| Pub. Date | : Dec, 2023 |
|---|---|
| Product Name | : The IUP Journal of Information Technology |
| Product Type | : Article |
| Product Code | : IJIT011223 |
| Author Name | : Sushma Jaiswal, Harikumar Pallthadka, Rajesh P Chinchewadi and Tarun Jaiswal |
| Availability | : YES |
| Subject/Domain | : Engineering |
| Download Format | : PDF Format |
| No. of Pages | : 19 |
Attention-GAN is an innovative model for captioning images that combines generative adversarial networks (GANs) with attention mechanisms in a smooth and seamless manner. The proposed model comprises two main parts. In order to prioritize important visual components for contextually rich captions, an attention-based caption generator first creates strong associations between visual areas and caption segments. Second, the introduction of aesthetic variation through an adversarial training process results in refined and styled descriptions that incorporate creative variances as well as content. This dual-component approach generates engaging and diverse image captions by fusing creativity through adversarial learning with accuracy through attention-based modeling. The capacity of Attention-GAN to produce visually beautiful and contextually relevant captions is demonstrated through extensive trials on benchmark datasets. Both quantitative and qualitative analyses validate the model's ability to generate captions that are consistent with image content and accommodate a range of artistic subtleties. For a broad range of computer vision and natural language processing applications, Attention-GAN is a promising technology that bridges the gap between factual description and creative expression.
The automatic creation of appropriate captions for images is a fascinating subject at the intersection of natural language processing and computer vision. This task is known as image captioning. Our contribution to this field is Attention-GAN, a paradigmshifting model for image captioning that reimagines the field by utilizing generative adversarial networks (GANs) and attention processes. The Attention-GAN's dualcomponent architecture improves the quality of caption creation. The model can match caption segments to image regions causing an attention-based caption generator
Convolutional neural network (CNN), Long short-term memory (LSTM), Image caption, Monte-Carlo (MC) search, Attention-GAN