202 citations · 266 across the 23 of their papers we have counts for
5 papers · 1 filter
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition
Kai Wang, Xialei Liu, Andy Bagdanov +3
Most meta-learning approaches assume the existence of a very large set of labeled data available for episodic meta-learning of base knowledge. This contrasts with the more realisti…
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Wei-Hong Li, Xialei Liu, Hakan Bilen
Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient appro…
HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification
Kai Wang, Xialei Liu, Luis Herranz +1
Human beings learn and accumulate hierarchical knowledge over their lifetime. This knowledge is associated with previous concepts for consolidation and hierarchical construction. H…
Cross-domain Few-shot Learning with Task-specific Adapters
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. R…
Universal Representation Learning from Multiple Domains for Few-shot Classification
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods…