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Shot Boundary Detection for Automatic Video Analysis of Historical Films 2nd

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Shot Boundary Detection for Automatic Video Analysis of Hist...

2nd International Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st International Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st International Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, International Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th International Conference on Image Analysis and Processing, ICIAP 2019

作者： Helm, Daniel Kampel, Martin Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria

ISBN: (纸本)9783030307530

In automatic video content analysis and film preservation, Shot Boundary Detection (SBD) is a fundamental pre-processing step. While previous research focuses on detecting Abrupt Transitions (AT) as well as Gradual Transitions (GT) in different video genres such as sports movies or news clips only few studies investigate in the detection of shot transitions in historical footage. The main aim of this paper is to create an SBD mechanism inspired by state-of-the-art algorithms which is applied and evaluated on a self-generated historical dataset as well as a publicly available dataset called Clipshots. Therefore, a three-stage pipeline is introduced consisting of a Candidate Frame Range Selection based on the network DeepSBD, Extraction of Convolutional Neural Network (CNN) Features and Similarity Calculation. A combination of pre-trained backbone CNNs such as ResNet, VGG19 and SqueezeNet with different similarity metrics like Cosine Similarity and Euclidean Distance are used and evaluated. The outcome of this paper displays that the proposed algorithm reaches promising results on detecting ATs in historical videos without the need of complex optimization and re-training processes. Furthermore, it points out the main challenges concerning historical footage such as damaged film reels, scratches or splices. The results of this paper contribute a significant base for future research on automatic video analysis of historical videos. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

Toward an Evaluation Science for Generative AI Systems

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Bridge 2025年第1期55卷 9-18页

作者： Weidinger, Laura Raji, Deb Wallach, Hanna Mitchell, Margaret Wang, Angelina Salaudeen, Olawale Bommasani, Rishi Koyejo, Sanmi Isaac, William Google DeepMind United Kingdom University of California Berkeley United States Microsoft Research United States Hugging Face United States Human-Centered Artificial Intelligence and RegLab Stanford University United States Laboratory for Information and Decision Systems at the Schwarzman College of Computing Massachusetts Institute of Technology United States Stanford Center for Research on Foundation Models United States Stanford University United States

Mulseplayer: A Multi-Sensorial Media Content Delivery Solution to Enhance End-User Quality of Experience

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Mulseplayer: A Multi-Sensorial Media Content Delivery Soluti...

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Cristina Hava Muntean Irina Tal Diana Bogusevschi Marilena Bratu Ting Bi Gabriel-Miro Muntean School of Computing National College of Ireland Dublin Ireland Lero School of Computing Dublin City University Dublin Ireland Department of Visual and Human-Centred Computing Dundalk Institute of Technology Ireland Faculty of Psychology and Educational Sciences University of Bucharest Bucharest Romania School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

abstract-The rapid evolution of technology and its integration into educational settings has paved the way for innovative approaches to content delivery. This research work delves into the cutting-edge domain of multi-sensorial media content (mulsemedia) delivery applied in the education domain, where traditional pedagogical methods are reimagined to engage learners and increase their Quality of Experience through a myriad of sensory experiences. A novel Mulsemedia Content Delivery solution (Mulseplayer) is presented in this paper that was developed as part of a large-scale European project. Results from a pilot conducted with 64 participants indicate that the Mulsemedia-based teaching approach enhanced students’ Quality of Experience and improved the overall learning process.

关键词： Electronic learning Interactive systems Education Europe Games Media Sensor systems

Deep Learning: Classification and Automated Detection Earlier of Alzheimer's Disease Using Brain MRI Images

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Deep Learning: Classification and Automated Detection Earlie...

International Laser technology and Optics Symposium in Conjunction with Photonics Meeting 2020, ILATOSPM 2020

作者： Kadhim, Karrar A. Mohamed, Farhan Khudhair, Zaid Nidhal Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam ja'Afar Al-sadiq University Baghdad Iraq UTM-IRDA MaGICX Institute of Human Centered Engineering Universiti Teknologi Malaysia

