Image Captioning with Multimodal Guidance and Search Space Optimization

Image captioning bridges the gap between visual perception and natural language understanding by transforming image content into descriptive text. While existing methods have made significant progress in visual feature extraction, encoding, and cross-modal semantic alignment, challenges remain in terms of fine-grained feature representation, cross-modal alignment efficiency, and suboptimal search strategies. To address these issues, a multimodal-guided and search space-optimized image captioning model is proposed. In the visual encoding stage, we construct a hierarchical network that integrates regional and grid features through a geometry-constrained multi-layer feature aggregation mechanism, which enhances the model's capability to jointly capture global semantics and local details. In the decoding stage, we introduce a dynamic grouped beam width adjustment strategy to improve semantic path exploration. Additionally, a diversity-driven scoring function is designed to enforce intra-group diversity rewards and inter-group similarity penalties, encouraging the generation of more diverse captions. Finally, we incorporate a two-level pruning algorithm based on syntactic and spatial logic constraints to refine the search space from both hard and soft constraint perspectives, improving both the accuracy and diversity of generated captions. A 3% improvement in CIDEr is achieved by the proposed method over state-of-the-art (SOTA) models, as demonstrated by experiments on the COCO and Flickr30k datasets.