  This study explores innovative methods for improving Visual Question
Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and
attention mechanisms. Leveraging a balanced VQA dataset, we investigate three
distinct strategies. Firstly, GAN-based approaches aim to generate answer
embeddings conditioned on image and question inputs, showing potential but
struggling with more complex tasks. Secondly, autoencoder-based techniques
focus on learning optimal embeddings for questions and images, achieving
comparable results with GAN due to better ability on complex questions. Lastly,
attention mechanisms, incorporating Multimodal Compact Bilinear pooling (MCB),
address language priors and attention modeling, albeit with a
complexity-performance trade-off. This study underscores the challenges and
opportunities in VQA and suggests avenues for future research, including
alternative GAN formulations and attentional mechanisms.
