001461432 000__ 06855cam\a2200745\i\4500
001461432 001__ 1461432
001461432 003__ OCoLC
001461432 005__ 20230503003352.0
001461432 006__ m\\\\\o\\d\\\\\\\\
001461432 007__ cr\cn\nnnunnun
001461432 008__ 230315s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001461432 019__ $$a1372395523
001461432 020__ $$a9783031263484$$q(electronic bk.)
001461432 020__ $$a3031263480$$q(electronic bk.)
001461432 020__ $$z9783031263477
001461432 0247_ $$a10.1007/978-3-031-26348-4$$2doi
001461432 035__ $$aSP(OCoLC)1372631853
001461432 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dUKAHL$$dOCLCF
001461432 049__ $$aISEA
001461432 050_4 $$aTA1634
001461432 08204 $$a006.3/7$$223/eng/20230315
001461432 1112_ $$aAsian Conference on Computer Vision$$n(16th :$$d2022 :$$cMacau, China)
001461432 24510 $$aComputer vision - ACCV 2022 :$$b16th Asian Conference on Computer Vision, Macao, China, December 4-8, 2022, proceedings.$$nPart V /$$cLei Wang, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa, editors.
001461432 264_1 $$aCham :$$bSpringer,$$c[2023]
001461432 264_4 $$c©2023
001461432 300__ $$a1 online resource (xxii, 528 pages) :$$billustrations (chiefly color).
001461432 336__ $$atext$$btxt$$2rdacontent
001461432 337__ $$acomputer$$bc$$2rdamedia
001461432 338__ $$aonline resource$$bcr$$2rdacarrier
001461432 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13845
001461432 500__ $$aInternational conference proceedings.
001461432 500__ $$aIncludes author index.
001461432 5050_ $$aIntro -- Preface -- Organization -- Contents - Part V -- Recognition: Feature Detection, Indexing, Matching, and Shape Representation -- Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer*-12pt -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Spatially-aware Few-shot Learning -- 3.2 Self-supervised Scale and Scale Discrepancy -- 3.3 Transformer-Based Spatially-Aware Pipeline -- 4 Experiments -- 4.1 Datasets -- 4.2 Performance Analysis -- 5 Conclusions -- References -- AONet: Attentional Occlusion-Aware Network for Occluded Person Re-identification*-12pt
001461432 5058_ $$a1 Introduction -- 2 Related Works -- 3 Attentional Occlusion-Aware Network -- 3.1 Landmark Patterns and Memorized Dictionary -- 3.2 Attentional Latent Landmarks -- 3.3 Referenced Response Map -- 3.4 Occlusion Awareness -- 3.5 Training and Inference -- 4 Experiments -- 4.1 Datasets and Implementations -- 4.2 Comparisons to State-of-the-Arts -- 4.3 Ablation Studies -- 5 Conclusion -- References -- FFD Augmentor: Towards Few-Shot Oracle Character Recognition from Scratch -- 1 Introduction -- 2 Related Works -- 2.1 Oracle Character Recognition -- 2.2 Few-Shot Learning
001461432 5058_ $$a2.3 Data Augmentation Approaches -- 2.4 Non-Rigid Transformation -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overview of Framework -- 3.3 FFD Augmentor -- 3.4 Training with FFD Augmentor -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Evaluation of FFD Augmented Training -- 4.3 Further Analysis of FFD Augmentor -- 4.4 Applicability to Other Problems -- 5 Conclusion -- References -- Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval*-12pt -- 1 Introduction -- 2 Related Work -- 2.1 Metric Learning -- 2.2 Few-shot Classification -- 3 Few-shot Metric Learning
001461432 5058_ $$a3.1 Metric Learning Revisited -- 3.2 Problem Formulation of Few-shot Metric Learning -- 4 Methods -- 4.1 Simple Fine-Tuning (SFT) -- 4.2 Model-Agnostic Meta-Learning (MAML) -- 4.3 Meta-Transfer Learning (MTL) -- 4.4 Channel-Rectifier Meta-Learning (CRML) -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Effectiveness of Few-shot Metric Learning -- 5.3 Influence of Domain Gap Between Source and Target -- 5.4 Few-shot Metric Learning vs. Few-shot Classification -- 5.5 Results on miniDeepFashion -- 6 Conclusion -- References
001461432 5058_ $$a3D Shape Temporal Aggregation for Video-Based Clothing-Change Person Re-identification*-12pt -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Parametric 3D Human Estimation -- 3.2 Identity-Aware 3D Shape Generation -- 3.3 Difference-Aware Shape Aggregation -- 3.4 Appearance and Shape Fusion -- 4 VCCR Dataset -- 4.1 Collection and Labelling -- 4.2 Statistics and Comparison -- 4.3 Protocol -- 5 Experiments -- 5.1 Implementation Details -- 5.2 Evaluation on CC Re-Id Datasets -- 5.3 Evaluation on Short-Term Re-Id Datasets -- 5.4 Ablation Study -- 6 Conclusion -- References
001461432 506__ $$aAccess limited to authorized users.
001461432 520__ $$aThe 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.
001461432 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 15, 2023).
001461432 650_0 $$aComputer vision$$vCongresses.
001461432 655_0 $$aElectronic books.
001461432 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001461432 655_7 $$aConference papers and proceedings.$$2lcgft
001461432 7001_ $$aWang, Lei,$$eeditor.
001461432 7001_ $$aGall, Juergen,$$eeditor.
001461432 7001_ $$aChin, Tat-Jun,$$eeditor.
001461432 7001_ $$aSato, Imari,$$eeditor.
001461432 7001_ $$aChellappa, Rama,$$eeditor.
001461432 77608 $$iPrint version:$$aWang, Lei$$tComputer Vision - ACCV 2022$$dCham : Springer,c2023$$z9783031263477
001461432 830_0 $$aLecture notes in computer science ;$$v13845.$$x1611-3349
001461432 852__ $$bebk
001461432 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-26348-4$$zOnline Access$$91397441.1
001461432 909CO $$ooai:library.usi.edu:1461432$$pGLOBAL_SET
001461432 980__ $$aBIB
001461432 980__ $$aEBOOK
001461432 982__ $$aEbook
001461432 983__ $$aOnline
001461432 994__ $$a92$$bISE