رسالة جامعية
AN ANALYSIS OF BOTTOM-UP ATTENTION MODELS AND MULTIMODAL REPRESENTATION LEARNING FOR VISUAL QUESTION ANSWERING
| العنوان: | AN ANALYSIS OF BOTTOM-UP ATTENTION MODELS AND MULTIMODAL REPRESENTATION LEARNING FOR VISUAL QUESTION ANSWERING |
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| المؤلفون: | Narayanan, Venkatraman |
| المساهمون: | Shrivastava, Abhinav, Digital Repository at the University of Maryland, University of Maryland (College Park, Md.), Systems Engineering |
| سنة النشر: | 2019 |
| المجموعة: | University of Maryland: Digital Repository (DRUM) |
| مصطلحات موضوعية: | Artificial intelligence, Robotics, Computer science, Computer Vision, Deep learning, Natural Language Processing, Visual Question Answering |
| الوصف: | 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. |
| نوع الوثيقة: | thesis |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://doi.org/10.13016/ahta-tfksTest; http://hdl.handle.net/1903/26167Test |
| DOI: | 10.13016/ahta-tfks |
| الإتاحة: | https://doi.org/10.13016/ahta-tfksTest http://hdl.handle.net/1903/26167Test |
| رقم الانضمام: | edsbas.3F14F492 |
| قاعدة البيانات: | BASE |
| DOI: | 10.13016/ahta-tfks |
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