1 citations · 1 across the 2 of their papers we have counts for
5 papers
Modular Adaptation for Cross-Domain Few-Shot Learning
Xiao Lin, Meng Ye, Yunye Gong +4
Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples. While literature has demonstrated great successes via repr…
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning
Meng Ye, Xiao Lin, Giedrius Burachas +2
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes. Recent FSL works have made steady progress via metric learning, meta learning, repr…
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
Meng Ye, Yuhong Guo
Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot lear…
Progressive Ensemble Networks for Zero-Shot Recognition
Meng Ye, Yuhong Guo
Despite the advancement of supervised image recognition algorithms, their dependence on the availability of labeled data and the rapid expansion of image categories raise the signi…
Deep Triplet Ranking Networks for One-Shot Recognition
Meng Ye, Yuhong Guo
Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevent…