Sign inGet started
Paper · arXiv

Back to Source: Open-Set Continual Test-Time Adaptation via Domain Compensation

Test-Time Adaptation (TTA) aims to mitigate distributional shifts between training and test domains during inference time. However, existing TTA methods fall short in the realistic scenario where models face both continually changing domains and the simultaneous emergence of unknown semantic classes, a challenging setting we term Open-set Continual Test-Time Adaptation (OCTTA). The coupling of domain and semantic shifts often collapses the feature space, severely degrading both classification and out-of-distribution detection. To tackle this, we propose DOmain COmpensation (DOCO), a lightweight and effective framework that robustly performs domain adaptation and OOD detection in a synergi...

AuthorsYingkai Yang, Chaoqi Chen, Hui Huang
Primary categorycs.CV
Published23 April 2026
Last updated at source23 April 2026
cs.CV
Embed this entry
<iframe src="https://silytics.si/embed/item/9e136b35-f11a-455c-a85d-a08a41e1ac32" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Connected in the knowledge graph

Explore this entry in the graph

Models and datasets referencing this paper

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.