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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Enhancing image Classification with Bayesian Neural Networks: Improved Training Techniques and Decision Strategies for Reliable AI Support in Uncertainty-Sensitive Tasks

Enhancing Image Classification with Bayesian Neural Networks...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Yan.Cheng. Liu Hua.Jun Huang Yi.Wen Wang School of Information Technology and Management Hunan University of Finance and Economics Changsha China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Bayesian theory is a probabilistic statistical framework for dealing with uncertainty and incomplete information, while neural networks are widely applied in deep learning fields such as image processing. Bayesian Neural Networks (BNNs) combine Bayesian theory and neural networks to provide more comprehensive predictive results. Although BNNs have significant advantages in expressing model uncertainty, their training methods and network structure design are still to be explored. Effectively training BNNs and exploring their network structure design to enhance predictive performance and computational efficiency are the core of this research. The study aims to improve the performance of BNNs in image classification tasks, specifically including the quantification and utilization of uncertainty to enhance model reliability and decision support capabilities. We use efficient training methods such as variational inference to redesign Bayesian neural networks and conduct deep image recognition training on the CIFAR-10 dataset. The improved training method allows for the rapid and accurate estimation of model weight uncertainty, while the proposed decision strategy utilizes uncertainty information to make reliable decisions. This study demonstrates that BNNs have tremendous potential in addressing real-world uncertainty issues, with the results expected to provide safer AI decision support for risk-sensitive tasks and promote the reliability and practicality of deep learning technology in various critical applications.

关键词： Training Deep learning Uncertainty computer network reliability Neural networks Bayes methods Reliability

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Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

Watch the Speakers: A Hybrid Continuous Attribution Network ...

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Shanglin Lei Xiaoping Wang Guanting Dong Jiang Li Yingjian Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.

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CAMPEOD: A Cross Attention-Based Multi-Scale Patch Embedding Organoid Detection Model

CAMPEOD: A Cross Attention-Based Multi-Scale Patch Embedding...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Xun Deng Lun Hu Zhu-Hong You Peng-Wei Hu The Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China University of Chinese Academy of Sciences Beijing China Xinjiang Laboratory of Minority Speech and Language Information Processing Urumqi China School of Computer Science Northwestern Polytechnical University Xi’an China

In medical research, organoids, which exhibit structural and functional resemblances to authentic organs, offer a substantial avenue for delving into aspects encompassing physiology, pathophysiology, diseases, and pharmaceutical screening. Critical insights into drug responsiveness are often gleaned from these organoids' dimensional and configurational disparities. However, conventional detection methodologies reliant upon fluorescent labeling engender potential hazards to organoid integrity, thereby impinging upon their intrinsic dynamic attributes. Traditional bounding-box detection methodologies fall short in encapsulating intricate morphological particulars, and certain deep-learning approaches grapple with the intricate task of capturing multi-scale data, particularly when tasked with discerning organoid structures characterized by marked shape and size heterogeneities. In a bid to surmount these constraints, our study introduces CAMPEOD, an innovative framework that synergistically amalgamates multi-scale attributes derived from organoid specimens, employing cross-attention mechanisms. This novel approach effectively obviates superfluous background interference and image noise, thereby endowing an automated, finely-tuned dissection of organoid samples. Significantly, this segmentation process ensures congruence with authentic organoid quantities and morphological characteristics. By facilitating comprehensive scrutiny of microscopy images of organoid samples on a large scale, CAMPEOD assumes considerable implications for the realm of pharmaceutical screening and ailment emulation.

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Advancements in Video-Based Insect Tracking: A Bibliometric Analysis to A Short Survey 23

Advancements in Video-Based Insect Tracking: A Bibliometric ...