Alzheimer's disease (AD) progression can be avoided by conducting diagnosis beforehand. This diagnosis acquired quick preventive care which could be possibly done by specialists. Fast and accurate evaluation at the earliest and most challenging stage were required to detect in the diagnosis of AD. In this paper, previous studies were reviewed into a better approach that recognizes the presence of disease in sagittal magnetic resonance automatically (MRI) images that are unusually used. The MRI brain images were used to identify and distinguish characteristics using a range of characteristics recognition techniques. The review of research papers on Alzheimer's Disease published in reputable journals from 2017 to 2020 were presented and discussion of various strategies related to the latest tools used in early diagnosis is our main focus in this study, which could enable researchers to understand current algorithms and techniques in this area, and eventually develop new and more effective algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Magnetic resonance

AVTENet: A human-Cognition-Inspired Audio-visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

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arXiv 2024年

作者： Wisnu, Dyah A.M.G. Rini, Stefano Zezario, Ryandhimas E. Wang, Hsin-Min Tsao, Yu Taiwan International Graduate Program—Social Network and Human Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

This paper introduces HAAQI-Net, a non-intrusive deep learning-based music audio quality assessment model for hearing aid users. Unlike traditional methods like the Hearing Aid Audio Quality Index (HAAQI) that require intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. By utilizing a Bidirectional Long Short-Term Memory (BLSTM) architecture with attention mechanisms and features extracted from the pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. Experimental results demonstrate HAAQI-Net’s effectiveness, achieving a Linear Correlation Coefficient (LCC) of 0.9368, a Spearman’s Rank Correlation Coefficient (SRCC) of 0.9486, and a Mean Squared Error (MSE) of 0.0064 and inference time significantly reduces from 62.52 to 2.54 seconds. To address computational overhead, a knowledge distillation strategy was applied, reducing parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores like the Mean Opinion Score (MOS) through fine-tuning. This adaptation significantly improved prediction accuracy, validated through statistical analysis. Furthermore, the robustness of HAAQI-Net was evaluated under varying Sound Pressure Level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a scalable solution for music audio quality assessment in hearing aid applications, contributing to efficient and accurate models in audio signal processing and hearing aid technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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arXiv 2020年

作者： Sebernegg, Anna Kan, Peter Kaufmann, Hannes Virtual and Augmented Reality Group Institute of Visual Computing Human-Centered Technology Vienna University of Technology Austria

The analysis of human motion opens up a wide range of possibilities, such as realistic training simulations or authentic motions in robotics or animation. One of the problems underlying motion analysis is the meaningful comparison of actions based on similarity measures. Since the motion analysis is application-dependent, it is essential to find the appropriate motion similarity method for the particular use case. This state of the art report provides an overview of human motion analysis and different similarity modeling methods, while mainly focusing on approaches that work with 3D motion data. The survey summarises various similarity aspects and features of motion and describes approaches to measuring the similarity between two actions. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion analysis

How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and human Perception

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arXiv 2024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 Taipei Taiwan The Department of Computer Science National Chengchi University Taipei Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan The Institute of Information Science Academia Sinica Taiwan

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learning-based forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks. © 2024, CC BY.

关键词： Audiovisual

Exploring the Impact of Non-Verbal Virtual Agent Behavior on User Engagement in Argumentative Dialogues

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arXiv 2024年

作者： Aicher, Annalena Bea Minker, Wolfgang Matsuda, Yuki André, Elisabeth Yasumoto, Keichii Ultes, Stefan Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany Institute of Communications Engineering Ulm University Ulm Germany Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan Ubiquitous Computing Systems Laboratory NAIST Ikoma Nara Japan Natural Language Generation and Dialogue Systems University of Bamberg Bamberg Germany

Engaging in discussions that involve diverse perspectives and exchanging arguments on a controversial issue is a natural way for humans to form opinions. In this process, the way arguments are presented plays a crucial role in determining how engaged users are, whether the interaction takes place solely among humans or within human-agent teams. This is of great importance as user engagement plays a crucial role in determining the success or failure of cooperative argumentative discussions. One main goal is to maintain the user’s motivation to participate in a reflective opinion-building process, even when addressing contradicting viewpoints. This work investigates how non-verbal agent behavior, specifically co-speech gestures, influences the user’s engagement and interest during an ongoing argumentative interaction. The results of a laboratory study conducted with 56 participants demonstrate that the agent’s co-speech gestures have a substantial impact on user engagement and interest and the overall perception of the system. Therefore, this research offers valuable insights for the design of future cooperative argumentative virtual agents. © 2024, CC BY-NC-ND.

关键词： Chatbots