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

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering