  In this paper, we propose to investigate the problem of out-of-domain
visio-linguistic pretraining, where the pretraining data distribution differs
from that of downstream data on which the pretrained model will be fine-tuned.
Existing methods for this problem are purely likelihood-based, leading to the
spurious correlations and hurt the generalization ability when transferred to
out-of-domain downstream tasks. By spurious correlation, we mean that the
conditional probability of one token (object or word) given another one can be
high (due to the dataset biases) without robust (causal) relationships between
them. To mitigate such dataset biases, we propose a Deconfounded
Visio-Linguistic Bert framework, abbreviated as DeVLBert, to perform
intervention-based learning. We borrow the idea of the backdoor adjustment from
the research field of causality and propose several neural-network based
architectures for Bert-style out-of-domain pretraining. The quantitative
results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and
Visual Question Answering, show the effectiveness of DeVLBert by boosting
generalization ability.
