# MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering Source: [MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:40:59.400Z Authors: Aisha Urooj Khan, Amir Mazaheri, Niels da Vitoria Lobo, Mubarak Shah Published: 2020-10-27T06:34:14.000Z ## Abstract We present MMFT-BERT(MultiModal Fusion Transformer with BERT encodings), to solve Visual Question Answering (VQA) ensuring individual and combined processing of multiple input modalities. Our approach benefits from processing multimodal data (video and text) adopting the BERT encodings individually and using a novel transformer-based fusion method to fuse them together. Our method decomposes the different sources of modalities, into different BERT instances with similar architectures, but variable weights. This achieves SOTA results on the TVQA dataset. Additionally, we provide TVQA-Visual, an isolated diagnostic subset of TVQA, which strictly requires the knowledge of visual (V) modality based on a human annotator's judgment. This set of questions helps us to study the model's behavior and the challenges TVQA poses to prevent the achievement of super human performance. Extensive experiments show the effectiveness and superiority of our method. ## 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]()