检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Realistic shrinking city layouts

引用

Computers and Graphics (Pergamon) 2020年 86卷 15-26页

作者： Pueyo, Oriol Sabrià, Albert Pueyo, Xavier Patow, Gustavo Wimmer, Michael University of Girona. ViRVIG. Dept. IMAE GironaMontilivi 17003 Spain TU Wien. Institute of Visual Computing and Human-Centered Technology Austria

One important use of realistic city environments is in the video game industry. When a company works on a game whose action occurs in a real-world environment, a team of designers usually creates a simplified model of the real city. In particular, the resulting city is desired to be smaller in extent to increase playability and fun, avoiding long walks and "boring" neighborhoods. This is manual work, usually started from scratch, where the first step is to take the original city map as input, and from it create the street network of the final city, removing insignificant streets and bringing important places closer together in the process. This first draft of the city street network is like a kind of skeleton with the most important places connected, from which the artist can (and should) start working until the desired result is obtained. In this paper, we propose a solution to automatically generate such a first simplified street network draft. This is achieved by using the well-established seam-carving technique applied to a skeleton of the city layout, built with the important landmarks and streets of the city. The output that our process provides is a street network that reduces the city area as much as the designer wants, preserving landmarks and key streets, while keeping the relative positions between them. For this, we run a shrinking process that reduces the area in an irregular way, prioritizing the removal of areas of less importance. This way, we achieve a smaller city but retain the essence of the real-world one. To further help the designer, we also present an automatic filling algorithm that adds unimportant streets to the shrunken skeleton. © 2019 Elsevier Ltd

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

Unmasking Illusions: Understanding human Perception of Audiovisual Deepfakes

arXiv

引用

arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

来源：评论

学校读者我要写书评

暂无评论

Understanding Audiovisual Deepfake Detection: Techniques, Challenges, human Factors and Perceptual Insights

arXiv

引用

arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan The Department of Computer Science National Chengchi University Taipei11529 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. © 2024, CC BY.

关键词： Audiovisual

来源：评论

学校读者我要写书评

暂无评论

AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection

arXiv

引用

arXiv 2023年

作者： Shahzad, Sahibzada Adil Hashmi, Ammarah Peng, Yan-Tsung Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan The Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Multimodal manipulations (also known as audiovisual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely detection is crucial. The damage to either modality (i.e., visual or audio) can only be discovered through multi-modal models that can exploit both pieces of information simultaneously. Previous methods mainly adopt uni-modal video forensics and use supervised pre-training for forgery detection. This study proposes a new method based on a multi-modal self-supervised-learning (SSL) feature extractor to exploit inconsistency between audio and visual modalities for multi-modal video forgery detection. We use the transformer-based SSL pre-trained Audio-visual HuBERT (AV-HuBERT) model as a visual and acoustic feature extractor and a multi-scale temporal convolutional neural network to capture the temporal correlation between the audio and visual modalities. Since AV-HuBERT only extracts visual features from the lip region, we also adopt another transformer-based video model to exploit facial features and capture spatial and temporal artifacts caused during the deepfake generation process. Experimental results show that our model outperforms all existing models and achieves new state-of-the-art performance on the FakeAVCeleb and DeepfakeTIMIT datasets. © 2023, CC BY.

关键词： Audio acoustics

来源：评论

学校读者我要写书评

暂无评论

Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation

Deep Sensor Fusion with Pyramid Fusion Networks for 3D Seman...

引用

IEEE Symposium on Intelligent Vehicle

作者： Hannah Schieber Fabian Duerr Torsten Schoen Jü rgen Beyerer Human-Centered Computing and Extended Reality Friedrich-Alexander University (FAU) Erlangen-N&#x00FC rnberg Erlangen Germany Vision and Fusion Laboratory Karlsruhe Institute of Technology Karlsruhe Germany Research Institute Almotion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation (IOSB) Fraunhofer Center of Machine Learning Karlsruhe Germany

Robust environment perception for autonomous vehicles is a tremendous challenge, which makes a diverse sensor set with e.g. camera, lidar and radar crucial. In the process of understanding the recorded sensor data, 3D semantic segmentation plays an important role. Therefore, this work presents a pyramid-based deep fusion architecture for lidar and camera to improve 3D semantic segmentation of traffic scenes. Individual sensor backbones extract feature maps of camera images and lidar point clouds. A novel Pyramid Fusion Backbone fuses these feature maps at different scales and combines the multimodal features in a feature pyramid to compute valuable multimodal, multi-scale features. The Pyramid Fusion Head aggregates these pyramid features and further refines them in a late fusion step, incorporating the final features of the sensor backbones. The approach is evaluated on two challenging outdoor datasets and different fusion strategies and setups are investigated. It outperforms recent range view based lidar approaches as well as all so far proposed fusion strategies and architectures.

关键词： Point cloud compression Image segmentation Laser radar Three-dimensional displays Head Fuses Semantics

来源：评论

学校读者我要写书评

暂无评论

Person Localisation under Fragmented Occlusion*

Person Localisation under Fragmented Occlusion*

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Roman Pflugfelder Jonas Auer Center of Digital Safety & Security Giefinggasse 4 AIT Austrian Institute of Technology Vienna Austria TU Wien Inst. of Visual Comp. & Human-Centered Technology Favoritenstr. 9/193-1 Vienna Austria

ISBN: (纸本)9781665433976

Occlusion is a fundamental challenge in object recognition. Fragmented occlusion is much more challenging than ordinary partial occlusion and occurs in natural environments such as forests. Less is known in computer vision about fragmented occlusion and object recognition. Interestingly, human vision has far more explored this problem as the human visual system evolved to fragmented occlusion at the times when mankind occupied rainforest. A motivating example of fragmented occlusion is object detection through foliage which is an essential requirement in green border surveillance. Instead of detection, this paper studies the simpler problem of localisation with persons. A neural network based method shows a precision larger than 90% on new image sequences capturing the problem. This is possible by two observations: (i) fragmented occlusion is unsolvable in single images without temporal information, and (ii) colour quantisation and colour swapping is essential to force the training of the network to learn from the available temporal information in the spatiotemporal data.

