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arXiv · 1801.07853

Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering

Zhe Wang·Xiaoyi Liu·Liangjian Chen·Limin Wang·Yu Qiao·Xiaohui Xie·Charless Fowlkes
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Abstract

Visual question answering (VQA) is of significant interest due to its potential to be a strong test of image understanding systems and to probe the connection between language and vision. Despite much recent progress, general VQA is far from a solved problem. In this paper, we focus on the VQA multiple-choice task, and provide some good practices for designing an effective VQA model that can capture language-vision interactions and perform joint reasoning. We explore mechanisms of incorporating part-of-speech (POS) tag guided attention, convolutional n-grams, triplet attention interactions between the image, question and candidate answer, and structured learning for triplets based on image-question pairs. We evaluate our models on two popular datasets: Visual7W and VQA Real Multiple Choice. Our final model achieves the state-of-the-art performance of 68.2% on Visual7W, and a very competitive performance of 69.6% on the test-standard split of VQA Real Multiple Choice.

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Zhe Wang, Xiaoyi Liu, Liangjian Chen, Limin Wang, Yu Qiao, Xiaohui Xie, Charless Fowlkes. 2018-01-24. Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering. https://arxiv.org/abs/1801.07853

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