Using A Unique Neural Network Framework For Visual Question Answering

Over the last few years, niche areas in artificial intelligence such as computer vision (CV) and natural language processing (NLP), have seen tremendous growth. This can be attributed to the fact that the nature of research has improved greatly in this field. Although research in AI gathers insights from various disciplines, in case of CV and NLP, there haven’t been sufficient methods to determine images and text together (known as ‘image captioning’). Apart from this, AI implementation needs a standard metric for monitoring progress, which is a tough challenge. In this article, we will explore Visual Question Answering (VQA) system used to set a response for images, and how it is made better with a unique neural network framework known as end-to-end module network. Information F
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Picture of Abhishek Sharma
Abhishek Sharma
I research and cover latest happenings in data science. My fervent interests are in latest technology and humor/comedy (an odd combination!). When I'm not busy reading on these subjects, you'll find me watching movies or playing badminton.
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