Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semantically different question types. A lot of previous works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. In this work, we propose Question Type-guided Attention (QTA). It utilizes the information of question type to dynamically balance between bottom-up and top-down visual features, respectively extracted from ResNet and Faster R-CNN networks. We experiment with multiple VQA architectures with extensive input ablation studies over the TDIUC dataset and show that QTA systematically improves the performance by more than 5% across multiple question type categories such as "Activity Recognition", "Utility" and "Counting" on TDIUC dataset. By adding QTA on the state-of-art model MCB, we achieve 3% improvement for overall accuracy. Finally, we propose a multi-task extension to predict question types which generalizes QTA to applications that lack of question type, with minimal performance loss.

本文提出了一个名为Question Type-guided Attention (QTA)的方法，利用问题类型信息动态平衡从ResNet和Faster R-CNN网络中分别提取的自下而上和自上而下的视觉特征。在TDIUC数据集上进行了多项输入实验，并显示QTA显著提高了多个问题类型类别的性能，例如“活动识别”、“效用”和“计数”。添加QTA后，我们在最先进的MCB模型上实现了3％的总体准确度提高。最后，我们提出了一个多任务扩展以预测问题类型，在缺少问题类型的应用程序中推广QTA，性能损失最小。

视觉问答中的问题类型引导注意力机制