1 code implementation • COLING 2022 • Yiming Wang, Qianren Mao, Junnan Liu, Weifeng Jiang, Hongdong Zhu, JianXin Li
Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications.
no code implementations • 27 Jul 2021 • Jinchao Zhu, XiaoYu Zhang, Xian Fang, Feng Dong, Li Yuehua, Junnan Liu
Effective fusion of different types of features is the key to salient object detection.
no code implementations • 28 May 2021 • Junnan Liu, Qianren Mao, Bang Liu, Hao Peng, Hongdong Zhu, JianXin Li
In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus.