Image Classification

Image classification, the task of assigning predefined labels to images, aims to develop robust and accurate algorithms for diverse applications. Current research emphasizes improving generalization to unseen data and handling challenges like data scarcity, class imbalance, and adversarial attacks, often employing deep learning models such as convolutional neural networks (CNNs), vision transformers (ViTs), and large language models (LLMs) integrated with techniques like self-supervised learning, data augmentation, and uncertainty quantification. These advancements are crucial for various fields, including medical diagnosis, autonomous driving, and environmental monitoring, where reliable and efficient image analysis is paramount.