# Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering Source: [Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:19:52.933Z Authors: Duy-Kien Nguyen, Takayuki Okatani Published: 2018-04-03T01:24:23.000Z ## Abstract A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question. We show that an attention mechanism that enables dense, bi-directional interactions between the two modalities contributes to boost accuracy of prediction of answers. Specifically, we present a simple architecture that is fully symmetric between visual and language representations, in which each question word attends on image regions and each image region attends on question words. It can be stacked to form a hierarchy for multi-step interactions between an image-question pair. We show through experiments that the proposed architecture achieves a new state-of-the-art on VQA and VQA 2.0 despite its small size. We also present qualitative evaluation, demonstrating how the proposed attention mechanism can generate reasonable attention maps on images and questions, which leads to the correct answer prediction. ## 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]()