  Visual Question Answering (VQA) is a challenging multimodal task to answer
questions about an image. Many works concentrate on how to reduce language bias
which makes models answer questions ignoring visual content and language
context. However, reducing language bias also weakens the ability of VQA models
to learn context prior. To address this issue, we propose a novel learning
strategy named CCB, which forces VQA models to answer questions relying on
Content and Context with language Bias. Specifically, CCB establishes Content
and Context branches on top of a base VQA model and forces them to focus on
local key content and global effective context respectively. Moreover, a joint
loss function is proposed to reduce the importance of biased samples and retain
their beneficial influence on answering questions. Experiments show that CCB
outperforms the state-of-the-art methods in terms of accuracy on VQA-CP v2.
