title: Semantic Library Adaptation: LoRA Retrieval and Fusion for
    Open-Vocabulary Semantic Segmentation
summary: Open-vocabulary semantic segmentation models associate vision and text to
  label pixels from an undefined set of classes using textual queries, providing
  versatile performance on novel datasets. However, large shifts between training
  and test domains degrade their performance, requiring fine-tuning for effective
  real-world applications. We introduce Semantic Library Adaptation (SemLA), a
  novel framework for training-free, test-time domain adaptation. SemLA leverages
  a library of LoRA-based adapters indexed with CLIP embeddings, dynamically
  merging the most relevant adapters based on proximity to the target domain in
  the embedding space. This approach constructs an ad-hoc model tailored to each
  specific input without additional training. Our method scales efficiently,
  enhances explainability by tracking adapter contributions, and inherently
  protects data privacy, making it ideal for sensitive applications.
  Comprehensive experiments on a 20-domain benchmark built over 10 standard
  datasets demonstrate SemLA's superior adaptability and performance across
  diverse settings, establishing a new standard in domain adaptation for
  open-vocabulary semantic segmentation.
paper_authors._id: 67e6bcb5b07543ff130223c9
paper_authors.name: Reza Qorbani
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paper_authors.fullname: Reza Qorbani
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paper_authors.name: Gianluca Villani
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paper_authors.fullname: Gianluca Villani
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paper_authors.name: Theodoros Panagiotakopoulos
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paper_authors.fullname: Theodoros Panagiotakopoulos
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paper_authors.user: theo2021
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paper_authors.name: Marc Botet Colomer
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paper_authors.fullname: Marc Botet Colomer
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paper_authors.user: MarcBotet
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paper_authors.name: Linus Härenstam-Nielsen
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paper_authors.fullname: Linus Härenstam-Nielsen
paper_authors.type: user
paper_authors.user: Linusnie
paper_authors._id: 67e6bcb5b07543ff130223ce
paper_authors.name: Mattia Segu
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paper_authors.avatarUrl: /avatars/da260d4d0f013404cbdb6aaf6929c871.svg
paper_authors.fullname: Mattia Segu
paper_authors.type: user
paper_authors.user: mattiasegu
paper_authors._id: 67e6bcb5b07543ff130223cf
paper_authors.name: Pier Luigi Dovesi
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paper_authors.avatarUrl: /avatars/1163a7d4ffcfa1cf4ffb6e01476b85df.svg
paper_authors.fullname: Pier Luigi Dovesi
paper_authors.type: user
paper_authors.user: dovpie
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paper_authors.name: Jussi Karlgren
paper_authors.status: admin_assigned
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paper_authors.fullname: Jussi  Karlgren
paper_authors.type: user
paper_authors.user: jussikarlgren
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paper_authors.name: Daniel Cremers
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paper_authors.name: Federico Tombari
paper_authors._id: 67e6bcb5b07543ff130223d3
paper_authors.name: Matteo Poggi
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paper_authors.fullname: Matteo Poggi
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paper_title: Semantic Library Adaptation: LoRA Retrieval and Fusion for
    Open-Vocabulary Semantic Segmentation
paper_summary: Open-vocabulary semantic segmentation models associate vision and text to
  label pixels from an undefined set of classes using textual queries, providing
  versatile performance on novel datasets. However, large shifts between training
  and test domains degrade their performance, requiring fine-tuning for effective
  real-world applications. We introduce Semantic Library Adaptation (SemLA), a
  novel framework for training-free, test-time domain adaptation. SemLA leverages
  a library of LoRA-based adapters indexed with CLIP embeddings, dynamically
  merging the most relevant adapters based on proximity to the target domain in
  the embedding space. This approach constructs an ad-hoc model tailored to each
  specific input without additional training. Our method scales efficiently,
  enhances explainability by tracking adapter contributions, and inherently
  protects data privacy, making it ideal for sensitive applications.
  Comprehensive experiments on a 20-domain benchmark built over 10 standard
  datasets demonstrate SemLA's superior adaptability and performance across
  diverse settings, establishing a new standard in domain adaptation for
  open-vocabulary semantic segmentation.
paper_ai_summary: Semantic Library Adaptation (SemLA) enhances open-vocabulary semantic segmentation with a train-free approach using LoRA adapters indexed by CLIP embeddings for real-world applications across various domains.
paper_ai_keywords: open-vocabulary semantic segmentation LoRA-based adapters CLIP embeddings domain adaptation semantic segmentation SemLA train-free adaptation model adaptation test-time adaptation adapter contributions data privacy