Deep Learning Models for Computer Vision: Progress and Future Perspectives

Abstract

Deep learning has transformed the field of computer vision by enabling machines to achieve remarkable accuracy in tasks such as image classification, object detection, semantic segmentation, image generation, and visual understanding. Over the past decade, advances in convolutional neural networks (CNNs), transformer based architectures, and self-supervised learning have significantly improved the performance, scalability, and adaptability of computer vision systems across diverse applications, including healthcare, autonomous vehicles, surveillance, agriculture, and industrial automation.