Previous studies mainly focus on the scenario of a single category label per example but have not solved the more challenging multi-label scenario with exponential-sized output space and low-data effectively. In this paper, we propose a semantic-aware meta-learning model for multi-label few-shot...
论文名称:Few-shot Learning for Multi-label Intent Detection 推荐一篇来自哈尔滨工业大学赛尔实验室刘挺教授组工作,此项工作研究了用于用户意图检测的少样本多标签分类方法。刘挺教授现任哈工大计算学部主任...
特征增广 LaSO: Label-Set Operations networks for multi-label few-shot learning motivation:任务:多标签的小样本分类任务。对于两个多标签的图像,将其进行交/并/差可以得到新的图像和对应的类别,从而扩充了图像集合。 方法:在特征层面上进行样本的扩充,对于两个样本特征F1和F2,经过三个网络(M-int;M-unit;M-...
However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD task since a sentence may contain multiple aspect categories. Therefore, we propose a multi-label few-shot learning method based on the prototypical network. To ...
Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, and Alex M Bronstein.Laso: Label-set operations networks for multi-label few-shot learning.In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6548–6557...
从已有方法可以看出，NLP解决Few-Shot Learning问题的有效方法就是，引入大规模外部知识或数据，因此无标注...
【9】Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces.【10】Few-Shot Text...
LaSO: Label-Set Operations networks for multi-label few-shot learning motivation:任务:多标签的小样本分类任务。对于两个多标签的图像,将其进行交/并/差可以得到新的图像和对应的类别,从而扩充了图像集合。 方法:在特征层面上进行样本的扩充,对于两个样本特征F1和F2,经过三个网络(M-int;M-unit;M-sub),合成...
This repository contains the implementation of "LaSO: Label-Set Operations networks for multi-label few-shot learning" by Alfassy et al. It was posted on arxiv in Feb 2019 and will be presented in CVPR 2019. In this paper we have presented the label set manipulation concept and have demons...
【27】Knowledge-guided multi-label few-shot learning for general image recognition; 【28】MELR: Meta-learning via modeling episode-level relationships for few-shot learning; 【29】Rethinking few-shot image classification: a good embedding is all you need?