5 citations · 13 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation
Pascal Schlachter, Bin Yang
In real-world applications, there is often a domain shift from training to test data. This observation resulted in the development of test-time adaptation (TTA). It aims to adapt a…
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging
Karim Guirguis, Johannes Meier, George Eskandar +3
Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neura…
LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking
Zhihua Liu, Bin Yang, Yan Shen +2
Accurate tracking of an anatomical landmark over time has been of high interests for disease assessment such as minimally invasive surgery and tumor radiation therapy. Ultrasound i…
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…