# VQA with Cascade of Self- and Co-Attention Blocks

## TL;DR

This paper introduces a novel VQA model that employs a cascade of self- and co-attention blocks to enhance multi-modal representation through dense visual-textual interactions, improving performance on standard datasets.

## Contribution

It proposes a cascade architecture combining self- and co-attention modules for better multi-modal feature learning in VQA tasks.

## Key findings

- Improved accuracy on VQA2.0 and TDIUC datasets.
- Key components validated through ablation studies.
- Cascading attention modules enhances multi-modal interaction.

## Abstract

The use of complex attention modules has improved the performance of the Visual Question Answering (VQA) task. This work aims to learn an improved multi-modal representation through dense interaction of visual and textual modalities. The proposed model has an attention block containing both self-attention and co-attention on image and text. The self-attention modules provide the contextual information of objects (for an image) and words (for a question) that are crucial for inferring an answer. On the other hand, co-attention aids the interaction of image and text. Further, fine-grained information is obtained from two modalities by using a Cascade of Self- and Co-Attention blocks (CSCA). This proposal is benchmarked on the widely used VQA2.0 and TDIUC datasets. The efficacy of key components of the model and cascading of attention modules are demonstrated by experiments involving ablation analysis.