202 citations · 257 across the 9 of their papers we have counts for
12 papers
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…
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…
Learning to Rank for Active Learning: A Listwise Approach
Minghan Li, Xialei Liu, Joost van de Weijer +1
Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous…
Generative Feature Replay For Class-Incremental Learning
Xialei Liu, Chenshen Wu, Mikel Menta +5
Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We co…
Semantic Drift Compensation for Class-Incremental Learning
Lu Yu, Bartłomiej Twardowski, Xialei Liu +5
Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a tim…
Multi-Task Incremental Learning for Object Detection
Xialei Liu, Hao Yang, Avinash Ravichandran +2
Multi-task learns multiple tasks, while sharing knowledge and computation among them. However, it suffers from catastrophic forgetting of previous knowledge when learned incrementa…