  Commonsense is defined as the knowledge that is shared by everyone. However,
certain types of commonsense knowledge are correlated with culture and
geographic locations and they are only shared locally. For example, the
scenarios of wedding ceremonies vary across regions due to different customs
influenced by historical and religious factors. Such regional characteristics,
however, are generally omitted in prior work. In this paper, we construct a
Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test
vision-and-language models' ability to understand cultural and
geo-location-specific commonsense. In particular, we study two state-of-the-art
Vision-and-Language models, VisualBERT and ViLBERT trained on VCR, a standard
multimodal commonsense benchmark with images primarily from Western regions. We
then evaluate how well the trained models can generalize to answering the
questions in GD-VCR. We find that the performance of both models for
non-Western regions including East Asia, South Asia, and Africa is
significantly lower than that for Western region. We analyze the reasons behind
the performance disparity and find that the performance gap is larger on QA
pairs that: 1) are concerned with culture-related scenarios, e.g., weddings,
religious activities, and festivals; 2) require high-level geo-diverse
commonsense reasoning rather than low-order perception and recognition. Dataset
and code are released at https://github.com/WadeYin9712/GD-VCR.
