  This paper presents a strong baseline for real-world visual reasoning (GQA),
which achieves 60.93% in GQA 2019 challenge and won the sixth place. GQA is a
large dataset with 22M questions involving spatial understanding and multi-step
inference. To help further research in this area, we identified three crucial
parts that improve the performance, namely: multi-source features, fine-grained
encoder, and score-weighted ensemble. We provide a series of analysis on their
impact on performance.
