Computer vision -- ACCV 2020 : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, revised selected papers
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書誌事項
Computer vision -- ACCV 2020 : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, revised selected papers
（Lecture notes in computer science, 12626 . LNCS sublibrary ; SL 6 . Image processing,
Springer, c2021
- pt. 5
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ACCV 2020
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注記
"The Asian Conference on Computer Vision (ACCV) 2020, originally planned to take place in Kyoto, Japan, was held online during November 30 - December 4, 2020."--Preface
Other editors: Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi
Includes bibliographical references and author index
内容説明・目次
内容説明
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.
目次
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.
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