PandA: Unsupervised learning of parts and appearances in the feature maps of GANs
We propose an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion.
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning
Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting.
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
we study the task of synthetic-to-real domain generalized semantic segmentation, which aims to learn a model that is robust to unseen real-world scenes using only synthetic data.
Augmentation-free unsupervised approach for point clouds
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
Novel Class Discovery in Semantic Segmentation (NCDSS)
We introduce a new setting of Novel Class Discovery in Semantic Segmentation (NCDSS), which aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes.