  We present a novel unsupervised feature representation learning method,
Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to
serve as an improved visual region encoder for high-level tasks such as
captioning and VQA. Given a set of detected object regions in an image (e.g.,
using Faster R-CNN), like any other unsupervised feature learning methods
(e.g., word2vec), the proxy training objective of VC R-CNN is to predict the
contextual objects of a region. However, they are fundamentally different: the
prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while
others are by using the conventional likelihood: P(Y|X). This is also the core
reason why VC R-CNN can learn "sense-making" knowledge like chair can be sat --
while not just "common" co-occurrences such as chair is likely to exist if
table is observed. We extensively apply VC R-CNN features in prevailing models
of three popular tasks: Image Captioning, VQA, and VCR, and observe consistent
performance boosts across them, achieving many new state-of-the-arts. Code and
feature are available at https://github.com/Wangt-CN/VC-R-CNN.
