  Visual grounding localizes regions (boxes or segments) in the image
corresponding to given referring expressions. In this work we address image
segmentation from referring expressions, a problem that has so far only been
addressed in a fully-supervised setting. A fully-supervised setup, however,
requires pixel-wise supervision and is hard to scale given the expense of
manual annotation. We therefore introduce a new task of weakly-supervised image
segmentation from referring expressions and propose Text grounded semantic
SEGgmentation (TSEG) that learns segmentation masks directly from image-level
referring expressions without pixel-level annotations. Our transformer-based
method computes patch-text similarities and guides the classification objective
during training with a new multi-label patch assignment mechanism. The
resulting visual grounding model segments image regions corresponding to given
natural language expressions. Our approach TSEG demonstrates promising results
for weakly-supervised referring expression segmentation on the challenging
PhraseCut and RefCOCO datasets. TSEG also shows competitive performance when
evaluated in a zero-shot setting for semantic segmentation on Pascal VOC.