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Proceedings of the 2023 7th international conference on Advances in image processing

作者： Erick A. Adje Arnaud S. R. M. Ahouandjinou Gilles Delmaire Gilles Roussel Ratheil V. Houndji Unité de Recherche en Sciences Informatiques et Applications Université d'Abomey-Calavi Benin and Laboratoire d'Informatique Signal et Image de la Côte d'Opale Université du Littoral Cote d'Opale France Unité de Recherche en Sciences Informatiques et Applications Université d'Abomey-Calavi Benin Laboratoire d'Informatique Signal et Image de la Côte d'Opale Université du Littoral Cote d'Opale France

ISBN: (纸本)9798400708275

Video-based insect tracking provides vital insights into insect behavior and ecology, enhancing our understanding of their movements and interactions. Therefore, examining trends in this field over the last few years is essential. This study aims to conduct a bibliometric analysis to unveil the growing interest in video-based insect tracking with a short review based on documents used for bibliometric analysis. To achieve this, 453 documents were extracted from Scopus on 12 June 2023. Only documents in English published between 2010 and 2023, resulting in a dataset of 318 documents, were analyzed. The findings illustrate a consistent growth in video-based insect research over the last years, with a significant peak in 2021, comprising 32 documents. The journal PLOS ONE stands out as the most productive source. The USA exhibited the most significant interest in video-based insect tracking over the last years. Keyword analysis reflects the multidisciplinary nature of insect tracking research. The review demonstrated that video-based insect tracking serves two primary objectives: pose estimation and trajectory information. However, the main challenge in video-based insect tracking is to preserve the identity of multiple individuals in situations involving occlusions or complex interactions.

关键词： bibliometric analysis computer vision insect movement behavior video tracking

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Quantum Computational Intelligence with Generative AI image for Human-Machine Interaction

Quantum Computational Intelligence with Generative AI Image ...

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ieee international conference on Fuzzy Systems (FUZZ-ieee)

作者： Chang-Shing Lee Mei-Hui Wang Jun-Kui Chiang Naoyuki Kubota Eri Sato-Shimokawara Yusuke Nojima Giovanni Acampora Pei-Yu Wu Szu-Chi Chiu Sheng-Chi Yang Chyan-Zheng Siow National University of Tainan Tainan Taiwan Tokyo Metropolitan University Tokyo Japan Osaka Metropolitan University Japan University of Naples Federico II Italy

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

This paper introduces a Quantum Computational Intelligence (QCI) agent equipped with a content attention ontology model, specifically designed to enhance human-machine interaction based on a Generative Artificial Intelligence (GAI) image generation agent for Taiwanese/English learning and experience. Its diverse primary applications include social media analysis on Facebook groups and YouTube learning videos related to the 2023 ieee CIS Education Portal (EP) Subcommittee, as well as in the areas of Taiwanese/English language learning and dialogue experience with GAI image generation. To establish the knowledge and inference models for the QCI agent, we initially developed a Taiwanese/English learning and experience ontology, including a content attention ontology, and an image attention ontology. The QCI agent utilizes metrics such as the number of views, posts, and comments to predict the fuzzy number of reactions. In addition, the GAI image agent generates Taiwanese speech-based/English text-based images and evaluates the fuzzy similarity score between Taiwanese/English and the attention ontology together with the Sentence BERT (SBERT) agent. This Taiwanese/English fuzzy similarity score is further validated through human assessments, with these evaluations subsequently serving as an additional metric for comparative analysis of Human-Machine Interaction (HMI). Furthermore, the GAI image agent is designed to create images and Chinese/English texts from text/speech translated by the Meta AI Universal Speech Translator (UST) Taiwanese/English agent. A Particle Swarm Optimization (PSO)-based machine learning mechanism is employed to train the QCI model for assessing learners' performance and predicting the performance of others. The National University of Tainan (NUTN) Taiwan-Large Language Model (***-LLM) agent has been further enhanced to support interactive learning experiences for HMI. An SBERT-based assessment agent is used to calculate fuzzy similarities be

关键词： Human computer interaction Measurement Quantum computing Social networking (online) image synthesis Generative AI Computational modeling

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A Multi-Hop-Aware User To Edge-Server Association Game

A Multi-Hop-Aware User To Edge-Server Association Game

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ieee conference on Network Softwarization (NetSoft)

