Interestingness Prediction by Robust Learning to Rank
By Yanwei Fu, Timothy M. Hospedales, Tao A. Xiang, Shaogang Gong, Yuan Yao. 2014
91 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Tao A. Xiang
- Shaogang Gong
- Yanwei Fu
- Timothy M. Hospedales
- Zhenyong Fu
- Zeynep Akata
- Wei-Lun Chao
- Fei Sha
In Computer Vision and Pattern Recognition
- Tao A. Xiang
- Shaogang Gong
- Timothy M. Hospedales
- Yanwei Fu
- Xiaogang Wang
- Luc Van Gool
- Yi Yang
- Bernt Schiele
Citations in the corpus
- Transductive Multi-View Zero-Shot Learning (2015, methodology): “Zero-shot learning Multi-class single label zero-shot learning has now been widely studied using attribute-based intermediate semantic layers [3, 6, 10, 11, 19, 25] or data-driven [8, 9, 21, 30] representations.”
- Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (2020, background): “formulated interestingness as a problem of unified learning to rank, which is able to jointly identify human annotation outliers [11,12].”
- Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation (2014, background): “Attribute-centric models have been explored for images [18, 6, 12, 31] and to a lesser extent videos [8, 11, 21].”