8 citations · 17 across the 6 of their papers we have counts for
6 papers
Multi-Level Correlation Network For Few-Shot Image Classification
Yunkai Dang, Min Zhang, Zhengyu Chen +4
Few-shot image classification(FSIC) aims to recognize novel classes given few labeled images from base classes. Recent works have achieved promising classification performance, esp…
Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse
Didi Zhu, Zexi Li, Min Zhang +6
Large-scale vision-language (V-L) models have demonstrated remarkable generalization capabilities for downstream tasks through prompt tuning. However, the mechanisms behind the lea…
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization
Yunze Tong, Junkun Yuan, Min Zhang +4
Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source doma…
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning
Min Zhang, Zifeng Zhuang, Zhitao Wang +2
Gradient-based meta-learning (GBML) algorithms are able to fast adapt to new tasks by transferring the learned meta-knowledge, while assuming that all tasks come from the same dist…
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting
Linzhi Wu, Pengjun Xie, Jie Zhou +4
Self-augmentation has received increasing research interest recently to improve named entity recognition (NER) performance in low-resource scenarios. Token substitution and mixup a…
Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition
Siteng Huang, Min Zhang, Yachen Kang +1
The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recen…