| 000 | 06425nam a2200445 i 4500 | ||
|---|---|---|---|
| 999 |
_c334579 _d334579 _x1 |
||
| 001 | 334579 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230102114728.0 | ||
| 006 | a|||| o|||| 10| 0 | ||
| 007 | cr nn nnnaamaa | ||
| 008 | 210225s2021 gw a o |1|| 0|eng d | ||
| 020 | _a9783030695415 | ||
| 024 | 7 |
_a10.1007/978-3-030-69541-5 _2doi |
|
| 040 |
_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
||
| 050 | 4 |
_aTA1634 _b2021 EB |
|
| 245 | 0 | 0 |
_aComputer Vision - ACCV 2020 : _b15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, Revised Selected Papers, Part V _cedited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi |
| 250 | _aFirst edition 2021 | ||
| 264 | 1 |
_aCham, Switzerland _bSpringer International Publising _c2021 |
|
| 300 |
_a1 recurso en línea (XVIII, 706 páginas) _b5 ilustraciones, 1 ilustraciones a color |
||
| 336 |
_2rdacontent _aTexto _btxt |
||
| 337 |
_2rdamedia _aelectrónico _bc |
||
| 338 |
_2rdacarrier _arecurso electrónico _bcr |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 0 |
_aImage Processing Computer Vision Pattern Recognition and Graphics ; _v12626 |
|
| 505 | 0 | _aFace, Pose, Action, and Gesture -- Video-Based Crowd Counting Using a Multi-Scale Optical Flow Pyramid Network -- RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition -- Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action-Gesture Recognition -- Unpaired Multimodal Facial Expression Recognition -- Gaussian Vector: An Efficient Solution for Facial Landmark Detection -- A Global to Local Double Embedding Method for Multi-person Pose Estimation -- Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning -- MMD based Discriminative Learning for Face Forgery Detection -- RE-Net: A Relation Embedded Deep Model for AU Occurrence and Intensity Estimation -- Learning 3D Face Reconstruction with a Pose Guidance Network -- Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation -- Faster, Better and More Detailed: 3D Face Reconstruction with Graph Convolutional Networks -- Localin Reshuffle Net: Toward Naturally and Efficiently Facial Image Blending -- Rotation Axis Focused Attention Network (RAFA-Net) for Estimating Head Pose -- Unified Application of Style Transfer for Face Swapping and Reenactment -- Multiple Exemplars-based Hallucination for Face Super-resolution and Editing -- Imbalance Robust Softmax for Deep Embedding Learning -- Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency -- Speech2Video Synthesis with 3D Skeleton Regularization and Expressive Body Poses -- 3D Human Motion Estimation via Motion Compression and Refinement -- Spatial Temporal Attention Graph Convolutional Networks with Mechanics-Stream for Skeleton-based Action Recognition -- DiscFace: Minimum Discrepancy Learning for Deep Face Recognition -- Uncertainty Estimation and Sample Selection for Crowd Counting -- Multi-Task Learning for Simultaneous Video Generation and Remote Photoplethysmography Estimation -- Video Analysis and Event Recognition -- Interpreting Video Features: A Comparison of 3D Convolutional Networks and Convolutional LSTM Networks -- Encode the Unseen: Predictive Video Hashing for Scalable Mid-Stream Retrieval -- Active Learning for Video Description With Cluster-Regularized Ensemble Ranking -- Condensed Movies: Story Based Retrieval with Contextual Embeddings -- Play Fair: Frame Contributions in Video Models -- Transforming Multi-Concept Attention into Video Summarization -- Learning to Adapt to Unseen Abnormal Activities under Weak Supervision -- TSI: Temporal Scale Invariant Network for Action Proposal Generation -- Discovering Multi-Label Actor-Action Association in a Weakly Supervised Setting -- Reweighted Non-convex Non-smooth Rank Minimization based Spectral Clustering on Grassmann Manifold -- Biomedical Image Analysis -- Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction -- Hierarchical X-Ray Report Generation via Pathology tags and Multi Head Attention -- Self-Guided Multiple Instance Learning for Weakly Supervised Thoracic Disease Classification and Localizationin Chest Radiographs -- MBNet: A Multi-Task Deep Neural Network for Semantic Segmentation and Lumbar Vertebra Inspection on X-ray Images -- Attention-Based Fine-Grained Classification of Bone Marrow Cells -- Learning Multi-Instance Sub-pixel Point Localization -- Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images. | |
| 520 | 3 | _aThe six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually. | |
| 988 | _aSpringer_Computer_2021 | ||
| 650 | 7 |
_2embne _aVisión por ordenador _vCongresos y asambleas _9159793 |
|
| 650 | 7 |
_2embne _aReconocimiento de formas _vCongresos y asambleas _9152614 |
|
| 700 | 1 |
_aIshikawa, Hiroshi _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aLiu, Cheng-Lin _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aPajdla, Tomas _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aShi, Jianbo _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 710 | 2 | _aSpringerLink | |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-69541-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
||
| 998 |
_b04/2021 _dz _eb _zSI |
||