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Articles by Peter Eisert
Proceedings of the 22nd ACM SIGGRAPH European Conference on Visual Media Production
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Visual Fire Safety Inspection Framework Using Computer Vision Algorithms
Practical Applications Ensuring fire safety in buildings requires accurate and efficient inspections, yet traditional methods often rely on time-consuming manual processes. This research presents a new approach to automate fire safety inspections using advanced technologies. By combining machine learning (ML) with building information modeling (BIM), the framework detects and analyzes fire safety equipment (FSE), such as fire extinguishers, directly from images.
Computer Science > Machine Learning
[Submitted on 17 Mar 2025 ( v1 ), last revised 26 Jun 2026 (this version, v3)] Title:Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians View a PDF of the paper titled Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians, by Kai Uwe Barthel and 2 other authors View PDF HTML (experimental) Abstract:Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing...
Proceedings of 21st ACM SIGGRAPH Conference on Visual Media Production
Abstract Skip Table Of Content Section Select All SESSION: Session 1 SESSION: Session 2 SESSION: Session 3 Index Terms Proceedings of 21st ACM SIGGRAPH Conference on Visual Media Production Index terms have been assigned to the content through auto-classification. Recommendations Acceptance Rates YearSubmittedAcceptedRate CVMP '17 16 10 63% CVMP '13 28 18 64% CVMP '12 23 12 52% Overall674060%
Data Fusion for Cross-Domain Real-Time Object Detection on the Edge
4.2. Models We compared various popular object-detection models such as SSD (Single Shot MultiBox Detector) [41], YOLOv5 (You Only Look Once) [42] and Faster-RCNN (Faster Region-Based CNN) [43], as well as different model sizes of YOLOv5 and SSD. The model chosen for our visual analysis system was YOLOv5 [42], which belongs to a family of object-detection architectures and models. The network architecture of YOLO is divided into three parts: a backbone, a neck, and a head.
[2306.10006] Unsupervised Learning of Style-Aware Facial Animation from Real Acting Performances
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