  We present LSeg, a novel model for language-driven semantic image
segmentation. LSeg uses a text encoder to compute embeddings of descriptive
input labels (e.g., "grass" or "building") together with a transformer-based
image encoder that computes dense per-pixel embeddings of the input image. The
image encoder is trained with a contrastive objective to align pixel embeddings
to the text embedding of the corresponding semantic class. The text embeddings
provide a flexible label representation in which semantically similar labels
map to similar regions in the embedding space (e.g., "cat" and "furry"). This
allows LSeg to generalize to previously unseen categories at test time, without
retraining or even requiring a single additional training sample. We
demonstrate that our approach achieves highly competitive zero-shot performance
compared to existing zero- and few-shot semantic segmentation methods, and even
matches the accuracy of traditional segmentation algorithms when a fixed label
set is provided. Code and demo are available at
https://github.com/isl-org/lang-seg.
