9 citations · 18 across the 6 of their papers we have counts for
6 papers
Few-shot Class-incremental Learning for Classification and Object Detection: A Survey
Jinghua Zhang, Li Liu, Olli Silvén +2
Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in Machine Learning (ML), as it necessitates the Incremental Learning (IL) of new classes from sparsely labe…
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Huali Xu, Shuaifeng Zhi, Shuzhou Sun +2
While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this,…
Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters
Yawen Cui, Zitong Yu, Rizhao Cai +3
The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current F…
Rethinking Two Consensuses of the Transferability in Deep Learning
Yixiong Chen, Jingxian Li, Chris Ding +1
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is name…
Cross-Domain Few-Shot Classification via Inter-Source Stylization
Huali Xu, Shuaifeng Zhi, Li Liu
The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxi…
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Yawen Cui, Zitong Yu, Wei Peng +1
Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultan…