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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

共 14697 条记录，以下是311-320 订阅

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Why are Visually-Grounded Language Models Bad at image Classification? 38

Why are Visually-Grounded Language Models Bad at Image Class...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhang, Yuhui Unell, Alyssa Wang, Xiaohan Ghosh, Dhruba Su, Yuchang Schmidt, Ludwig Yeung-Levy, Serena Stanford University United States University of Washington United States Tsinghua University China

image classification is one of the most fundamental capabilities of machine vision intelligence. In this work, we revisit the image classification task using visually-grounded language models (VLMs) such as GPT-4V and LLaVA. We find that existing proprietary and public VLMs, despite often using CLIP as a vision encoder and having many more parameters, significantly underperform CLIP on standard image classification benchmarks like imageNet. To understand the reason, we explore several hypotheses concerning the inference algorithms, training objectives, and data processing in VLMs. Our analysis reveals that the primary cause is data-related: critical information for image classification is encoded in the VLM's latent space but can only be effectively decoded with enough training data. Specifically, there is a strong correlation between the frequency of class exposure during VLM training and instruction-tuning and the VLM's performance in those classes;when trained with sufficient data, VLMs can match the accuracy of state-of-the-art classification models. Based on these findings, we enhance a VLM by integrating classification-focused datasets into its training, and demonstrate that the enhanced classification performance of the VLM transfers to its general capabilities, resulting in an improvement of 11.8% on the newly collected imageWikiQA dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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FedDHr: Improved Adaptive Learning Strategy Using Federated Learning for image processing

FedDHr: Improved Adaptive Learning Strategy Using Federated ...

引用

2023 International conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2023

作者： Velagapudi, Anuhya Jhansi, B. Hemalikha, M. Vijayalakshmi, P. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350300857

FL (Federated learning) has grown in popularity as a field of research that allows for the training of an algorithm over many decentralised servers that are having local data samples without requiring data exchange. Numerous application domains (like medical image analysis diagnosis made by doctors) lack a significant amount of properly labeled and full data that can be found in one place. Due to the prevalence of artificial intelligence in new application sectors, there are few rising issues on privacy of data and user. New algorithms of various research works are analysed in this study and it introduces a new technique that address the data heterogeneity and adaptive learning rate of the model to perform image processing, Deep Learning. © 2023 IEEE.

关键词： Electronic data interchange

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Clinical Breast Imaging and Deep Learning Model For image Malignancy Prediction Using Resource-Constrained Embedded Devices 4

Clinical Breast Imaging and Deep Learning Model For Image Ma...

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4th International conference on Artificial Intelligence and Signal processing

作者： Chokhandre, Sumedha R. Jadhav, Ms Pranjali Gole, Nilesh Thakare, Amit N. Bondade, Akansha R. Singh, Priyanka YCCE Dept Informat Technol Nagpur Maharashtra India Vishwakarma Inst Informat Technol AI & DS Dept Pune Maharashtra India Vidharbha Inst Technol Dept CSE Nagpur Maharashtra India Cummins Coll Engn Women Nagpur Maharashtra India G H Raisoni Univ Dept CSE Saikheda India VIT AP Univ Sch Comp Sci Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Computer assisted diagnosis(CAD) of diseases provides more accurate and precise diagnostic reports towards better information regarding the medical condition of patients. A clinician can minimize the error by applying his experience acquired by practice, cognitive intuition or scientific research backed by laboratory reports and computer assisted medical image analysis. The findings by the experts based on the analysis of such data are crucial as the suggested treatment is dependent on evaluation at this stage. Machine learning techniques while applied in the medical field performs decision making by mimicking the steps performed by a medical expert in diagnosing the disease, but using algorithms rather intuitive. It brings out accurate medical data through analysis of images performed by computing devices that can reveal valuable information regarding the disease prognosis. Computer aided disease diagnosis with state-of-the-art machine learning and deep learning offers seamless assistance in medical care with near human accuracy. Technology integrated medical support systems combined with sophisticated algorithms can reduce the number of false positive incidents as well as false negative cases. Convolutional neural network (CNN) models can be trained using handcrafted features to derive conclusive inferences for binary class as well as multi-class classification. Artificial Intelligence (AI) supported techniques in disease diagnosis provide assistance to medical experts in decision making by virtue of cloud based data analytics tools for storage and computing.

