Deep Learning Techniques for Image Recognition and Computer Vision

Abstract

Deep learning has revolutionized image recognition and computer vision by enabling computers to automatically learn complex visual patterns from large datasets. Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs) have significantly improved the accuracy of object detection, image classification, facial recognition, medical image analysis, autonomous driving, and surveillance systems. The increasing availability of high-performance computing, cloud platforms, and large annotated datasets has accelerated research and industrial adoption. However, challenges such as computational cost, model interpretability, data bias, privacy, and adversarial attacks remain important concerns. This paper examines major deep learning techniques for image recognition and computer vision, their applications, challenges, and future directions.