202 citations · 292 across the 17 of their papers we have counts for
52 papers
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
Dipam Goswami, René Schuster, Joost van de Weijer +1
In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift. Although…
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Kai Wang, Chenshen Wu, Andy Bagdanov +4
Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and tha…
Attention Distillation: self-supervised vision transformer students need more guidance
Kai Wang, Fei Yang, Joost van de Weijer
Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is there…
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation
Aitor Alvarez-Gila, Joost van de Weijer, Yaxing Wang +1
We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera loca…
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
Francesco Pelosin, Saurav Jha, Andrea Torsello +2
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the k…
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…