69 citations · 192 across the 19 of their papers we have counts for
5 papers · 1 filter
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation
Longhui Yuan, Shuang Li, Zhuo He +1
Test-time adaptation (TTA) adapts the pre-trained models during inference using unlabeled test data and has received a lot of research attention due to its potential practical valu…
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
Binhui Xie, Shuang Li, Qingju Guo +2
Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges ste…
Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios
Shuang Li, Longhui Yuan, Binhui Xie +1
Test-time adaptation (TTA) adapts the pre-trained models to test distributions during the inference phase exclusively employing unlabeled test data streams, which holds great value…
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Dina Bashkirova, Samarth Mishra, Diala Lteif +16
Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sortin…
Robust Test-Time Adaptation in Dynamic Scenarios
Longhui Yuan, Binhui Xie, Shuang Li
Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams. Most of the previous TTA methods have achieved great s…