A key goal for the advancement of AI is to develop technologies that serve
the needs not just of one group but of all communities regardless of their
geographical region. In fact, a significant proportion of knowledge is locally
shared by people from certain regions but may not apply equally in other
regions because of cultural differences. If a model is unaware of regional
characteristics, it may lead to performance disparity across regions and result
in bias against underrepresented groups. We propose GIVL, a Geographically
Inclusive Vision-and-Language Pre-trained model. There are two attributes of
geo-diverse visual concepts which can help to learn geo-diverse knowledge: 1)
concepts under similar categories have unique knowledge and visual
characteristics, 2) concepts with similar visual features may fall in
completely different categories. Motivated by the attributes, we design new
pre-training objectives Image Knowledge Matching (IKM) and Image Edit Checking
(IEC) to pre-train GIVL. Compared with similar-size models pre-trained with
similar scale of data, GIVL achieves state-of-the-art (SOTA) and more balanced
performance on geo-diverse V&L tasks.

提出了 Geographically Inclusive Vision-and-Language Pre-trained model（GIVL）的概念。GIVL 是一种视觉语言预训练模型，利用图像知识匹配（IKM）和图像编辑检查（IEC）两种新的预训练目标。与以类似数据规模预先培训的类似规模模型相比，GIVL 在地理多元化的视觉语言任务上表现出更好的性能平衡和最新技术水平（SOTA）。