# Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining Source: [Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:08:59.042Z Authors: Yundong Zhang, Juan Carlos Niebles, Alvaro Soto Published: 2018-08-01T11:06:08.000Z ## Abstract A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annotations specific for visual grounding is difficult and expensive. In this work, we demonstrate that we can effectively train a VQA architecture with grounding supervision that can be automatically obtained from available region descriptions and object annotations. We also show that our model trained with this mined supervision generates visual groundings that achieve a higher correlation with respect to manually-annotated groundings, meanwhile achieving state-of-the-art VQA accuracy. ## 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]()