We propose a novel attention based deep learning architecture for visual
question answering task (VQA). Given an image and an image related natural
language question, VQA generates the natural language answer for the question.
Generating the correct answers requires the model's attention to focus on the
regions corresponding to the question, because different questions inquire
about the attributes of different image regions. We introduce an attention
based configurable convolutional neural network (ABC-CNN) to learn such
question-guided attention. ABC-CNN determines an attention map for an
image-question pair by convolving the image feature map with configurable
convolutional kernels derived from the question's semantics. We evaluate the
ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR,
and VQA dataset. ABC-CNN model achieves significant improvements over
state-of-the-art methods on these datasets. The question-guided attention
generated by ABC-CNN is also shown to reflect the regions that are highly
relevant to the questions.