关键词： Computer assisted diagnosis(CAD) Convolutional neural network (CNN) Artificial Intelligence (AI) cloud based data analytics

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A Comparative Analysis of image Partitioning and Compression Mechanisms 15

A Comparative Analysis of Image Partitioning and Compression...

引用

15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaudhari, Sampada P. Thakur, Nileshsingh V. Dept. of Computer Science and Engineering Prof Ram Meghe College of Engineering & Management Badnera India Dept. of Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331300579

This paper presents a comprehensive comparative analysis of image partitioning and compression mechanisms, two fundamental techniques in image processing and data compression. image partitioning involves dividing an image into smaller components, while compression aims to reduce the size of digital images while preserving visual quality. The analysis encompasses various aspects of both techniques, including methodologies, algorithms, and performance metrics. For image partitioning, segmentation methods such as block-based, region-based, and hierarchical partitioning are examined, along with their respective advantages and limitations. Additionally, the paper explores compression mechanisms such as transform coding, quantization, entropy coding, and predictive coding, highlighting their roles in achieving efficient compression. Furthermore, the comparative analysis evaluates the tradeoffs between different partitioning and compression techniques in terms of compression ratio, image quality, computational complexity, and applicability across different types of images and applications. Various compression standards such as JPEG and PNG are also reviewed to provide insights into real-world implementations. Finally, the paper discusses future research directions and emerging trends in image partitioning and compression, including the integration of machine learning techniques for improved segmentation and compression performance. The findings presented in this comparative analysis can serve as a valuable resource for researchers, practitioners, and developers working in the fields of image processing, data compression, and multimedia systems. © Grenze Scientific Society, 2024.

关键词： image compression

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How to combine issues related to autonomous vehicles - a proposal with a literature review 26

How to combine issues related to autonomous vehicles - a pro...

引用

26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Balcerek, Julian Pawlowski, Pawel Poznan University of Technology Division of Signal Processing and Electronic Systems Institute of Automatic Control and Robotics Poznan Poland

ISBN: (纸本)9798350304985

This article proposes a model that combines the issues related to autonomous vehicles into seven groups. The groups are included in mutual iterations between the user, the autonomous vehicle and the environment. They are: vehicle automation, environmental sensors, automatic perception of the environment based on the processing of vision signals, communication with external devices, interaction with the user, direct transmission of signals to the environment, and future development plans. Bearing in mind the proposed model, various latest solutions presented in the state-of-the-art literature or already offered in vehicles are collected and described. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： image processing

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Application of image Security Transmission Encryption Algorithm Based on Chaos Algorithm in Networking systems of Artificial Intelligence 5

Application of Image Security Transmission Encryption Algori...

引用

5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

作者： Tian, Hongmei Yuan, Zhijie Zhou, Jiao He, Rui Xi’an University of Technology Xi'an710054 China China Coal Technology & Engineering Group Xian Research Institute Xi'an710076 China

ISBN: (纸本)9781643685243

This study focuses on enhancing the security of image transmission in Networking systems of Artificial Intelligence (NSAI) by implementing an advanced encryption algorithm (AEA) based on chaotic algorithms. The research begins by exploring the fundamental concepts, structures, and practical applications of NSAI. It then introduces the development of the AEA, specifically designed to safeguard image data during transmission. The methodology involves integrating a chaotic algorithm into the encryption and decryption processes, thereby improving the confidentiality and integrity of the images. Additionally, an encryption protocol consistent with NSAI specifications is developed to further enhance the security of transmitted images. Through simulations, the AEA demonstrates exceptional performance, with minimal errors, high accuracy, and outstanding efficiency in encryption tasks. Overall, this study concludes that the AEA represents a significant advancement in securing image transmission within NSAI, providing robust protection for future applications. © 2024 The Authors.

