To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
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.
64c375cc29dd19f87e4cbae1
Marc Botet Colomer
claimed_verified
2023-07-28T16:16:49.443Z
64c3d659ed521f27a4e4a3cd
/avatars/7ef00e391d61bb25234c7f304501b844.svg
Marc Botet Colomer
user
MarcBotet
64c375cc29dd19f87e4cbae2
Pier Luigi Dovesi
admin_assigned
2023-07-28T09:45:01.657Z
6272b397d9ce7af3f4f95938
/avatars/1163a7d4ffcfa1cf4ffb6e01476b85df.svg
Pier Luigi Dovesi
user
dovpie
64c375cc29dd19f87e4cbae3
Theodoros Panagiotakopoulos
claimed_verified
2023-07-28T16:16:45.264Z
64c3e47ee77ea9f2811523f4
/avatars/204fbd8e72f6208da56934cc64cee3a4.svg
Theodoros Panagiotakopoulos
user
theo2021
64c375cc29dd19f87e4cbae4
Joao Frederico Carvalho
64c375cc29dd19f87e4cbae5
Linus Härenstam-Nielsen
admin_assigned
2023-07-28T09:48:07.085Z
645cf26ace72244df7b11787
/avatars/98daec0b19a45ebe5ac7798dd48ab7c9.svg
Linus Härenstam-Nielsen
user
Linusnie
64c375cc29dd19f87e4cbae6
Hossein Azizpour
admin_assigned
2023-07-28T09:48:18.199Z
63da8ddef03c3d71ef32656a
/avatars/7c2ceb4908f102f02f17fe459c90d892.svg
Hossein Azizpour
user
azizpour
64c375cc29dd19f87e4cbae7
Hedvig Kjellström
64c375cc29dd19f87e4cbae8
Daniel Cremers
64c375cc29dd19f87e4cbae9
Matteo Poggi
To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
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.
HAMLET, a hardware-aware modular framework, enables real-time domain adaptation for semantic segmentation at high frame rates with low computational cost.
Online Domain Adaptation semantic segmentation hardware-aware back-propagation domain-shift detector FPS