Region Graph Embedding Network for Zero-Shot Learning

By Guosen Xie, Li Liu, Fan Zhu, Fang Zhao, Zheng Zhang, Yazhou Yao, Jie Qin, Ling Shao. 2020

179 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luc Van Gool
2. Yin Cui
3. Weicheng Kuo
4. Xiuye Gu
5. Wenguan Wang
6. Tsung-Yi Lin
7. Jian Yang
8. Mohammad Rostami

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jianbing Shen
3. Wenguan Wang
4. Ming-Hsuan Yang
5. Ling Shao
6. Chen Change Loy
7. Lei Zhang
8. Dacheng Tao

In Radiology, Nuclear Medicine and Imaging

1. Yin Cui
2. Xiuye Gu
3. Zongyan Han
4. Mohammad Rostami
5. Jianbing Shen
6. Shuo Chen
7. Luc Van Gool
8. Wenguan Wang

Citations in the corpus

• Detection and Continual Learning of Novel Face Presentation Attacks (2021, background): “The learning setting that we explore is more related to the zero-shot learning (ZSL) formulation [9, 37, 38, 25].”
• Open-vocabulary Object Detection via Vision and Language Knowledge Distillation (2021, background): “…direction, researchers have also explored class hierarchy, class similarity, and object parts as discriminative features to aid the knowledge transfer (Rohrbach et al., 2011; Akata et al., 2016; Zhao et al., 2017; Elhoseiny et al., 2017; Ji et al., 2018; Cacheux et al., 2019; Xie et al., 2020).”
• Zero-Shot Detection via Vision and Language Knowledge Distillation (2021, background): “Researchers have also explored class hierarchy, class similarity, and object parts as discriminative features to aid the knowledge transfer [38, 1, 8, 23, 4, 44].”
• Video Object Segmentation with Episodic Graph Memory Networks (2020, background): “mory networks augment neural networks with an external memory component[28,43,58], which allow the network to explicitly access the past experiences. They have been shown eective in few-shot learning[39,62,63] and object tracking [67]. Recently, episodic external memory networks have been explored t”
• Contrastive Embedding for Generalized Zero-Shot Learning (2021, methodology): “To relieve the overfitting problem, some methods [10, 44, 3, 29, 73, 74, 49] have designed new loss functions to balance the predictions between seen and unseen classes.”
• A Review of Generalized Zero-Shot Learning Methods (2020, methodology): “The studies in [45], [90]–[92] exploit the graph convolutional network (GCN) [93] to transfer knowledge among different categories.”