Attribute-Driven Filtering: A new attributes predicting approach for fine-grained image captioning

Fine-grained image captioning with attribute information has garnered significant attention in the realms of computer vision and natural language processing, demanding precise and contextually relevant descriptions of visual content. While previous attribute-driven image captioning models have shown improvements, challenges remain, such as the independence of attribute predictors and caption generators and the semantic gap between images and attributes. Another common issue is the inclusion of all attributes at every time step, despite most attributes being irrelevant to the word currently being generated. This can divert the model's attention toward erroneous semantic details, resulting in a performance decline. To address these issues, we propose a novel Attribute-Driven Filtering (ADF) captioning network designed to provide rich and nuanced descriptions. This model incorporates a unique Attribute Predictor Module (APM) that dynamically predicts the most pertinent attributes in accordance with the textual context, utilizing different attributes at various time steps. The novelty of this approach lies in recognizing that not all attributes hold equal relevance at each time step, and the APM filters out irrelevant attributes to generate precise and contextually relevant captions. Furthermore, this model features a fusion mechanism that integrates visual information from a conventional attention module with attribute information predicted by the APM, aiming to reduce the visual semantic gap between images and attributes. Extensive experimentation demonstrates that the ADF model outperforms advanced models, achieving impressive CIDEr-D scores of 72.0 (Flickr30K) and 123.3 (MS-COCO) through reinforcement learning optimization. It consistently surpasses baseline models across diverse evaluation metrics, highlighting its effectiveness and robustness.