  Bilinear models provide rich representations compared with linear models.
They have been applied in various visual tasks, such as object recognition,
segmentation, and visual question-answering, to get state-of-the-art
performances taking advantage of the expanded representations. However,
bilinear representations tend to be high-dimensional, limiting the
applicability to computationally complex tasks. We propose low-rank bilinear
pooling using Hadamard product for an efficient attention mechanism of
multimodal learning. We show that our model outperforms compact bilinear
pooling in visual question-answering tasks with the state-of-the-art results on
the VQA dataset, having a better parsimonious property.

Computer Vision and Pattern Recognition;Artificial Intelligence;Neural and Evolutionary Computing