Deep Learning Architectures for Advanced Video Understanding
Ergashev NuriddinDepartment of Information Systems and Technologies, Karshi State Technical University, UzbekistanXurramov RuslanDepartment of Information Technology and Exact Sciences, Termez University of Economics and Service, Termez, UzbekistanAnorgul AshirovaDepartment of General Professional Sciences, Mamun University, Khiva, UzbekistanKhayrulla UrozboevDepartment of International Scientific Journals and Ratings, Alfraganus University, Tashkent, Uzbekistan
2026
ABI
Аннотация
Advanced video understanding has become a key research area in computer vision, driven by the rapid growth of multimedia data. Deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and vision-language models, have significantly improved the accuracy of video analysis tasks such as action recognition, object tracking, event detection, and video captioning. This study provides an overview of these architectures and highlights their effectiveness in extracting spatial-temporal features for intelligent video understanding.
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