This study explores innovative methods to enhance Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. The research is conducted on a balanced VQA dataset, focusing on three distinct strategies: GAN-based approaches, autoencoder-based techniques, and attention mechanisms incorporating Multinodal Compact Bilinear pooling (MCB). 1. **GAN-Based Mechanism**: GANs are applied to generate answer embeddings conditioned on image and question inputs. The study investigates two approaches for combining image and question feature vectors, and two generator architectures. The training algorithm follows the GAN-CLS method, with variations including pre-training the generator and adding noise to the generator's conditioning input. 2. **Autoencoder-Based Mechanism**: This approach modifies the GAN technique by passing concatenated features through an autoencoder to generate low-dimensional embeddings. The goal is to learn the optimal embedding for question and image features. 3. **Attention Based Mechanism**: This mechanism addresses language priors and attention modeling using MCB. It combines visual and textual features more effectively, outperforming both GAN-based and autoencoder-based approaches in complex question answering scenarios but at the cost of increased computational complexity. The study concludes that: - **GANs**: Show marked improvement over baseline methods when pretraining is selectively applied, specifically pretraining the generator but not the discriminator. - **Autoencoders**: Are effective at learning optimal embeddings for question and image data, achieving slightly better results on complex questions compared to GANs. - **Attention Mechanisms**: Provide substantial benefits in addressing language priors and improving attention modeling, outperforming both GANs and autoencoders in complex scenarios. Overall, while these innovative methods show promise, they each come with unique challenges. Future research should focus on refining these methods, exploring alternative GAN formulations, and enhancing stability techniques for more complex tasks. Additionally, hybrid models combining these techniques could yield further improvements in performance and efficiency.This study explores innovative methods to enhance Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. The research is conducted on a balanced VQA dataset, focusing on three distinct strategies: GAN-based approaches, autoencoder-based techniques, and attention mechanisms incorporating Multinodal Compact Bilinear pooling (MCB). 1. **GAN-Based Mechanism**: GANs are applied to generate answer embeddings conditioned on image and question inputs. The study investigates two approaches for combining image and question feature vectors, and two generator architectures. The training algorithm follows the GAN-CLS method, with variations including pre-training the generator and adding noise to the generator's conditioning input. 2. **Autoencoder-Based Mechanism**: This approach modifies the GAN technique by passing concatenated features through an autoencoder to generate low-dimensional embeddings. The goal is to learn the optimal embedding for question and image features. 3. **Attention Based Mechanism**: This mechanism addresses language priors and attention modeling using MCB. It combines visual and textual features more effectively, outperforming both GAN-based and autoencoder-based approaches in complex question answering scenarios but at the cost of increased computational complexity. The study concludes that: - **GANs**: Show marked improvement over baseline methods when pretraining is selectively applied, specifically pretraining the generator but not the discriminator. - **Autoencoders**: Are effective at learning optimal embeddings for question and image data, achieving slightly better results on complex questions compared to GANs. - **Attention Mechanisms**: Provide substantial benefits in addressing language priors and improving attention modeling, outperforming both GANs and autoencoders in complex scenarios. Overall, while these innovative methods show promise, they each come with unique challenges. Future research should focus on refining these methods, exploring alternative GAN formulations, and enhancing stability techniques for more complex tasks. Additionally, hybrid models combining these techniques could yield further improvements in performance and efficiency.