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Hyper-parameter optimization on viola jones algorithm for gesture recognition

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Hyper-parameter optimization on viola jones algorithm for ge...

International Conference on Modelling, Simulation and intelligent Computing, MoSICom 2020

作者： Pande, Aditya Rout, B.K. Das, Sangram K. Department of Mechanical Engineering Center for Robotics and Intelligent Systems Birla Institute of Technology and Science Pilani Campus PilaniRajasthan India

ISBN: (纸本)9789811547744

The problem of features, objects, gestures, and face detection has been tackled using a numerous vision-based algorithms available in literature. Each of these algorithms requires a set of hyper-parameters, which need to be set on the basis of trial and error such that the results provide best performance to a situation. Mostly, researchers use trial and error approach to satisfactory result and solve the above problems. In this work, an approach has been suggested to determine an optimum set of hyper-parameters, which will provide a starting point for anyone using Viola Jones algorithm for hand gesture recognition or similar endeavors. This will reduce the time spent in searching for the best combination of hyper-parameters. © Springer Nature Singapore Pte Ltd 2020.

关键词： computer vision

Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

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Improving Referring Expression Comprehension by Suppressing ...

3rd International Conference on Electronic Information Engineering and computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

Stochastic orderings between two finite mixture models with inverted-Kumaraswamy distributed components

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arXiv 2023年

作者： Bhakta, Raju Kundu, Pradip Kayal, Suchandan Alizadeh, Morad Department of Mathematics National Institute of Technology Rourkela Odisha Rourkela769008 India School of Computer Science and Engineering XIM University Odisha Bhubaneswar India Department of Statistics Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75169 Iran

In this paper, we consider two finite mixture models (FMMs), with inverted-Kumaraswamy distributed components’ lifetimes. Several stochastic ordering results between the FMMs have been obtained. Mainly, we focus on three different cases in terms of the heterogeneity of parameters. The usual stochastic order between the FMMs have been established when heterogeneity presents in one parameter as well as two parameters. In addition, we have also studied ageing faster order in terms of the reversed hazard rate between two FMMs when heterogeneity is in two parameters. For the case of heterogeneity in three parameters, we obtain the comparison results based on reversed hazard rate and likelihood ratio orders. The theoretical developments have been illustrated using several examples and *** Codes 60E15, 90B25 Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

gDNA: Towards Generative Detailed Neural Avatars

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arXiv 2022年

作者： Chen, Xu Jiang, Tianjian Song, Jie Yang, Jinlong Black, Michael J. Geiger, Andreas Hilliges, Otmar ETH Zürich Department of Computer Science Switzerland University of Tübingen Germany Max Planck Institute for Intelligent Systems Tübingen Germany

To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is challenging due to the diversity of clothed body shapes, their complex articulations, and the resulting rich, yet stochastic geometric detail in clothing. Hence, current methods that represent 3D people do not provide a full generative model of people in clothing. In this paper, we propose a novel method that learns to generate detailed 3D shapes of people in a variety of garments with corresponding skinning weights. Specifically, we devise a multi-subject forward skinning module that is learned from only a few posed, un-rigged scans per subject. To capture the stochastic nature of high-frequency details in garments, we leverage an adversarial loss formulation that encourages the model to capture the underlying statistics. We provide empirical evidence that this leads to realistic generation of local details such as wrinkles. We show that our model is able to generate natural human avatars wearing diverse and detailed clothing. Furthermore, we show that our method can be used on the task of fitting human models to raw scans, outperforming the previous state-of-the-art. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

A Comparative Study on the Efficiency of the Modified COMET in Decision-Making

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Procedia computer science 2024年 246卷 103-112页

作者： Bartosz Paradowski Paweł Olender Wojciech Sałabun Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland

