[ad_1] Introduction Object detection for localization and classification is a crucial task in the field of computer vision and machine learning. It involves the identification and precise location of objects within an image or video,…
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[ad_1] Introduction Action recognition is a crucial task in video understanding and has gained significant attention in recent years due to its wide range of applications in various fields such as surveillance, human-computer interaction, sports…
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[ad_1] Introduction: Gaze estimation for attention tracking has become a prominent research area in recent years, with applications in various fields such as human-computer interaction, psychology, and medical diagnosis. The ability to accurately estimate where…
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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 In recent years, there has been a growing interest in the development and application of deep learning techniques for the analysis of spatial data. Convolutional Deep Belief Networks (CDBNs) have emerged as a…
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[ad_1] Introduction In recent years, deep belief networks (DBNs) have gained significant attention in the field of artificial intelligence and machine learning due to their ability to learn hierarchical representations of data. These networks are…
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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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