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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

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Rank-based Decomposable Losses in Machine Learning: A Survey

arXiv

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arXiv 2022年

作者： Hu, Shu Wang, Xin Lyu, Siwei The Department of Computer Information and Graphics Technology Purdue School of Engineering and Technology Indiana University-Purdue University IndianapolisIN46202 United States The Heinz College of Information Systems and Public Policy Carnegie Mellon University PittsburghPA15213 United States The Department of Computer Science and Engineering University at Buffalo SUNY BuffaloNY14260 United States

Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses vs. aggregate losses. The individual loss measures the quality of the model on a sample, while the aggregate loss combines individual losses/scores over each training sample. Both have a common procedure that aggregates a set of individual values to a single numerical value. The ranking order reflects the most fundamental relation among individual values in designing losses. In addition, decomposability, in which a loss can be decomposed into an ensemble of individual terms, becomes a significant property of organizing losses/scores. This survey provides a systematic and comprehensive review of rank-based decomposable losses in machine learning. Specifically, we provide a new taxonomy of loss functions that follows the perspectives of aggregate loss and individual loss. We identify the aggregator to form such losses, which are examples of set functions. We organize the rank-based decomposable losses into eight categories. Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses. We describe general formulas for these losses and connect them with existing research topics. We also suggest future research directions spanning unexplored, remaining, and emerging issues in rank-based decomposable losses. Copyright © 2022, The Authors. All rights reserved.

关键词： Aggregates

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Either Or: Interactive Articles or Videos for Climate science Communication

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computer graphics Forum 2025年

作者： Poehls, J. Meuschke, M. Carvalhais, N. Lawonn, K. Max Planck Institute for Biogeochemistry Department of Biogeochemical Integration Germany University of Magdeburg Department of Simulation and Graphics Germany University of Jena Institute of Computer Science Germany

Effective communication of climate science is critical as climate-related disasters become more frequent and severe. Translating complex information, such as uncertainties in climate model predictions, into formats accessible to diverse audiences is key to informed decision-making and public engagement. This study investigates how different teaching formats can enhance understanding of these uncertainties. This study compares two multimodal strategies: (1) a text-image format with interactive components and (2) an explainer video combining dynamic visuals with narration. Participants' immediate and delayed retention (one week) and engagement are assessed to determine which format offers greater saliency. Sample analysis (n = 622) displayed equivalent retention by viewers between both formats. Metrics assessing interactivity found no correlation between interactivity and information retention. However, a stark contrast was observed in the time viewers spent engaging with each format. The video format was 29% more efficient with information taught over a period of time vs. the article. Additionally, retention on the video format worsened with age (P = 0.004) while retention on the article format improved with education (P = 0.038). These results align with previous findings in literature. © 2025 The Author(s). computer graphics Forum published by Eurographics - The European Association for computer graphics and John Wiley & Sons Ltd.

关键词： Signaling

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Object Detection at Sea Using Ensemble Methods Across Spectral Ranges

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IFAC-PapersOnLine 2021年第16期54卷 1-6页

作者： Frederik E.T. Schöller Martin K. Plenge-Feidenhans’l Jonathan D. Stets Mogens Blanke Automation and Control Group Department of Electrical Engineering Technical University of Denmark DK-2800 Kgs. Lyngby Denmark Section of Image Analysis and Computger Graphics Department of Applied Mathematics and Computer Science DTU

Having the option of a temporally unmanned bridge when conditions allow, while maintaining or even enhancing navigational safety, is a long term aim in the maritime industry. Such a system requires excellent perception of the environment using an array of sensors. This paper investigates performance of object detection at sea using electro-optical sensors in relevant spectral ranges and discusses how missed detection risk is minimised for objects within navigation range. Using a combination of cameras in visible, near- and far infrared ranges, convolutional neural networks are employed for object detection. Ensemble techniques are suggested to minimise the amount of missed detections and it is shown how optimisation of confidence thresholds can be used to increase performance. The results are based on image data from vessels in near-coast operation in Danish waters.

关键词： Multi-modal sensor fusion Deep learning Detection performance Object detection at sea computer vision Autonomous Marine Vehicles

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Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions

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computers in Biology and Medicine 2024年 171卷 108199-108199页

作者： Saad, Fatima Frysch, Robert Saalfeld, Sylvia Kellnberger, Stephan Schulz, Jessica Fahrig, Rebecca Bhadra, Krish Nürnberger, Andreas Rose, Georg Institute for Medical Engineering Otto-von-Guericke University Magdeburg Germany Forschungscampus STIMULATE Otto-von-Guericke University Magdeburg Germany Department of Simulation and Graphics Otto-von-Guericke University Magdeburg Germany Siemens Healthcare GmbH Forchheim Germany CHI Memorial Rees Skillern Cancer Institute Chattanooga United States Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting. © 2024

关键词： Image registration

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Condition attributes, properties of decision rules, and discretisation: Analysis of relations and dependencies

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Procedia computer science 2021年 192卷 3922-3931页

作者： Beata Zielosko Urszula Stańczyk University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

When mining of input data is focused on rule induction, knowledge, discovered in exploration of existing patterns, is stored in combinations of certain conditions on attributes included in rule premises, leading to specific decisions. Through their properties, such as lengths, supports, cardinalities of rule sets, inferred rules characterise relations detected among variables. The paper presents research dedicated to analysis of these dependencies, considered in the context of various discretisation methods applied to the input data from stylometric domain. For induction of decision rules from data, Classical Rough Set Approach was employed. Next, based on rule properties, several factors were proposed and evaluated, reflecting characteristics of available condition attributes. They allowed to observe how variables and rule sets changed depending on applied discretisation algorithms.

