Adaptive Re-Balancing Network with Gate Mechanism for Long-Tailed Visual Question Answering

Visual Question Answering (VQA) is a challenging task which requires a fine-grained semantic understanding of visual and textual contents. Existing works focus on better modality representations. However, these methods give little consideration to the long-tailed data distribution in common VQA datasets. The extreme class imbalance causes training bias to behave well in head class, but fail in tail class. Therefore, we propose a unified Adaptive Re-balancing Network (ARN) to take care of classification in both head and tail classes, exhaustively improving performance for VQA. Specifically, two training branches are introduced to per-form their own duty iteratively, which learn the universal representations first and then emphasize the tail data progressively by the re-balancing branch with adaptive learning. Meanwhile, contextual information in the question is vital for guiding accurate visual attention. Thus our network is further equipped with a novel gate mechanism to give higher weight to contextual information. The Experimental results on common benchmarks such as VQA-v2 have demonstrated the superiority of our method compared with state of the art.