关键词： Artificial intelligence

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Innovative Quality Metrics for Enhanced Interpretation of Instance Segmentation in Complex image Scenarios 21st

Innovative Quality Metrics for Enhanced Interpretation of In...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Pedro Llerena, Juan Patricio, Miguel A. Molina, Jose Manuel Mora-Sanchez, Angel Rodriguez-Jimenez, Silvia Univ Alcala Dept Comp Sci Alcala De Henares Spain Univ Carlos III Madrid Appl Artificial Intelligent Grp Colmenarejo Spain Sigma Cognit Ave Manoteras 6 Madrid 28050 Spain

ISBN: (纸本)9783031809453;9783031809460

Manual data annotation involves human annotators labeling and reviewing data according to predefined criteria. It can be time-consuming and expensive, so automated data annotation based on artificial intelligence algorithms has gained popularity. However, the classical integrated artificial intelligence models have some limitations, especially in complex scenarios characterized by occlusions, low resolution, and illumination variability. This paper introduces new evaluation methods for image instance segmentation, focusing on the need for easy-to-understand quality metrics in the face of the complexity of image annotation. Considering the difficulties of manual annotation processes and the diversity of computer vision models focused on instance segmentation, four quality metrics are proposed: Number of Detections, Normalized Bounding Box Area, Normalized Mask Area, and Bounding Box Occlusion Rate. These metrics attempt to overcome the difficulties of select the best model for automated labeling based on conventional metrics such as Intersection Over Union and Mean Average Precision, especially in scenarios where classical models have limitations. The application and analysis of these metrics in the OVIS database demonstrates their potential to improve the interpretation of instance segmentation models, thus facilitating a more accurate and accessible automated annotation process.

关键词： image Annotation Tools image Quality Metrics image Instance Segmentation and image Detection

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Intelligent algorithms of digital processing of biomedical images in wavelet methods

Intelligent algorithms of digital processing of biomedical i...

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International conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Zaynidinov, H. Xuramov, L. Khodjaeva, D. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Uzbekistan Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9781032669663

To date, the use of computer vision in biomedicine with the use of artificial intelligent systems, which in turn receive information from images, and then give out new knowledge and final conclusions about the disease is the most relevant. images are obtained from different video sensors using them in different frequency ranges, which can be represented using two-dimensional brightness matrices and two-dimensional sensors. You can improve the image quality in several steps: preprocessing, highlighting the characteristic properties of the image (filtering, brightness equalization) classification and conclusion. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Luminance

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Garbage image classification robotic arm based on deep learning

Garbage image classification robotic arm based on deep learn...

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2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Lyu, Shaofan School of Electronic Engineering Xidian University Shaanxi Xi'an China

ISBN: (纸本)9781510677685

This study aims to compare and evaluate the performance of four popular deep learning models (CNN, ResNet50, VGG16, and InceptionResNetV2) in the garbage classification task. Waste classification is essential for sustainable development, and automated waste classification systems have become the key to solving the increasing global waste problem. To achieve accurate waste classification, these systems utilize various machine learning algorithms. In recent years, deep learning models have achieved great success in image classification tasks, so they have attracted much attention in garbage classification. In this study, we comprehensively evaluate the performance of CNN, ResNet50, VGG16, and InceptionResNetV2 models in classifying garbage into 12 different categories by comparing metrics such as accuracy, precision, and confusion matrix. The experimental results show that the InceptionResNetV2 model outperforms other models in the garbage classification task, with high precision and recall. This means that using the InceptionResNetV2 model for garbage classification can identify and separate different types of garbage more accurately. Finally, the deep learning model is connected to the CoppeliaSim platform to control the movement of the robotic arm based on the predictive results of the deep learning model. © 2024 SPIE.

关键词： image classification

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STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics 38

STimage-1K4M: A histopathology image-gene expression dataset...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Chen, Jiawen Zhou, Muqing Wu, Wenrong Zhang, Jinwei Li, Yun Li, Didong University of North Carolina Chapel Hill United States

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15, 000-30, 000 dimensional gene expressions. With 4, 293, 195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond. © 2024 Neural information processing systems foundation. All rights reserved.

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