High-order Attention Models For Visual Question Answering | Awesome LLM Papers Add your paper to Awesome LLM Papers

High-order Attention Models For Visual Question Answering

Idan Schwartz, Alexander G. Schwing, Tamir Hazan . Arxiv 2017 – 50 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Compositional Generalization Content Enrichment Datasets Interdisciplinary Approaches Model Architecture Question Answering Variational Autoencoders Visual Question Answering

The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper 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.

Similar Work