# DualNet: Domain-Invariant Network for Visual Question Answering Source: [DualNet: Domain-Invariant Network for Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:37:15.413Z Authors: Kuniaki Saito, Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada Published: 2016-06-20T13:28:35.000Z ## Abstract Visual question answering (VQA) task not only bridges the gap between images and language, but also requires that specific contents within the image are understood as indicated by linguistic context of the question, in order to generate the accurate answers. Thus, it is critical to build an efficient embedding of images and texts. We implement DualNet, which fully takes advantage of discriminative power of both image and textual features by separately performing two operations. Building an ensemble of DualNet further boosts the performance. Contrary to common belief, our method proved effective in both real images and abstract scenes, in spite of significantly different properties of respective domain. Our method was able to outperform previous state-of-the-art methods in real images category even without explicitly employing attention mechanism, and also outperformed our own state-of-the-art method in abstract scenes category, which recently won the first place in VQA Challenge 2016. ## 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. - [Visual Question Answering on abstract scenes (test)](): Visual Question Answering; abstract scenes (test); Overall Accuracy (MC): 71.18. ## Original sources and implementation [Original paper]() [PDF]()