Learning from Models and Data for Visual Grounding
Ruozhen He Paola Cascante-Bonilla Ziyan Yang Alexander C. Berg Vicente Ordonez
We introduce SynGround, a novel framework that combines data-driven learning
and knowledge transfer from various large-scale pretrained models to enhance
the visual grounding capabilities of a pretrained vision-and-language model.
The knowledge transfer from the models initiates the generation of image
descriptions through an image description generator. These descriptions serve
dual purposes: they act as prompts for synthesizing images through a
text-to-image generator, and as queries for synthesizing text, from which
phrases are extracted using a large language model. Finally, we leverage an
open-vocabulary object detector to generate synthetic bounding boxes for the
synthetic images and texts. We finetune a pretrained vision-and-language model
on this dataset by optimizing a mask-attention consistency objective that
aligns region annotations with gradient-based model explanations. The resulting
model improves the grounding capabilities of an off-the-shelf
vision-and-language model. Particularly, SynGround improves the pointing game
accuracy of ALBEF on the Flickr30k dataset from 79.38% to 87.26%, and on
RefCOCO+ Test A from 69.35% to 79.06% and on RefCOCO+ Test B from 53.77% to
63.67%.