作者： Youcef Kardjadja Alan Tsang Mohamed Ibnkahla Yacine Ghamri-Doudane Systems and Computer Enginering Department Carleton University Ottawa ON Canada Laboratory of Informatics Image and Interaction (L3I) Univ. La Rochelle France School of Computer Science Department Carleton University Ottawa ON Canada

Nowadays, services and applications are becoming more latency-sensitive and resource-hungry. Due to their high computational complexity, they can not always be processed locally in user equipment, and have to be offloaded to a distant powerful server. Instead of resorting to remote Cloud servers with high latency and traffic bottlenecks, service providers could map their users to Multi-Access Edge Computing (MEC) servers that can run computation-intensive tasks nearby. This mapping of users to MEC distributed servers is known as the Edge User Allocation (EUA) problem, and has been widely studied in the literature from the perspective of service providers. However, users in previous works can only be allocated to a server if they are in its coverage. In reality, it may be optimal to allocate a user to a distant server (e.g., two hops away from the user) if the latency threshold and system cost are both respected. This work presents the first attempt to tackle the multi-hop aware EUA problem. We consider the static EUA problem where users have a simultaneous-batch arrival pattern, and detail the added complexity compared to the original EUA setting. Afterwards, we propose a game theory-based distributed approach for allocating users to edge servers. We finally conduct a series of experiments to evaluate the performance of our approach against other baseline approaches. The results illustrate the potential benefits of allowing multi-hop allocations in providing better overall system cost to service providers.

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Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrared and Visible images

Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrare...

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Machine Vision, image processing and Imaging Technology (MVIPIT), international conference on

作者： Xu Song Yongbiao Xiao Hui Li Xiao-Jun Wu Jun Sun Vasile Palade School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Faculty of Engineering Environment and Computing Coventry University Conventry U.K.

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

The fusion of visible light and infrared images has garnered significant attention in the field of imaging due to its pivotal role in various applications, including surveillance, remote sensing, and medical imaging. Therefore, this paper introduces a novel fusion framework using Res2Net architecture, capturing features across diverse receptive fields and scales for effective extraction of global and local features. Our methodology is structured into three fundamental components: the first part involves the Res2Net-based encoder, followed by the second part, which encompasses the fusion layer, and finally, the third part, which comprises the decoder. The encoder based on Res2Net is utilized for extracting multi-scale features from the input image. Simultaneously, with a single image as input, we introduce a pioneering training strategy tailored for a Res2Net-based encoder. We further enhance the fusion process with a novel strategy based on the attention model, ensuring precise reconstruction by the decoder for the fused image. Experimental results unequivocally showcase our method’s unparalleled fusion performance, surpassing existing techniques, as evidenced by rigorous subjective and objective evaluations.

关键词： Training Surveillance Machine vision Feature extraction Decoding image fusion Remote sensing

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Gender Recognition Based on Face image Using Deep Learning Method

Gender Recognition Based on Face Image Using Deep Learning M...

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international conference on Cyber and IT Service Management (CITSM)

作者： Mochamad Wahyudi Waeisul Bismi Firmansyah Mugi Raharjo Untung Rahardja Lise Pujiastuti Departement of Computer Science Universitas Bina Sarana Informatika Jakarta Indonesia Departement of Informatics University Nusa Mandiri Jakarta Indonesia Departement of Digital Business University Raharja Tangerang Indonesia Departement of Information System STMIK Antar Bangsa Tangerang Indonesia

Gender recognition based on facial images is one of the interesting applications in the field of image processing and artificial intelligence. Deep Learning methods, particularly artificial neural networks, have emerged as an effective tool for extracting complex facial features and classifying gender with high accuracy. This research outlines a study that aims to develop an employee gender recognition system based on facial images using Deep Learning methods. The method used in this study proposes the inceptionV3 model as well as the OpenCV and matplotlib libraries in Python in involving the collection of facial image datasets covering different genders with a total of 1024 facial images, each with 40 attributes. This dataset was used to train a deep artificial neural network to recognize patterns and features that are unique to male and female genders and validated. Furthermore, the artificial neural network was tested with a dataset of never-before-seen facial images to evaluate the performance of the system. The results show that the InceptionV3 Deep Learning model has great potential in gender recognition based on facial images. The developed system was able to achieve a high level of accuracy in classifying the gender of individuals from facial images with a total accuracy of 94.8% against the test data, even in complex situations such as variations in facial expressions and lighting.

