77 citations · 353 across the 60 of their papers we have counts for
4 papers · 1 filter
Modeling Multi-modal Cross-interaction for Multi-label Few-shot Image Classification Based on Local Feature Selection
Kun Yan, Zied Bouraoui, Fangyun Wei +4
The aim of multi-label few-shot image classification (ML-FSIC) is to assign semantic labels to images, in settings where only a small number of training examples are available for…
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention
Kun Yan, Chenbin Zhang, Jun Hou +4
Multi-label few-shot image classification (ML-FSIC) is the task of assigning descriptive labels to previously unseen images, based on a small number of training examples. A key fea…
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning
Kun Yan, Zied Bouraoui, Ping Wang +2
Few-shot learning (FSL) is the task of learning to recognize previously unseen categories of images from a small number of training examples. This is a challenging task, as the ava…
Few-shot Image Classification with Multi-Facet Prototypes
Kun Yan, Zied Bouraoui, Ping Wang +2
The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples. A central challenge is that the available training exampl…