 3.2 MCD: Multimodal Causal Discovery -- 3.3 MMC-CLIP -- 3.4 Image-Text Alignment -- 4 Experiments -- 4.1 Datasets and Settings -- 4.2 Results on MSCOCO -- 4.3 Results on Flickr30K -- 4.4 Ablation Studies -- 5 Conclusion -- References -- Exploring Cross-Modal Inconsistency in Entities and Emotions for Multimodal Fake News Detection -- 1 Introduction -- 2 Related Works -- 2.1 Single-Modality Fake News Detection -- 2.2 Multimodal Fake News Detection -- 3 Methodology -- 3.1 Feature Extraction -- 3.2 Cross-Modal Contrastive Learning -- 3.3 Entity Consistency Learning -- 3.4 Emotional Consistency Learning -- 3.5 Multimodal Fake News Detector -- 4 Experiments -- 4.1 Experimental Configurations -- 4.2 Overall Performance -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Deep Consistency Preserving Network for Unsupervised Cross-Modal Hashing -- 1 Introduction -- 2 The Proposed Method -- 2.1 Problem Definition -- 2.2 Deep Feature Extraction and Hashing Learning -- 2.3 Features Fusion and Similarity Matrix Construction -- 2.4 Hash Code Fusion and Reconstruction -- 2.5 Objective Function -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Implementation Details -- 3.3 Results and Analysis -- 4 Conclusion -- References -- Learning Adapters for Text-Guided Portrait Stylization with Pretrained Diffusion Models -- 1 Introduction -- 2 Related Work -- 2.1 Text-to-Image Diffusion Models -- 2.2 Control of Pretrained Diffusion Model -- 2.3 Text-Guided Portrait Stylizing -- 3 Method -- 3.1 Background and Preliminaries -- 3.2 Overview of Our Method -- 3.3 Portrait Stylization with Text Prompt -- 3.4 Convolution Adapter -- 3.5 Adapter Optimization -- 4 Experiments. 327 $a4.1 Implementation Settings. 330 $aThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13?15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications andSystems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. . 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14425 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 $aLiu$b Qingshan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Hanzi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMa$b Zhanyu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZheng$b Weishi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZha$b Hongbin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChen$b Xilin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Liang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJi$b Rongrong$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910799221303321 996 $aPattern recognition and computer vision$91972598 997 $aUNINA