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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 I de Hiroshi Ishikawa

Computer Vision – ACCV 2020: 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part I: Lecture Notes in Computer Science, cartea 12622

Editat de Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi
en Limba Engleză Paperback – 27 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: 9783030695248
ISBN-10: 3030695247
Pagini: 740
Ilustrații: XVIII, 740 p. 10 illus.
Dimensiuni: 155 x 235 mm
Greutate: 1.04 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

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 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.