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[null,null,"Titolo",null,"Computer Vision – ACCV 2020 : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part I / / edited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-69525-5"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (755 pages) : illustrations"]
[null,null,"Collana",null,"Image Processing, Computer Vision, Pattern Recognition, and Graphics, , 3004-9954 ; ; 12622"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Visió per ordinador"]
[{"$nulls":4},"Reconeixement de formes (Informàtica)"]
[{"$nulls":4},"Congressos"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"3D Computer Vision -- Weakly-supervised Reconstruction of 3D Objects with Large Shape Variation from Single In-the-Wild Images -- HPGCNN: Hierarchical Parallel Group Convolutional Neural Networks for Point Clouds Processing -- 3D Object Detection and Pose Estimation of Unseen Objects in Color Images with Local Surface Embeddings -- Reconstructing Creative Lego Models, George Tattersall -- Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People -- Learning Global Pose Features in Graph "]
[{"$nulls":4},"Convolutional Networks for 3D Human Pose Estimation -- SGNet: Semantics Guided Deep Stereo Matching -- Reconstructing Human Body Mesh from Point Clouds by Adversarial GP Network -- SDP-Net: Scene Flow Based Real-time Object Detection and Prediction from Sequential 3D Point Clouds -- SAUM: Symmetry-Aware Upsampling Module for Consistent Point Cloud Completion -- Faster Self-adaptive Deep Stereo -- AFN: Attentional Feedback Network based 3D Terrain Super-Resolution -- Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds -- Anatomy and Geometry Constrained One-Stage Framework for 3D Human Pose Estimation -- DeepVoxels++: Enhancing the Fidelity of Novel View Synthesis from 3D Voxel Embeddings -- Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media -- Homography-based Egomotion Estimation Using Gravity and SIFT Features -- Mapping of Sparse 3D Data using Alternating Projection -- Best Buddies Registration for Point Clouds -- Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data -- Dynamic Depth Fusion and Transformation for Monocular 3D Object Detection -- Attention-Aware Feature Aggregation for Real-time Stereo Matching on Edge Devices -- FKAConv: Feature-Kernel Alignment for Point Cloud Convolution -- Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks -- IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image -- Attended-Auxiliary Supervision Representation for Face Anti-spoofing -- 3D Object Detection from Consecutive Monocular Images -- Data-Efficient Ranking Distillation for Image Retrieval -- Quantum Robust Fitting -- HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning -- Segmentation and Grouping -- RGB-D Co-attention Network for Semantic Segmentation -- Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation -- Dense Dual-Path Network for Real-time Semantic Segmentation -- Learning More Accurate Features for Semantic Segmentation in CycleNet -- 3D Guided Weakly Supervised Semantic Segmentation -- Real-Time Segmentation Networks should be Latency Aware -- Mask-Ranking Network for Semi-Supervised Video Object Segmentation -- SDCNet: Size Divide and Conquer Network for Salient Object Detection -- Bidirectional Pyramid Networks for Semantic Segmentation -- DEAL: Difficulty-aware Active Learning for Semantic Segmentation -- EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion -- Local Context Attention for Salient Object Segmentation -- Generic Image Segmentation in Fully Convolutional Networks by Superpixel Merging Map."]
[null,null,null,"Sommario/riassunto","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."]