关键词： Training visualization Image color analysis Surveillance Vegetation mapping Detectors visual systems

来源：评论

学校读者我要写书评

暂无评论

Precomputed fast rejection ray-triangle intersection

引用

Graphics and visual computing 2022年 6卷

作者： Thomas Alois Pichler Andrej Ferko Michal Ferko Peter Kán Hannes Kaufmann Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Department of Algebra and Geometry Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Bratislava Slovakia

We propose a ray-triangle intersection algorithm with fast-rejection strategies. We intersect the ray with the triangle plane, then transform the intersection problem into 2D by applying a transformation matrix to the ray-plane intersection point. For 2D transformation, we study two different approaches. The first approach uses a transformation matrix which transforms the triangle into a unit triangle. Then, simple 2D tests are performed. The second approach transforms the triangle into a 2D triangle while preserving similarity. This allows us to prune (i.e., to clip away) areas surrounding the triangle, determining whether the transformed intersection point lies within the triangle. We discuss several optimizations for this pruning approach. We implemented both approaches into the CPU-based ray-tracing framework PBRT, version 3, and we performed a time-based comparison against PBRT’s default intersection algorithm and Baldwin and Weber’s algorithm. The results show that our algorithms are faster than the default algorithm. They are comparable to or slightly slower than Baldwin and Weber’s algorithm, however, the pruning approach produces watertight results and may be further optimized. Moreover, additional CPU/GPU experiments outside of PBRT document promising speedup over the standard Möller–Trumbore algorithm in areas like ray-casting or collision detection.

关键词： Ray-Triangle Intersection Ray shooting Collision Point location Intersections

来源：评论

学校读者我要写书评

暂无评论

Scalable Class-Centric visual Interactive Labeling

引用

Computers & Graphics 2025年 129卷

作者： Matthias Matt Jana Sedlakova Jürgen Bernard Matthias Zeppelzauer Manuela Waldner Institute of Visual Computing & Human-Centered Technology TU Wien Vienna 1040 Austria Department of Informatics University of Zurich Zurich 8006 Switzerland Digital Society Initiative University of Zurich Zurich 8006 Switzerland Institute of Creative Media Technologies St. Pölten University of Applied Sciences St. Pölten 3100 Austria

Large unlabeled datasets demand efficient and scalable data labeling solutions, in particular when the number of instances and classes is large. This leads to significant visual scalability challenges and imposes a high cognitive load on the users. Traditional instance-centric labeling methods, where (single) instances are labeled in each iteration struggle to scale effectively in these scenarios. To address these challenges, we introduce cVIL, a Class-Centric visual Interactive Labeling methodology designed for interactive visual data labeling. By shifting the paradigm from assigning-classes-to-instances to assigning-instances-to-classes , cVIL reduces labeling effort and enhances efficiency for annotators working with large, complex and class-rich datasets. We propose a novel visual analytics labeling interface built on top of the conceptual cVIL workflow, enabling improved scalability over traditional visual labeling. In a user study, we demonstrate that cVIL can improve labeling efficiency and user satisfaction over instance-centric interfaces. The effectiveness of cVIL is further demonstrated through a usage scenario, showcasing its potential to alleviate cognitive load and support experts in managing extensive labeling tasks efficiently.

关键词： visual analytics visual-interactive data labeling Class-centric labeling Property measures Interactive machine learning

来源：评论

学校读者我要写书评

暂无评论

Explaining Automated Gender Classification of human Gait

arXiv

引用

arXiv 2022年

作者： Horst, Fabian Slijepcevic, Djordje Zeppelzauer, Matthias Raberger, Anna-Maria Lapuschkin, Sebastian Samek, Wojciech Schöllhorn, Wolfgang Immanuel Breiteneder, Christian Horsak, Brian Department of Training and Movement Science Institute of Sport Science Johannes Gutenberg-University Mainz Mainz Germany Institute of Creative Media Technologies Department of Media & Digital Technologies St. Pölten University of Applied Sciences St. Pölten Austria Institute of Health Sciences Department of Health Sciences St. Pölten University of Applied Sciences St. Pölten Austria Department of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Berlin Germany Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria

State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions. This "black-box" characteristic makes it impossible to understand on which input patterns, ML models base their predictions. The present study investigates whether Explainable Artificial Intelligence methods, i.e., Layer-wise Relevance Propagation (LRP), can be useful to enhance the explainability of ML predictions in gait classification. The research question was: Which input patterns are most relevant for an automated gender classification model and do they correspond to characteristics identified in the literature? We utilized a subset of the GAITREC dataset containing five bilateral ground reaction force (GRF) recordings per person during barefoot walking of 62 healthy participants: 34 females and 28 males. Each input signal (right and left side) was min-max normalized before concatenation and fed into a multi-layer Convolutional Neural Network (CNN). The classification accuracy was obtained over a stratified ten-fold cross-validation. To identify gender-specific patterns, the input relevance scores were derived using LRP. The mean classification accuracy of the CNN with 83.3% showed a clear superiority over the zero-rule baseline of 54.8%. © 2022, CC BY-NC-SA.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：