title: To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
summary: The goal of Online Domain Adaptation for semantic segmentation is to handle
  unforeseeable domain changes that occur during deployment, like sudden weather
  events. However, the high computational costs associated with brute-force
  adaptation make this paradigm unfeasible for real-world applications. In this
  paper we propose HAMLET, a Hardware-Aware Modular Least Expensive Training
  framework for real-time domain adaptation. Our approach includes a
  hardware-aware back-propagation orchestration agent (HAMT) and a dedicated
  domain-shift detector that enables active control over when and how the model
  is adapted (LT). Thanks to these advancements, our approach is capable of
  performing semantic segmentation while simultaneously adapting at more than
  29FPS on a single consumer-grade GPU. Our framework's encouraging accuracy and
  speed trade-off is demonstrated on OnDA and SHIFT benchmarks through
  experimental results.
paper_authors._id: 64c375cc29dd19f87e4cbae1
paper_authors.name: Marc Botet Colomer
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-28T16:16:49.443Z
paper_authors._id: 64c3d659ed521f27a4e4a3cd
paper_authors.avatarUrl: /avatars/7ef00e391d61bb25234c7f304501b844.svg
paper_authors.fullname: Marc Botet Colomer
paper_authors.type: user
paper_authors.user: MarcBotet
paper_authors._id: 64c375cc29dd19f87e4cbae2
paper_authors.name: Pier Luigi Dovesi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-28T09:45:01.657Z
paper_authors._id: 6272b397d9ce7af3f4f95938
paper_authors.avatarUrl: /avatars/1163a7d4ffcfa1cf4ffb6e01476b85df.svg
paper_authors.fullname: Pier Luigi Dovesi
paper_authors.type: user
paper_authors.user: dovpie
paper_authors._id: 64c375cc29dd19f87e4cbae3
paper_authors.name: Theodoros Panagiotakopoulos
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-28T16:16:45.264Z
paper_authors._id: 64c3e47ee77ea9f2811523f4
paper_authors.avatarUrl: /avatars/204fbd8e72f6208da56934cc64cee3a4.svg
paper_authors.fullname: Theodoros Panagiotakopoulos
paper_authors.type: user
paper_authors.user: theo2021
paper_authors._id: 64c375cc29dd19f87e4cbae4
paper_authors.name: Joao Frederico Carvalho
paper_authors._id: 64c375cc29dd19f87e4cbae5
paper_authors.name: Linus Härenstam-Nielsen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-28T09:48:07.085Z
paper_authors._id: 645cf26ace72244df7b11787
paper_authors.avatarUrl: /avatars/98daec0b19a45ebe5ac7798dd48ab7c9.svg
paper_authors.fullname: Linus Härenstam-Nielsen
paper_authors.type: user
paper_authors.user: Linusnie
paper_authors._id: 64c375cc29dd19f87e4cbae6
paper_authors.name: Hossein Azizpour
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-28T09:48:18.199Z
paper_authors._id: 63da8ddef03c3d71ef32656a
paper_authors.avatarUrl: /avatars/7c2ceb4908f102f02f17fe459c90d892.svg
paper_authors.fullname: Hossein Azizpour
paper_authors.type: user
paper_authors.user: azizpour
paper_authors._id: 64c375cc29dd19f87e4cbae7
paper_authors.name: Hedvig Kjellström
paper_authors._id: 64c375cc29dd19f87e4cbae8
paper_authors.name: Daniel Cremers
paper_authors._id: 64c375cc29dd19f87e4cbae9
paper_authors.name: Matteo Poggi
paper_title: To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
paper_summary: The goal of Online Domain Adaptation for semantic segmentation is to handle
  unforeseeable domain changes that occur during deployment, like sudden weather
  events. However, the high computational costs associated with brute-force
  adaptation make this paradigm unfeasible for real-world applications. In this
  paper we propose HAMLET, a Hardware-Aware Modular Least Expensive Training
  framework for real-time domain adaptation. Our approach includes a
  hardware-aware back-propagation orchestration agent (HAMT) and a dedicated
  domain-shift detector that enables active control over when and how the model
  is adapted (LT). Thanks to these advancements, our approach is capable of
  performing semantic segmentation while simultaneously adapting at more than
  29FPS on a single consumer-grade GPU. Our framework's encouraging accuracy and
  speed trade-off is demonstrated on OnDA and SHIFT benchmarks through
  experimental results.
paper_ai_keywords: semantic segmentation domain adaptation HAMLET Hardware-Aware Modular Least Expensive Training hardware-aware back-propagation orchestration agent domain-shift detector real-time domain adaptation GPU OnDA benchmarks SHIFT benchmarks