6 citations · 10 across the 8 of their papers we have counts for
8 papers
A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models
Mario Döbler, Robert A. Marsden, Tobias Raichle +1
In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models…
Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation
Mario Döbler, Florian Marencke, Robert A. Marsden +1
Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update…
Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction
Robert A. Marsden, Mario Döbler, Bin Yang
Since distribution shifts are likely to occur during test-time and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model…
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation
Mario Döbler, Robert A. Marsden, Bin Yang
Since experiencing domain shifts during test-time is inevitable in practice, test-time adaption (TTA) continues to adapt the model after deployment. Recently, the area of continual…
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer
Robert A. Marsden, Felix Wiewel, Mario Döbler +2
In recent years, there has been tremendous progress in the field of semantic segmentation. However, one remaining challenging problem is that segmentation models do not generalize…
Introducing Intermediate Domains for Effective Self-Training during Test-Time
Robert A. Marsden, Mario Döbler, Bin Yang
Experiencing domain shifts during test-time is nearly inevitable in practice and likely results in a severe performance degradation. To overcome this issue, test-time adaptation co…
An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions
George Eskandar, Robert A. Marsden, Pavithran Pandiyan +3
Integrating different representations from complementary sensing modalities is crucial for robust scene interpretation in autonomous driving. While deep learning architectures that…
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation
Robert A. Marsden, Alexander Bartler, Mario Döbler +1
Deep convolutional neural networks have considerably improved state-of-the-art results for semantic segmentation. Nevertheless, even modern architectures lack the ability to genera…