Research on fine-grained image description generation method based on deep learning

With the rapid development of deep learning technology, image description generation has emerged as a hot research direction in the field of artificial intelligence, aiming to interpret image content through automatically generated natural language descriptions. Particularly, fine-grained image description generation, which delves into the detailed information within images to provide richer and more precise descriptions, holds broad application prospects, including aiding navigation for the visually impaired, automatic content creation, and augmented reality, among others. This study explores the technology of fine-grained image description generation, proposing a deep learning model that integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance the quality and accuracy of image descriptions. We employed a composite loss function to refine the model’s training process and introduced an attention mechanism to better capture key information in images. Through experimental validation on multiple public datasets, our model demonstrated superior performance to existing technologies. Additionally, we analyzed key factors affecting model performance and discussed potential directions for future improvements. The outcomes of this study not only provide an effective method for fine-grained image description generation but also pave new pathways for the application of deep learning technology in image understanding and natural language processing.