Decision-making is a high-risk area and, simultaneously, a process that cannot be ignored. Risks often arise under challenging situations where ranking the options under consideration is a complex problem. For such purposes, multi-criteria decision-making methods are resorted to, which, despite continuous improvement, still have some weaknesses, often due to their characteristics. One such method that has seen many modifications in the literature is the Characteristic Objects METhod (COMET). It allows a simplified comparison of alternatives considered in a given problem, compared to the Analytic Hierarchy Process (AHP), but has a relatively high computational complexity. In this study, we present a modification of the COMET method that simplifies the procedure for assigning preference values to characteristic objects. Furthermore, a comparison with the classical approach is given regarding the obtained preference values, rankings, and criteria weights, which occur implicitly in this method. The study showed that the proposed modification has grounds for broader use and offers similar results to classical COMET while reducing the number of required operations to obtain preference values.

关键词： COMET analysis MCDA MCDM weights decision making

Drug repositioning for Alzheimer's disease with transfer learning

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arXiv 2022年

作者： Wu, Yetao Liu, Han Yan, Jie Hu, Xiaolin Department of Computer Science and Technology Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems BNRist THBI Tsinghua University Beijing China

Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have attracted attention in drug discovery. However, the public DRUG-seq dataset is too small to be used for directly training a deep learning neural network from scratch. Inspired by the transfer learning technique, we pretrain a drug efficacy prediction neural network model with the Library of Integrated Network-based Cell-Signature (LINCS) L1000 data and then use human neural cell DRUG-seq data to fine-tune it. After training, the model is used for virtual screening to find potential drugs for Alzheimer’s disease (AD) treatment. Finally, we find 27 potential drugs for AD treatment including Irsogladine (PDE4 inhibitor), Tasquinimod (HDAC4 selective inhibitor), Suprofen (dual COX-1/COX-2 inhibitor) et al. Copyright © 2022, The Authors. All rights reserved.

关键词： Diagnosis

Create Your World: Lifelong Text-to-Image Diffusion

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arXiv 2023年

作者： Sun, Gan Liang, Wenqi Dong, Jiahua Li, Jun Ding, Zhengming Cong, Yang State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu210094 China The Department of Computer Science Tulane University New OrleansLA70118 United States The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China University of Chinese Academy of Sciences Beijing100049 China

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a user's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic forgetting" for the past encountered concepts, and semantic "catastrophic neglecting" for one or more concepts in the text prompt. In respect of knowledge "catastrophic forgetting", our L2DM framework devises a task-aware memory enhancement module and a elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic "catastrophic neglecting" is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models. The code will be released at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Comparative sensitivity analysis of single and multiple modifications in multi-criteria decision analysis

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Procedia computer science 2024年 246卷 91-102页

作者： Jakub Więckowski Paweł Białoń Wojciech Sałabun Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland

In the area of decision-making, where complexities often exceed experts’ analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Criteria Decision Analysis (MCDA) problems. Current analytical frameworks struggle to comprehensively consider potential modifications to values within decision matrices, making it challenging to understand their impact in detail. To address this challenge, decision-makers need enhanced tools, and MCDA methods combined with systematic changes in selected elements of the decision matrix stand out as a promising approach. However, existing studies primarily focus on different criteria-weight scenarios, leaving an unexplored gap in the simultaneous modification of multiple values within the decision matrix. This paper integrates sensitivity analysis within the MCDA methods, extending its role in the comprehensive assessment of decision problems. Sensitivity analysis becomes crucial in offering decision-makers a broader perspective, aiding them in navigating the complexities of decision-making in dynamic environments. Recognizing the unexplored potential in sensitivity analysis, the study proposed a novel approach of simultaneous modification of multiple values in a decision matrix, offering an extension of conventional one-at-a-time modifications. As a Proof of Concept (PoC) research work, the study investigated whether the determined approach provides divergent preference scores compared to the traditional single-modification method across ten different MCDA techniques. The results showed that modifying multiple values simultaneously produced different preference scores of alternatives than in the case of a conventional single change, showing that additional insight knowledge could be extracted from this type of sensitivity analysis.

关键词： Sensitivity analysis Multiple changes Decision making Results robustness MCDA