Comprehensive Analysis on Image Captioning Approaches

In this work, we explore the progressive strides made in image captioning, a discipline at the confluence of deep learning and computer vision. Our detailed examination sheds light on the evolutionary path of image captioning techniques, from traditional encoder-decoder models to innovative approaches like generative adversarial networks (GANs), attention mechanisms, and integrations of CNNs with LSTMs. By incorporating a comprehensive analysis of recent academic contributions, we compare these methodologies based on evaluation metrics such as the Bleu, Rouge, and Cider scores, providing a quantitative backdrop to our review. Furthermore, our work extends beyond theoretical models to consider their real-world applicability, identifying existing challenges and suggesting future avenues for research within the image captioning domain. This exhaustive investigation aims to offer an insightful overview to scholars, professionals, and enthusiasts engaged in the fields of computer vision and natural language processing, underscored by a critical assessment of dataset improvements and metric advancements driving this domain forward.