scholarly journals MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering

2020 ◽  
Author(s):  
Aisha Urooj ◽  
Amir Mazaheri ◽  
Niels Da vitoria lobo ◽  
Mubarak Shah
Keyword(s):  
Question Answering ◽  
Multimodal Fusion ◽  
Visual Question Answering

scholarly journals BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection

2019 ◽  
Vol 33 ◽  
pp. 8102-8109 ◽  
Author(s):  
Hedi Ben-younes ◽  
Remi Cadene ◽  
Nicolas Thome ◽  
Matthieu Cord
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Question Answering ◽  
Practical Interest ◽  
Tensor Decomposition ◽  
Representation Learning ◽  
Multimodal Fusion ◽  
Practical Implementation ◽  
Fusion Model ◽  
Visual Question Answering

Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combination of modalities, their number of parameters grows quadratically with the input dimensions, making their practical implementation within classical deep learning pipelines challenging. In this paper, we introduce BLOCK, a new multimodal fusion based on the block-superdiagonal tensor decomposition. It leverages the notion of block-term ranks, which generalizes both concepts of rank and mode ranks for tensors, already used for multimodal fusion. It allows to define new ways for optimizing the tradeoff between the expressiveness and complexity of the fusion model, and is able to represent very fine interactions between modalities while maintaining powerful mono-modal representations. We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities. Through extensive experiments, we show that BLOCK compares favorably with respect to state-of-the-art multimodal fusion models for both VQA and VRD tasks. Our code is available at https://github.com/Cadene/block.bootstrap.pytorch.

Cross-modality co-attention networks for visual question answering

2021 ◽  
Author(s):  
Dezhi Han ◽  
Shuli Zhou ◽  
Kuan Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Question Answering ◽  
Attention Networks ◽  
Visual Question Answering
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