关键词： Discretisation Decision rule Rule length Support Attribute Rough Sets

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Determining the Priority of Factors Influencing the Selection of Information Technology for Distance Education

Determining the Priority of Factors Influencing the Selectio...

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International computer sciences and Information Technologies (CSIT)

作者： Oleksandr Tymchenko Orest Khamula Svitlana Vasiuta Olha Sosnovska Solomiya Dorosh Mykhailo Drimaylo Department of Safety Engineering University of Warmia and Mazury Olsztyn Poland Department of Computer science and Information technology Ukrainian Academy of Printing Lviv Ukraine Department of Entrepreneurship and Marketing Ukrainian Academy of Printing Lviv Ukraine Department of Book and Easel Graphics Ukrainian Academy of Printing Lviv Ukraine

ISBN: (纸本)9798350334326

This article considers the task of determining the priority of factors influencing the selection of information technology for distance education. Since this process is one of those ones that are difficult to describe in the form of mathematical formulas, it is suggested to use the graph method and the method of analysis of hierarchies for calculations. These methods have shown themselves to be good in solving this kind of *** the course of the conducted survey among the participants of the educational process, eight factors are singled out, which, according to the correspondents, have a certain weight when selecting the information technology for distance education. Thus, such factors are singled out: communication tools; educational facilities; user data management; usability; adaptation; technical aspects; administration; course management. As the conducted studies show, the factor of usability acquires the highest priority level. The factor of course management, which is on the fourth step of the hierarchy, is at a lower level in terms of importance. According to calculations, the factors of the first hierarchy level turn out to be the least important: communication tools; educational facilities. These research findings are important for those educational institutions that are at a crossroads in their choice and they will help to decide on such information technologies.

关键词： Computational modeling Education Analytic hierarchy process Information technology Usability Task analysis

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Decoupling Contact for Fine-Grained Motion Style Transfer

arXiv

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arXiv 2024年

作者： Tang, Xiangjun Wu, Linjun Wang, He Wu, Yiqian Hu, Bo Li, Songnan Gong, Xu Liao, Yuchen Kou, Qilong Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University China Department of Computer Science UCL Centre for Artificial Intelligence University College London United Kingdom Tencent Technology Co. Ltd. China State Key Lab of CAD&CG Zhejiang University ZJU-Tencent Game and Intelligent Graphics Innovation Technology Joint Lab China

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to express the style vividly while enhancing motion naturalness and quality. However, it is unknown how to decouple and control contact to achieve fine-grained control in motion style transfer. In this paper, we present a novel style transfer method for fine-grained control over contacts while achieving both motion naturalness and spatial-temporal variations of style. Based on our empirical evidence, we propose controlling contact indirectly through the hip velocity, which can be further decomposed into the trajectory and contact timing, respectively. To this end, we propose a new model that explicitly models the correlations between motions and trajectory/contact timing/style, allowing us to decouple and control each separately. Our approach is built around a motion manifold, where hip controls can be easily integrated into a Transformer-based decoder. It is versatile in that it can generate motions directly as well as be used as post-processing for existing methods to improve quality and contact controllability. In addition, we propose a new metric that measures a correlation pattern of motions based on our empirical evidence, aligning well with human perception in terms of motion naturalness. Based on extensive evaluation, our method outperforms existing methods in terms of style expressivity and motion quality. Copyright © 2024, The Authors. All rights reserved.

关键词： computer games

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Inferring the 3D Standing Spine Posture from 2D Radiographs 23rd

Inferring the 3D Standing Spine Posture from 2D Radiographs

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Bayat, Amirhossein Sekuboyina, Anjany Paetzold, Johannes C. Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. Department of Informatics Technical University of Munich Munich Germany Department of Neuroradiology Klinikum rechts der Isar Munich Germany Institute of Computer Graphics and Vision Graz University of Technology Graz Austria School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9783030597245

The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2D projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3D vertebral shape from CT imaging for synthesizing an upright 3D model of spine, loaded naturally. Specifically, we propose a novel neural network architecture working vertebra-wise, termed TransVert, which takes orthogonal 2D radiographs and infers the spines 3D posture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 95.52 %, indicating an almost perfect 2D-to-3D domain translation. Deploying our model on clinical radiographs, we successfully synthesise full-3D, upright, patient-specific spine models for the first time. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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EFaR 2023: Efficient Face Recognition Competition

arXiv

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

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