  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.
