RECENT DEVELOPMENTS IN CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE-BASED TASKS

Abstract

Convolutional Neural Networks (CNNs) have become foundational in addressing image-based tasks across diverse domains such as medical imaging, autonomous driving, remote sensing, and surveillance. Recent developments in CNN architectures have significantly enhanced model performance, efficiency, and generalization capabilities. Innovations such as residual connections, depthwise separable convolutions, attention mechanisms, and hybrid models combining CNNs with transformers have led to breakthroughs in image classification, object detection, image segmentation, and super-resolution. In addition, advances in training strategies, including self-supervised learning and transfer learning, have expanded the applicability of CNNs to scenarios with limited labeled data. This paper reviews and synthesizes the latest advancements in CNN architectures and training techniques, analyzes their performance across key benchmarks, and discusses ongoing challenges and future research directions in the field of image-based AI systems.