Visual question answering (VQA) task demands proficiency in processing multi-modal
information, and the ability to reason effectively using the information. One promising
method for this task is neural-symbolic (NS) learning, which leverages the strengths of both
neural network (NN) learning and symbolic reasoning to achieve efficient VQA. However,
current NS approaches do not account for the uncertain nature of NN learning and can only
provide a single answer to a question without any indication of its confidence, thereby …

A method of performing visual question answering (VQA), including: obtaining an image and
a question corresponding to the image; generating a plurality of feature predictions about at
least one object included in the image by providing the image to an artificial intelligence (AI)
scene perception model; generating a plurality of symbolic programs and a plurality of
program confidence scores by providing the question to an AI question parsing model;
selecting a symbolic program associated with a program confidence score which is highest …