no code implementations • 27 Feb 2024 • Rui Cheng, Wanqing Cui
Existing two-stream models for image-text matching show good performance while ensuring retrieval speed and have received extensive attention from industry and academia.
no code implementations • 21 Feb 2024 • Wanqing Cui, Keping Bi, Jiafeng Guo, Xueqi Cheng
Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge.
2 code implementations • 11 Mar 2021 • Yuqi Huo, Manli Zhang, Guangzhen Liu, Haoyu Lu, Yizhao Gao, Guoxing Yang, Jingyuan Wen, Heng Zhang, Baogui Xu, Weihao Zheng, Zongzheng Xi, Yueqian Yang, Anwen Hu, Jinming Zhao, Ruichen Li, Yida Zhao, Liang Zhang, Yuqing Song, Xin Hong, Wanqing Cui, Danyang Hou, Yingyan Li, Junyi Li, Peiyu Liu, Zheng Gong, Chuhao Jin, Yuchong Sun, ShiZhe Chen, Zhiwu Lu, Zhicheng Dou, Qin Jin, Yanyan Lan, Wayne Xin Zhao, Ruihua Song, Ji-Rong Wen
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on Image Retrieval on RUC-CAS-WenLan
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Wanqing Cui, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng
This paper proposes a novel approach to learn commonsense from images, instead of limited raw texts or costly constructed knowledge bases, for the commonsense reasoning problem in NLP.