sections/intro/intro_part_1.md

Visual Question Answering (VQA) is a task where we expect the AI to answer a question about a given image. VQA has been an active area of research for the past 4-5 years, with most datasets using natural images found online. Two examples of such datasets are: [VQAv2](https://visualqa.org/challenge.html), [GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html). VQA is a particularly interesting multi-modal machine learning challenge because it has several interesting applications across several domains including healthcare chatbots, interactive-agents, etc. **However, most VQA challenges or datasets deal with English-only captions and questions.** 

In addition, even recent **approaches that have been proposed for VQA generally are obscure** due to the fact that CNN-based object detectors are relatively difficult to use and more complex for feature extraction. Click on the expandable region below to see steps for FasterRCNN-based approach.