The Evolution of Computer Vision: Trends, Challenges, and the Role of Hybrid CNN-Transformer Models in Enhancing Interpretability and Training Dynamics

The goal of computer vision, a branch of artificial intelligence (AI), is to enable machines to process and interpret visual data. From manual feature extraction in the past to advanced deep learning models like VisionTransformers (ViTs) and ConvolutionalNeuralNetworks (CNNs), computer vision has evolved throughout time. Despite their success, these models face challenges related to interpretability, training complexity, and computational load. This research explores the integration of CNNs and ViTs into hybrid architectures, aiming to enhance model transparency, efficiency, and performance in real-world applications.