no code implementations • 17 Jun 2022 • Xiao Dong, Xunlin Zhan, Yunchao Wei, XiaoYong Wei, YaoWei Wang, Minlong Lu, Xiaochun Cao, Xiaodan Liang
Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.
no code implementations • 17 Mar 2022 • Xunlin Zhan, Yuan Li, Xiao Dong, Xiaodan Liang, Zhiting Hu, Lawrence Carin
Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context.
no code implementations • CVPR 2022 • Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C. Kampffmeyer, XiaoYong Wei, Minlong Lu, YaoWei Wang, Xiaodan Liang
Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets.
1 code implementation • ICCV 2021 • Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, Xiaodan Liang
In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.
no code implementations • 24 Dec 2020 • Yinya Huang, Meng Fang, Xunlin Zhan, Qingxing Cao, Xiaodan Liang, Liang Lin
It is crucial since the quality of the evidence is the key to answering commonsense questions, and even determines the upper bound on the QA systems performance.