From Convolution to Attention and Beyond: A Systematic Review of Modern Vision Architectures

Abstract

In the last decade, we have witnessed the immense development and impact of the computer vision domain and how it has affected various aspects of life. The trigger for this evolution came in September 2012, when AlexNet, a neural network architecture, achieved unprecedented results on the ImageNet Large Scale Visual Recognition Challenge, marking a turning point for deep learning–based visual recognition. This led to significant progress in deep learning over the next few decades, spurring advances in vision tasks including image classification, object detection, segmentation, and generative modeling. This systematic literature review provides a chronological analysis of the developments that have shaped modern computer vision. It reviews the early days of computer vision and its advances in model architectures from convolutional networks to residual, attention, transformer, and hybrid architectures, and it closely analyzes important design patterns about connectivity and efficiency. It also considers the spectrum of learning strategies, ranging from supervised to self-supervised, weakly supervised, and open-vocabulary learning, as well as transfer and multi-task methodologies. The review further highlights optimization and scheduling methods that facilitate the training of large-scale models, and analyzes how performance commonly goes beyond accuracy to include localization quality and efficiency metrics. In short, this is a critical review of computer vision, illustrating how architectural design, learning paradigms, and evaluation practices have co-evolved over time to facilitate more flexible and scalable systems, and outlining new research directions.