|
Weakly-Supervised Domain Adaptive Semantic Segmentation with Prototypical Contrastive Learning
Anurag Das,
Yongqin Xian,
Dengxin Dai,
Bernt Schiele
CVPR, 2023
We propose a common framework to use different weak labels, e.g., image, point and coarse labels from the target domain to reduce the performance gap between UDA and supervised learning.
|
|
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation
Anurag Das,
Yongqin Xian,
Yang He,
Zeynep Akata,
Bernt Schiele
WACV, 2023
We propose to utlize cheaper coarse annotations for urban scene semantic segmentation. Coarse annotation lacks fine Boundary
details and are faster to annotate. Our proposed method obtains competitive performance with coarse annotation along with relatively
free synthetic data compared with fine annotation at a fraction of the annotation budget.
|
|
(SP)2Net for Generalized Zero-Label Semantic
Segmentation
Anurag Das,
Yongqin Xian,
Yang He,
Zeynep Akata,
Bernt Schiele
GCPR, 2021 [ Best Master Thesis Award in Germany]
Generalized Zero-shot Semantic Segmentation(GZSS) is a challenging problem as the prior works don't generalize well on unseen classes.
we propose to leverage a class-agnostic segmentation prior provided by superpixels and introduce a superpixel pooling (SP-pooling) module
that improves the performance on GZSS task.
|
|