For example, let’s say you have this document:

[Insert image or link to document]

And the question is: “What type of fruit is shown in the picture on page 3?” The system would use its fancy algorithms and machine learning skills to analyze both the text and images in the document, figure out which one matches the question best, and then give you an answer. In this case, it might say something like “The image on page 3 shows a banana.”

But here’s where things get interesting: sometimes there are multiple answers that could be correct based on different interpretations of the text or images. That’s why we need to use some fancy techniques to help us figure out which answer is most likely to be right. For example, if the question asks “What color is the car in this picture?” and the image shows a red car but also has other cars that are blue or green, the system might look for similar images with red cars in them to see how often they appear together. If it turns out that most of the time when there’s a red car in an image, it’s also labeled as “red,” then we can be pretty confident that the answer is “The car in this picture is red.”

But what if there are no similar images with red cars? Or what if the text says something like “This car could be any color” or “We don’t know for sure”? In those cases, the system might use other techniques to help it make a decision. For example, it might look at how often certain words appear together in documents (like “red” and “car”) or it might try to figure out which answer is most likely based on common sense or prior knowledge.

Overall, Visual Question Answering for Documents is all about using machine learning and other fancy techniques to help us make better decisions when we’re trying to answer questions based on pictures and text in documents. It can be really helpful for things like legal research, medical diagnosis, or any other situation where you need to analyze a lot of information quickly and accurately.