A Read-Write Memory Network for Movie Story Understanding
This work addresses multimodal question answering for movies, which is an incremental improvement in memory network design for sequential story representation.
The authors tackled movie story understanding by proposing a Read-Write Memory Network (RWMN) that uses multi-layered CNNs for memory operations, achieving the best accuracies on several tasks in the MovieQA benchmark, especially in visual QA.
We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal movie story understanding. The key focus of our RWMN model is to design the read network and the write network that consist of multiple convolutional layers, which enable memory read and write operations to have high capacity and flexibility. While existing memory-augmented network models treat each memory slot as an independent block, our use of multi-layered CNNs allows the model to read and write sequential memory cells as chunks, which is more reasonable to represent a sequential story because adjacent memory blocks often have strong correlations. For evaluation, we apply our model to all the six tasks of the MovieQA benchmark, and achieve the best accuracies on several tasks, especially on the visual QA task. Our model shows a potential to better understand not only the content in the story, but also more abstract information, such as relationships between characters and the reasons for their actions.