This review article about Few-Shot Learning techniques is focused on Computer visionapplications based on Deep Convolutional Neural Networks. A general discussion about Few-Shot Learning is given, featuring a context...
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ISBN:
(纸本)9783031133244;9783031133237
This review article about Few-Shot Learning techniques is focused on Computer visionapplications based on Deep Convolutional Neural Networks. A general discussion about Few-Shot Learning is given, featuring a context-constrained description, a short list of applications, a description of a couple of commonly used techniques and a discussion of the most used benchmarks for FSL computer visionapplications. In addition, the paper features a few examples of recent publications in which FSL techniques are used for training models in the context of Human Behaviour Analysis and Smart City Environment Safety. These examples give some insight about the performance of state-of-the-art FSL algorithms, what metrics do they achieve, and how many samples are needed for accomplishing that.
As everyone knows that in today's time Artificial Intelligence, machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we a...
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As everyone knows that in today's time Artificial Intelligence, machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of imageprocessing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SvM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.
visual Question Answering (vQA) lies at the crossroads of computer vision, natural language processing, and deep learning, captivating researchers across various AI domains. This dynamic field involves processing an i...
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Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain. While these models exhibit promise in generating abstract image captions ...
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ISBN:
(纸本)9798891760608
Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain. While these models exhibit promise in generating abstract image captions and facilitating natural conversations, their performance on text-rich images still requires improvement. In this paper, we introduce Contrastive Reading Model (Cream), a novel neural architecture designed to enhance the language-image understanding capability of LLMs by capturing intricate details that are often overlooked in existing methods. Cream combines vision and auxiliary encoders, fortified by a contrastive feature alignment technique, to achieve a more effective comprehension of language information in visually situated contexts within the images. Our approach bridges the gap between vision and language understanding, paving the way for the development of more sophisticated Document Intelligence Assistants. Through rigorous evaluations across diverse visually-situated language understanding tasks that demand reasoning capabilities, we demonstrate the compelling performance of Cream, positioning it as a prominent model in the field of visual document understanding. We provide our codebase and newly-generated datasets at https://***/naver-ai/cream.
Modern day computer visionapplications are frequently implemented using machine learning approaches. While these implementations can perform very well, the performance is heavily dependent on sufficient and accurate ...
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Smart measurements are widely deployed in many applications due to the technology advancement. For various industrial applications, automated inspection and analysis based on the image is provided by machinevision. F...
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Smart measurements are widely deployed in many applications due to the technology advancement. For various industrial applications, automated inspection and analysis based on the image is provided by machinevision. For the measurements in these applications, sensors must be connected. machinevision tries to creatively combine already existing technology and use them to address current issues. The term "measurement" is frequently used to refer to many tasks and is the cornerstone of industrial automation and security deployment. This Special Issue of Instrumentation & Measurement Magazine addresses some novel achievements in the measurement and instrumentation science and technology fields. It advances machinevision concerning production, application of smart materials, measurement and estimation techniques, etc. The variety of selected papers reflects the efforts made by the authors to focus either on methodological aspects or technical issues. In particular, three papers have been accepted for publication, reflecting several aspects of the abovementioned fields by covering machinevision and imageprocessing technology.
In view of the demand for cigarette case appearance quality detection in the production process of cigarette enterprises, a machinevision-based method for detecting cigarette case appearance defects is proposed, and ...
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One of the most important occupations in India is agriculture. Out of all the crops, cotton is the best and is crucial to the agricultural economy of the country. In India, 40-50 million people work in the cotton trad...
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Agriculture has been the most primary source of the livelihood of man for thousands of years. Even today, it provides subsistence to about 50% of the world population. Plant diseases are the serious cause of big losse...
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Agriculture has been the most primary source of the livelihood of man for thousands of years. Even today, it provides subsistence to about 50% of the world population. Plant diseases are the serious cause of big losses to crop production every year worldwide. It is necessary to keep the plants healthy at various stages of their growth/development to deal with the financial losses from plant diseases. Symptoms of infections are visible mainly at plant leaves;thus leaves are commonly used to detect and identify the diseases. Detecting the disease through visual observation is itself a challenging task and requires a lot of human expertise. imageprocessing techniques along with computational intelligence or soft computing techniques can be used to provide a better assistance for disease detection to the farmers. A disease in plants can be detected based on its symptoms extracted in the form of features. Feature extraction techniques thus play a vital role in such systems. The paper emphasizes on the review of hand-crafted and deep learning based feature extraction with their merits and demerits. It provides a comprehensive discussion on a variety of image features such as color, texture, and shape for various disorders in different cultures.
Automated character recognition is currently highly popular due to its wide range of applications. Bengali handwritten character recognition (BHCR) is an extremely difficult issue because of the nature of the script. ...
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Automated character recognition is currently highly popular due to its wide range of applications. Bengali handwritten character recognition (BHCR) is an extremely difficult issue because of the nature of the script. very few handwritten character recognition (HCR) models are capable of accurately classifying all different sorts of Bangla characters. Recently, image recognition, video analytics, and natural language processing have all found great success using convolutional neural network (CNN) due to its ability to extract and classify features in novel ways. In this paper, we introduce a vashaNet model for recognizing Bangla handwritten basic characters. The suggested vashaNet model employs a 26 -layer deep convolutional neural network (DCNN) architecture consisting of nine convolutional layers, six max pooling layers, two dropout layers, five batch normalization layers, one flattening layer, two dense layers, and one output layer. The experiment was performed over 2 datasets consisting of a primary dataset of 5750 images, CMATERdb 3.1.2 for the purpose of training and evaluating the model. The suggested character recognition model worked very well, with test accuracy rates of 94.60% for the primary dataset, 94.43% for CMATERdb 3.1.2 dataset. These remarkable outcomes demonstrate that the proposed vashaNet outperforms other existing methods and offers improved suitability in different character recognition tasks. The proposed approach is a viable candidate for the high efficient practical automatic BHCR system. The proposed approach is a more powerful candidate for the development of an automatic BHCR system for use in practical settings.
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