High-Order Attention Models for Visual Question Answering

Idan Schwartz, M.Sc. Thesis Seminar
Wednesday, 5.7.2017, 17:30
Taub 601
Prof. B. Kimelfeld, Prof. T. Hazan

The quest for algorithms which enable cognitive abilities is an important part of machine learning. A common trait in these recent cognitive-like tasks is that they take into account different data modalities, e.g., visual and lingual. We propose a novel and generally applicable form of attention mechanism that learns high-order correlations between various data modalities. We show that high-order correlations effectively direct the appropriate attention to the relevant elements in the different data modalities that are required to solve the joint task. We demonstrate the effectiveness of our high-order attention mechanism on the task of visual question answering (VQA), where we achieve state-of-the-art performance on the standard VQA dataset.

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