Exploring Diverse Methods in Visual Question Answering

arXiv:2404.13565v1 Announce Type: new
Abstract: 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 …

abstract adversarial aim arxiv attention attention mechanisms autoencoders cs.ai cs.cl cs.cv dataset diverse embeddings gan gans generate generative generative adversarial networks image improving inputs networks question question answering strategies study type visual vqa