  Recently, great success has been made in learning visual representations from
text supervision, facilitating the emergence of text-supervised semantic
segmentation. However, existing works focus on pixel grouping and cross-modal
semantic alignment, while ignoring the correspondence among multiple augmented
views of the same image. To overcome such limitation, we propose
multi-\textbf{View} \textbf{Co}nsistent learning (ViewCo) for text-supervised
semantic segmentation. Specifically, we first propose text-to-views consistency
modeling to learn correspondence for multiple views of the same input image.
Additionally, we propose cross-view segmentation consistency modeling to
address the ambiguity issue of text supervision by contrasting the segment
features of Siamese visual encoders. The text-to-views consistency benefits the
dense assignment of the visual features by encouraging different crops to align
with the same text, while the cross-view segmentation consistency modeling
provides additional self-supervision, overcoming the limitation of ambiguous
text supervision for segmentation masks. Trained with large-scale image-text
data, our model can directly segment objects of arbitrary categories in a
zero-shot manner. Extensive experiments show that ViewCo outperforms
state-of-the-art methods on average by up to 2.9\%, 1.6\%, and 2.4\% mIoU on
PASCAL VOC2012, PASCAL Context, and COCO, respectively.
