Enhancing visual contextual semantic information for image captioning

The article explores the integration of feature extraction methods from computer vision with machine translation techniques to enhance visual contextual semantic information for image captioning. It begins by reviewing the evolution of image captioning, from early retrieval-based and template-based methods to the adoption of deep learning techniques, particularly the encoder-decoder structure. The article then introduces a novel dual-feature multi-fusion module based on cross-attention mechanisms, which effectively integrates grid features and segmentation features, providing advanced spatial semantic guidance for grid features. A multi-scale dilated multi-head sparse attention mechanism is also presented, enabling grid features to capture contextual information and semantic dependencies at different scales, thereby generating more effective visual information. The article further discusses the training details and experimental setup, including the use of the MS-COCO dataset and various evaluation metrics. Extensive experiments and comparisons with other models demonstrate the superior performance of the proposed approach, highlighting its effectiveness in generating high-quality image captions. The article concludes with a discussion on future research directions and the potential for further improvements in image captioning technology.