  Recent advances in deep learning have brought significant progress in visual
grounding tasks such as language-guided video object segmentation. However,
collecting large datasets for these tasks is expensive in terms of annotation
time, which represents a bottleneck. To this end, we propose a novel method,
namely SynthRef, for generating synthetic referring expressions for target
objects in an image (or video frame), and we also present and disseminate the
first large-scale dataset with synthetic referring expressions for video object
segmentation. Our experiments demonstrate that by training with our synthetic
referring expressions one can improve the ability of a model to generalize
across different datasets, without any additional annotation cost. Moreover,
our formulation allows its application to any object detection or segmentation
dataset.
