  Well-formed context aware image captions and tags in enterprise content such
as marketing material are critical to ensure their brand presence and content
recall. Manual creation and updates to ensure the same is non trivial given the
scale and the tedium towards this task. We propose a new unified
Vision-Language (VL) model based on the One For All (OFA) model, with a focus
on context-assisted image captioning where the caption is generated based on
both the image and its context. Our approach aims to overcome the
context-independent (image and text are treated independently) nature of the
existing approaches. We exploit context by pretraining our model with datasets
of three tasks: news image captioning where the news article is the context,
contextual visual entailment, and keyword extraction from the context. The
second pretraining task is a new VL task, and we construct and release two
datasets for the task with 1.1M and 2.2K data instances. Our system achieves
state-of-the-art results with an improvement of up to 8.34 CIDEr score on the
benchmark news image captioning datasets. To the best of our knowledge, ours is
the first effort at incorporating contextual information in pretraining the
models for the VL tasks.
