Holistic Multi-modal Memory Network for Movie Question Answering
arXiv:1811.04595
Abstract
Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial interactions among data sources in one attention hop. In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop. In addition, it takes answer choices into consideration during the context retrieval stage. Therefore, the proposed framework effectively integrates multi-modal context, question, and answer information, which leads to more informative context retrieved for question answering. Our HMMN framework achieves state-of-the-art accuracy on MovieQA dataset. Extensive ablation studies show the importance of holistic reasoning and contributions of different attention strategies.
References in corpus (8)
- Hierarchical Question-Image Co-Attention for Visual Question Answering
- VQA: Visual Question Answering
- Dynamic Memory Networks for Visual and Textual Question Answering
- Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
- Compositional Attention Networks for Machine Reasoning
- Multimodal Convolutional Neural Networks for Matching Image and Sentence
- DeepStory: Video Story QA by Deep Embedded Memory Networks
- MemexQA: Visual Memex Question Answering