Annotation Free Semantic Segmentation with Vision Foundation Models

CoRR(2024)

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Abstract
Semantic Segmentation is one of the most challenging vision tasks, usually requiring large amounts of training data with expensive pixel-level annotations. With the success of foundation models and especially vision-language models, recent works attempt to achieve zero-shot semantic segmentation while requiring either large scale training or additional image/pixel-level annotations. In this work, we build a lightweight module on top of a self-supervised pretrained vision encoder to align patch features with a pre-trained text encoder. Importantly, we generate free annotations for any semantic segmentation dataset using existing foundation models and train our alignment module cost free. We use CLIP to detect objects and SAM to generate high quality object masks. Our approach can bring language-based semantics to any pre-trained vision encoder with minimal training. Our module is lightweight, uses foundation models as a sole source of supervision and shows impressive generalization capability from little training data with no annotation.
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