  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.
