3 papers
cs.CV2021
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
Binghui Chen, Zhaoyi Yan, Ke Li +4
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity…
cs.CV2020
Continual Local Replacement for Few-shot Learning
Canyu Le, Zhonggui Chen, Xihan Wei +2
The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good…
cs.LG2019
Learning Continually from Low-shot Data Stream
Canyu Le, Xihan Wei, Biao Wang +2
While deep learning has achieved remarkable results on various applications, it is usually data hungry and struggles to learn over non-stationary data stream. To solve these two li…