Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
This addresses the challenge of reasoning with general knowledge in AI for visual question answering, representing an incremental improvement over existing methods.
The paper tackled the problem of factual visual question answering by proposing a graph convolutional network to jointly consider all entities instead of processing facts sequentially, achieving a 7% improvement in accuracy on the FVQA dataset.
Accurately answering a question about a given image requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge remains an algorithmic challenge. To advance research in this direction a novel `fact-based' visual question answering (FVQA) task has been introduced recently along with a large set of curated facts which link two entities, i.e., two possible answers, via a relation. Given a question-image pair, deep network techniques have been employed to successively reduce the large set of facts until one of the two entities of the final remaining fact is predicted as the answer. We observe that a successive process which considers one fact at a time to form a local decision is sub-optimal. Instead, we develop an entity graph and use a graph convolutional network to `reason' about the correct answer by jointly considering all entities. We show on the challenging FVQA dataset that this leads to an improvement in accuracy of around 7% compared to the state of the art.