Language-driven Semantic SegmentationDownload PDF

29 Sept 2021, 00:31 (edited 23 Feb 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: language-driven, semantic segmentation, zero-shot, transformer
  • Abstract: 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
  • One-sentence Summary: We present a language-driven approach that enables synthesis of zero-shot semantic segmentation models from arbitrary label sets at test time.
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