15 citations · 34 across the 6 of their papers we have counts for
6 papers
Continually Learning Self-Supervised Representations with Projected Functional Regularization
Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu +2
Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable t…
Self-Training for Class-Incremental Semantic Segmentation
Lu Yu, Xialei Liu, Joost van de Weijer
In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer…
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
Yaxing Wang, Lu Yu, Joost van de Weijer
Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require lar…
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…
Learning Metrics from Teachers: Compact Networks for Image Embedding
Lu Yu, Vacit Oguz Yazici, Xialei Liu +3
Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to…
Weakly Supervised Domain-Specific Color Naming Based on Attention
Lu Yu, Yongmei Cheng, Joost van de Weijer
The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used…