Systems and methods for attention-based configurable convolutional neural networks (ABC-CNN) for visual question answering
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United States of America Patent
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May 8, 2018
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May 4, 2017
app pub date -
Jun 16, 2016
filing date -
Nov 3, 2015
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Abstract
Described herein are systems and methods for generating and using attention-based deep learning architectures for visual question answering task (VQA) to automatically generate answers for image-related (still or video images) questions. To generate the correct answers, it is important for a model's attention to focus on the relevant regions of an image according to the question because different questions may ask about the attributes of different image regions. In embodiments, such question-guided attention is learned with a configurable convolutional neural network (ABC-CNN). Embodiments of the ABC-CNN models determine the attention maps by convolving image feature map with the configurable convolutional kernels determined by the questions semantics. In embodiments, the question-guided attention maps focus on the question-related regions and filters out noise in the unrelated regions.
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