001461959 000__ 06137cam\a2200757\a\4500
001461959 001__ 1461959
001461959 003__ OCoLC
001461959 005__ 20230503003419.0
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001461959 019__ $$a1374241485
001461959 020__ $$a9783031270772$$q(electronic bk.)
001461959 020__ $$a3031270770$$q(electronic bk.)
001461959 020__ $$z3031270762
001461959 020__ $$z9783031270765
001461959 0247_ $$a10.1007/978-3-031-27077-2$$2doi
001461959 035__ $$aSP(OCoLC)1374430372
001461959 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dOCLCF
001461959 049__ $$aISEA
001461959 050_4 $$aTA1634
001461959 08204 $$a006.7$$223/eng/20230406
001461959 1112_ $$aInternational Conference on Multi-Media Modeling$$n(29th :$$d2023 :$$cBergen, Norway)
001461959 24510 $$aMultiMedia modeling :$$b29th International Conference, MMM 2023, Bergen, Norway, January 9-12, 2023, Proceedings.$$nPart I /$$cDuc-Tien Dang-Nguyen [and more], editors.
001461959 2463_ $$aMMM 2023
001461959 260__ $$aCham :$$bSpringer,$$c2023.
001461959 300__ $$a1 online resource (719 p.).
001461959 4901_ $$aLecture Notes in Computer Science ;$$v13833
001461959 500__ $$a2.2 Multi-modal Complementary Learning
001461959 500__ $$aIncludes author index.
001461959 5050_ $$aIntro -- Preface -- Organizing Committee -- Contents - Part I -- Contents - Part II -- Detection, Recognition and Identification -- MMM-GCN: Multi-Level Multi-Modal Graph Convolution Network for Video-Based Person Identification -- 1 Introduction -- 2 Related Work -- 2.1 Video-Based Multi-Modal Person Identification -- 2.2 Graph Convolution Networks -- 3 Methodology -- 3.1 Overview -- 3.2 Graph Construction and Learning -- 3.3 Graph Node Encoding -- 3.4 Person Identification -- 4 Experiment -- 4.1 Experiment Setups -- 4.2 Comparison to the State of the Art -- 4.3 Ablation Study
001461959 5058_ $$a4.4 Discussion -- 5 Conclusion -- References -- Feature Enhancement and Reconstruction for Small Object Detection -- 1 Introduction -- 2 Related Work -- 2.1 Upsampling Method -- 2.2 Multi-scale Feature Extraction -- 2.3 Attention Mechanism -- 3 Method -- 3.1 Network Architecture -- 3.2 Content-Aware Upsampling (CAU) -- 3.3 Channel Shuffle Attention (CSA) -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Ablation Study -- 4.5 Comparative Results -- 4.6 Qualitative Results -- 5 Conclusion -- References
001461959 5058_ $$aToward More Accurate Heterogeneous Iris Recognition with Transformers and Capsules -- 1 Introduction -- 2 Technical Details -- 2.1 Vision Token Generator -- 2.2 Transformer Encoder -- 2.3 Transformer Decoder -- 2.4 3D Capsule Matcher -- 2.5 Loss Function -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Network Structure -- 3.3 Training Setup -- 3.4 Evaluation Protocol -- 3.5 Comparison with Other State-of-the-art Heterogeneous Iris Recognition Algorithms -- 3.6 Ablation Experiments -- 4 Conclusions -- References
001461959 5058_ $$aMCANet: Multiscale Cross-Modality Attention Network for Multispectral Pedestrian Detection -- 1 Introduction -- 2 Related Work -- 2.1 Multispectral Pedestrian Detection -- 2.2 Attention Mechanism -- 3 Proposed Method -- 3.1 Cross-Modality Feature Extraction Module -- 3.2 Spatial Attention Fusion Module -- 3.3 Channel Attention Fusion Module -- 4 Experiments -- 4.1 Dataset and Metric -- 4.2 Implementation Details -- 4.3 Quantitative Evaluation -- 4.4 Qualitative Evaluation -- 4.5 Ablation Study -- 5 Conclusion -- References -- Human Action Understanding
001461959 5058_ $$aOverall-Distinctive GCN for Social Relation Recognition on Videos -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Framework Overview -- 3.2 Overall-level Character GCN -- 3.3 Distinctive-level Character GCN -- 3.4 Relation Classification -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Baseline Methods -- 4.4 Experiment Results -- 5 Ablation Study -- 6 Conculusion -- References -- Weakly-Supervised Temporal Action Localization with Regional Similarity Consistency -- 1 Introduction -- 2 Related Work -- 2.1 Weakly-supervised Temporal Action Localization
001461959 506__ $$aAccess limited to authorized users.
001461959 520__ $$aThe two-volume set LNCS 13833 and LNCS 13834 constitutes the proceedings of the 29th International Conference on MultiMedia Modeling, MMM 2023, which took place in Bergen, Norway, during January 9-12, 2023. The 86 papers presented in these proceedings were carefully reviewed and selected from a total of 267 submissions. They focus on topics related to multimedia content analysis; multimedia signal processing and communications; and multimedia applications and services.
001461959 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 6, 2023).
001461959 650_0 $$aMultimedia systems$$vCongresses.
001461959 650_0 $$aComputer simulation$$vCongresses.
001461959 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001461959 655_0 $$aElectronic books.
001461959 7001_ $$aDang-Nguyen, Duc-Tien.
001461959 7001_ $$aGurrin, Cathal.
001461959 7001_ $$aLarson, Martha.
001461959 7001_ $$aSmeaton, Alan F.,$$d1959-
001461959 7001_ $$aRudinac, Stevan.
001461959 7001_ $$aDao, Minh-Son.
001461959 7001_ $$aTrattner, Christoph.
001461959 7001_ $$aChen, Phoebe.
001461959 77608 $$iPrint version:$$aDang-Nguyen, Duc-Tien$$tMultiMedia Modeling$$dCham : Springer International Publishing AG,c2023$$z9783031270765
001461959 830_0 $$aLecture notes in computer science ;$$v13833.
001461959 852__ $$bebk
001461959 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-27077-2$$zOnline Access$$91397441.1
001461959 909CO $$ooai:library.usi.edu:1461959$$pGLOBAL_SET
001461959 980__ $$aBIB
001461959 980__ $$aEBOOK
001461959 982__ $$aEbook
001461959 983__ $$aOnline
001461959 994__ $$a92$$bISE