[ad_1] Introduction Scene understanding refers to the ability of a computer system to interpret and make sense of visual data in a given environment. This process involves recognizing objects, inferring relationships between them, and understanding…
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[ad_1] Introduction Biometric identification has become increasingly important in various fields such as security, banking, and healthcare. Among the various biometric modalities, face recognition has gained widespread attention due to its non-intrusive nature and ease…
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[ad_1] Introduction: Pose estimation for human body analysis is a rapidly growing field in computer vision and artificial intelligence. It involves the process of detecting and tracking the body joints and limbs of a human…
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[ad_1] Introduction In recent years, the explosion of online video content has created a need for automated methods of video summarization for highlight extraction. Video summarization involves condensing the content of a video into a…
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[ad_1] Introduction: Visual question answering (VQA) is an emerging research area that aims to enable machines to understand and answer questions about visual content. With the increasing availability of multimedia data, including images and videos,…
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[ad_1] Introduction Over the past few years, Generative Adversarial Networks (GANs) have gained significant attention in the field of artificial intelligence and machine learning. GANs are a type of deep neural network architecture that consists…
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[ad_1] Introduction Variational autoencoders (VAEs) have gained significant attention in the field of generative modeling due to their ability to learn complex distributions and generate realistic samples. This thesis aims to explore the use of…
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[ad_1] Introduction Autoencoders have gained significant attention in recent years as powerful tools for unsupervised representation learning. These neural networks are capable of learning compact and meaningful representations of data without the need for labeled…
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[ad_1] Introduction Long short-term memory (LSTM) networks are a type of recurrent neural network (RNN) that have been specifically designed to address the issue of capturing long-term dependencies in sequential data. Traditional RNNs suffer from…
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[ad_1] Introduction Convolutional neural networks (CNNs) have gained significant attention in recent years due to their exceptional performance in various domains, including computer vision, natural language processing, and speech recognition. CNNs are particularly well-suited for…
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