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    1. Data och IT
    2. Systemvetenskap och AI

    Computer Vision – ACCV 2020

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

    AvHiroshi Ishikawa,Cheng-Lin Liu

    Häftad, Engelska, 2021

    Del 12626 i serien Lecture Notes in Computer Science

    1 116 kr

    Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

    Beskrivning

    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 groupingPart II: low-level vision, image processing; motion and trackingPart III: recognition and detection; optimization, statistical methods, and learning; robot visionPart IV: deep learning for computer vision, generative models for computer visionPart V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysisPart VI: applications of computer vision; vision for X; datasets and performance analysis*The conference was held virtually.

    Produktinformation

    • Utgivningsdatum:2021-02-26
    • Mått:155 x 235 x 39 mm
    • Vikt:1 077 g
    • Format:Häftad
    • Språk:Engelska
    • Serie:Lecture Notes in Computer Science
    • Antal sidor:706
    • Förlag:Springer Nature Switzerland AG
    • ISBN:9783030695408

    Utforska kategorier

    • Systemvetenskap och AI inom Data och IT
    • Nätverk och kommunikation inom Data och IT

    Innehållsförteckning

    • 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.