  Zero-shot classification capabilities naturally arise in models trained
within a vision-language contrastive framework. Despite their classification
prowess, these models struggle in dense tasks like zero-shot open-vocabulary
segmentation. This deficiency is often attributed to the absence of
localization cues in captions and the intertwined nature of the learning
process, which encompasses both image representation learning and
cross-modality alignment. To tackle these issues, we propose SimZSS, a Simple
framework for open-vocabulary Zero-Shot Segmentation. The method is founded on
two key principles: i) leveraging frozen vision-only models that exhibit
spatial awareness while exclusively aligning the text encoder and ii)
exploiting the discrete nature of text and linguistic knowledge to pinpoint
local concepts within captions. By capitalizing on the quality of the visual
representations, our method requires only image-caption pairs datasets and
adapts to both small curated and large-scale noisy datasets. When trained on
COCO Captions across 8 GPUs, SimZSS achieves state-of-the-art results on 7 out
of 8 benchmark datasets in less than 15 minutes.
