ARAFNet: An attribute refinement attention fusion network for advanced visual captioning

Visual captioning, at the nexus of computer vision and natural language processing, is one of the pivotal aspects of multimedia content understanding, demands precise and contextually fitting image descriptions. Attribute-based approaches with attention mechanisms are effective in this realm. However, many of these approaches struggle to capture semantic details due to the prediction of irrelevant attributes and reduced performance. In response to these challenges, we propose an innovative solution: the Attribute Refinement Attention Fusion Network (ARAFNet), which aims to produce significant captions by distinctly identifying major objects and background information. The model features a comprehensive Attribute Refinement Attention (ARA) module, equipped with an attribute attention mechanism, which interactively extracts the most important attributes according to the linguistic context. Diverse attributes are employed at different time steps, enhancing the model's capability to utilize semantic features effectively while also filtering out irrelevant attribute words, thereby enhancing the precision of semantic guidance. An integrated fusion mechanism is then introduced to narrow the semantic gap between visual and attribute features. Finally, this fusion mechanism combined with the language LSTM to generate precise and contextually relevant captions. Extensive experimentation demonstrates our model's superiority over advanced counterparts, achieving an average CIDEr-D score of 11.88% on the Flickr30K dataset and 11.25% on the MS-COCO dataset through cross-entropy optimization. The ARAFNet model consistently outperforms the baseline model across a diverse range of evaluation metrics and makes a significant contribution to the field of image captioning precision. The implementing code and associated materials will be published at https://github.com/mdbipu/ARAFNet.