  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.
