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Computer Vision – ACCV 2020: 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part V de Hiroshi Ishikawa

Computer Vision – ACCV 2020: 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part V: Lecture Notes in Computer Science, cartea 12626

Editat de Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi
en Limba Engleză Paperback – 26 feb 2021
The 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.
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Specificații

ISBN-13: 9783030695408
ISBN-10: 3030695409
Pagini: 706
Ilustrații: XVIII, 706 p. 5 illus., 1 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 1 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

Face, 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.