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MedPipe: End-to-End Joint Search of Data Augmentation and Neural Architecture for 3D Medical image Classification

MedPipe: End-to-End Joint Search of Data Augmentation and Ne...

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Medical Artificial Intelligence (MedAI), ieee international conference on

作者： Xin He Xiaowen Chu Dept. Computer Science Hong Kong Baptist University Hong Kong China Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

Data augmentation plays a crucial role in deep learning-based medical imaging analysis, but manually designing tailored data augmentation strategies for each dataset is impractical. Although automatic data augmentation (ADA) techniques have been explored, they often focus solely on data augmentation without considering the importance of neural architecture. Similarly, neural architecture search (NAS) methods mainly concentrate on optimizing the neural architecture while overlooking the impact of data augmentation. However, both data augmentation and neural architecture are interrelated and should be considered together. The joint optimization of data augmentation and neural architecture can lead to improved model performance by harnessing the complementary effects of customized data augmentation strategies and compatible neural architectures. Despite this, the seamless integration of data augmentation and neural architecture search remains under-explored. To address this research gap, we propose MedPipe, an approach that enables end-to-end joint search of data augmentation and neural architecture. We introduce a compact data augmentation search space and unify data augmentation and neural architecture into a cohesive network. This allows simultaneous exploration, optimizing their synergy for enhanced performance. Experimental evaluation on nine medical datasets highlights the necessity of the joint search for data augmentation and neural architecture, demonstrating the superior performance of our approach. Our work opens up possibilities for future applications in diverse medical domains.

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Searching Optimizers for Deep Learning Based Hyperspectral image Classification 1st

Searching Optimizers for Deep Learning Based Hyperspectral ...

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1st international conference on Computational Technologies and Electronics, ICCTE 2023

作者： Sarkar, Anish Nandi, Utpal Paul, Bachchu Ghosal, Sudipta Kr. Singh, Moirangthem Marjit Mandal, Jyotsna Kumar Faruqui, Nuruzzaman Department of Computer Science Vidyasagar University West Bengal Midnapore India Department of Computer Science and Technology Behala Government Polytechnic West Bengal Kolkata India Department of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Arunachal Pradesh Nirjuli India Department of Computer Science and Engineering University of Kalyani West Bengal Nadia India Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031819346

In spite of its capacity to capture fine-grained features, hyperspectral images (HSIs), which provide extensive spectral and spatial information, have become more important in a variety of applications. Unlike traditional images, they give a multidimensional representation that allows for improved discrimination and analysis. This work analyses the effect of optimizers on the performance of the deep learning (DL) based models for hyperspectral image classification (HSIC) in terms of overall accuracy (OA), actual accuracy (AA), and Kappa. The research emphasizes the critical function of optimizers in deep learning model training for hyperspectral image processing, impacting convergence dynamics and generalization proficiency. The chosen testbed for testing the influence of optimizers is HybridSN, which is well-known for its ability to combine 2D and 3D convolutions to successfully extract both spectral and spatial data. Additionally, HybridSN is the ideal choice for investigating optimizer impacts on hyperspectral image classification outcomes because to its straightforward architecture and constant performance across varied datasets such as Indian Pines (IP), Pavia University (PU), and Salinas (SA). This study juxtaposes numerous optimizers for classification tasks on different datasets, emphasizing DiffMoment as the best performer for the IP dataset, while AdamP, RAdam, and DiffMoment shine on the PU dataset, and all optimizers do well except SGD on the SA dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ability testing

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