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This research aims to develop a framework for generating accurate and semantically rich image captions.

A Hybrid Generative Adversarial Framework for Image Captioning with Reinforced Attention and Metaheuristic Optimization

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This research aims to develop a framework for generating accurate and semantically rich image captions.
Introduced a hybrid Attention-reinforced transformer with Serval-Frigatebird Optimization.
Utilized contrastive learning to align generated captions with visual content.
Implemented a Reinforcement Learning generator for refining caption quality.
Achieved Recall-Oriented Understudy for Gisting Evaluation-L score of 60.19%.
Obtained Mean Average Precision score of 80.13%.
Secured Bilingual Evaluation Understudy score of 84.23% and CIDEr of 123.3.

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Randomized trial demonstrates improved image caption accuracy in dataset, suggesting enhanced NLP capabilities.

Girija et al. (2026) studied this question.

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