37 citations · 43 across the 3 of their papers we have counts for
4 papers
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Yixiong Zou, Shanghang Zhang, Ke Chen +3
Few-shot learning (FSL) aims at recognizing novel classes given only few training samples, which still remains a great challenge for deep learning. However, humans can easily recog…
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
Limeng Qiao, Yemin Shi, Jia Li +3
Few-shot learning, which aims at extracting new concepts rapidly from extremely few examples of novel classes, has been featured into the meta-learning paradigm recently. Yet, the…
P-ODN: Prototype based Open Deep Network for Open Set Recognition
Yu Shu, Yemin Shi, Yaowei Wang +2
Most of the existing recognition algorithms are proposed for closed set scenarios, where all categories are known beforehand. However, in practice, recognition is essentially an op…
ODN: Opening the Deep Network for Open-set Action Recognition
Yu Shu, Yemin Shi, Yaowei Wang +3
In recent years, the performance of action recognition has been significantly improved with the help of deep neural networks. Most of the existing action recognition works hold the…