An Improved Attention for Visual Question Answering

TL;DR

This paper introduces an enhanced attention mechanism with an Attention on Attention module within an encoder-decoder framework for Visual Question Answering, significantly improving accuracy on the VQA-v2 benchmark.

Contribution

It proposes a novel Attention on Attention (AoA) module and a multimodal fusion approach, advancing the state-of-the-art in VQA performance.

Findings

Achieves state-of-the-art results on VQA-v2 dataset

Demonstrates the effectiveness of AoA in capturing complex dependencies

Improves multimodal information integration

Abstract

We consider the problem of Visual Question Answering (VQA). Given an image and a free-form, open-ended, question, expressed in natural language, the goal of VQA system is to provide accurate answer to this question with respect to the image. The task is challenging because it requires simultaneous and intricate understanding of both visual and textual information. Attention, which captures intra- and inter-modal dependencies, has emerged as perhaps the most widely used mechanism for addressing these challenges. In this paper, we propose an improved attention-based architecture to solve VQA. We incorporate an Attention on Attention (AoA) module within encoder-decoder framework, which is able to determine the relation between attention results and queries. Attention module generates weighted average for each query. On the other hand, AoA module first generates an information vector and an attention gate using attention results and current context; and then adds another attention to generate final attended information by multiplying the two. We also propose multimodal fusion module to combine both visual and textual information. The goal of this fusion module is to dynamically decide how much information should be considered from each modality. Extensive experiments on VQA-v2 benchmark dataset show that our method achieves the state-of-the-art performance.