Stylistic Image Captioning with Adversarial Learning: A Novel Approach

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Sushma Jaiswal
Harikumar Pallthadka
Rajesh P. Chinchewadi
Tarun Jaiswal


this paper present "Attention-GAN," a new image captioning model that synergistically integrates attention mechanisms and Generative Adversarial Networks (GANs) to revolutionize image caption production. Attention-GAN has two main parts. First, an attention-based caption generator that strongly correlates visual regions with caption segments. This attention mechanism helps the model highlight important visual aspects and provide meaningful, contextual captions. Second, an adversarial training process adds aesthetic diversity to the caption generator. Adversarial training produces more subtle and different stylized descriptions, resulting in captions that express the image's content and aesthetic and stylistic variances. More interesting and varied image captions result from our dual-component technique, which blends attention-based modelling precision with adversarial learning inventiveness. Attention-GAN generates contextually relevant and artistically appealing captions in extensive benchmark dataset trials. Quantitative and qualitative analyses show that the model is capable of creating captions that match image content and have varied stylistic subtleties. Attention-GAN is a promising image captioning technology that can bridge the gap between factual description and creative expression for a variety of computer vision and natural language processing applications.

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How to Cite
Sushma Jaiswal, Harikumar Pallthadka, Rajesh P. Chinchewadi, and Tarun Jaiswal, “Stylistic Image Captioning with Adversarial Learning: A Novel Approach”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 1, pp. 1–8, Jan. 2024.
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