AN ANALYSIS OF BOTTOM-UP ATTENTION MODELS AND MULTIMODAL REPRESENTATION LEARNING FOR VISUAL QUESTION ANSWERING
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A Visual Question Answering (VQA) task is the ability of a system to take an image and an open-ended, natural language question about the image and provide a natural language text answer as the output. The VQA task is a relatively nascent field, with only a few strategies explored. The performance of the VQA system, in terms of accuracy of answers to the image-question pairs, requires a considerable overhaul before the system can be used in practice. The general system for performing the VQA task consists of an image encoder network, a question encoder network, a multi-modal attention network that combines the information obtained image and question, and answering network that generates natural language answers for the image-question pair. In this thesis, we follow two strategies to improve the performance (accuracy) of VQA. The first is a representation learning approach (utilizing the state-of-the-art Generative Adversarial Models (GANs) (Goodfellow, et al., 2014)) to improve the image encoding system of VQA. This thesis evaluates four variants of GANs to identify a GAN architecture that best captures the data distribution of the images, and it was determined that GAN variants become unstable and fail to become a viable image encoding system in VQA. The second strategy is to evaluate an alternative approach to the attention network, using multi-modal compact bilinear pooling, in the existing VQA system. The second strategy led to an increase in the accuracy of VQA by 2% compared to the current state-of-the-art technique.