Computer Vision Systems: 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings

Attention Systems.- Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields.- MARL: Multimodal Attentional Representation Learning for Disease Prediction.- Object Localization with Attribute Preference based on Top-Down Attention.- See the silence: improving visual-only voice activity detection by optical flow and RGB fusion.- Classification and Detection.- Score to Learn: a Comparative Analysis of Scoring Functions for Active Learning in Robotics.- Enhancing the performance of image classification through features automatically learned from depth-maps.- Object Detection on TPU Accelerated Embedded Devices.- Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification.- Semantic Interpretation.- Measuring the Sim2Real gap in 3D Object classification for different 3D data representation.- Spatially-Constrained Semantic Segmentation with Topological Μaps and Visual Εmbeddings.- Knowledge-enabled generation of semantically annotated image sequences of manipulation activities from VR demonstrations.- Make It Easier: An Empirical Simplification of a Deep 3D Segmentation Network for Human Body Parts.- Video and Motion Analysis.- Video Popularity Prediction through Fusing Early Viewership with Video Content.- Action Prediction during Human-Object Interaction based on DTW and Early Fusion of Human and Object Representations.- GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids.- An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset.- Computer Vision Systems in Agriculture.- Robust Counting of Soft Fruit through Occlusions with Re-identification.- Non-destructive Soft Fruit Mass and Volume Estimation for Phenotyping in Horticulture.- Learning Image-based Contaminant Detection in Wool Fleece from Noisy Annotations.- Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network's Feature Pyramid Levels.