  Recent Vision-Language Pre-training (VLP) models have demonstrated
significant advancements. Nevertheless, these models heavily rely on image-text
pairs that capture only coarse and global information of an image, leading to a
limitation in their regional understanding ability. In this work, we introduce
\textbf{RegionVLM}, equipped with explicit regional modeling capabilities,
allowing them to understand user-indicated image regions. To achieve this, we
design a simple yet innovative architecture, requiring no modifications to the
model architecture or objective function. Additionally, we leverage a dataset
that contains a novel source of information, namely Localized Narratives, which
has been overlooked in previous VLP research. Our experiments demonstrate that
our single generalist model not only achieves an interactive dialogue system
but also exhibits superior performance on various zero-shot region
understanding tasks, without compromising its ability for global image
understanding.
