# Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering Source: [Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:11:58.696Z Authors: Medhini Narasimhan, Alexander G. Schwing Published: 2018-09-04T17:59:55.000Z ## Abstract Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()