Enhancing Vision-Language Pre-training with Rich Supervisions
Yuan Gao Kunyu Shi Pengkai Zhu Edouard Belval Oren Nuriel Srikar Appalaraju Shabnam Ghadar Vijay Mahadevan Zhuowen Tu Stefano Soatto
We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel
pre-training paradigm for Vision-Language Models using data from large-scale
web screenshot rendering. Using web screenshots unlocks a treasure trove of
visual and textual cues that are not present in using image-text pairs. In S4,
we leverage the inherent tree-structured hierarchy of HTML elements and the
spatial localization to carefully design 10 pre-training tasks with large scale
annotated data. These tasks resemble downstream tasks across different domains
and the annotations are cheap to obtain. We demonstrate that, compared to
current screenshot pre-training objectives, our innovative pre-training method
significantly enhances performance of image-to-text model in nine varied and
popular downstream tasks - up to 76.1% improvements on Table Detection, and at
least 1% on Widget Captioning.