2 code implementations • 23 Nov 2022 • Sifeng He, Yue He, Minlong Lu, Chen Jiang, Xudong Yang, Feng Qian, Xiaobo Zhang, Lei Yang, Jiandong Zhang
Previous methods typically start from frame-to-frame similarity matrix generated by cosine similarity between frame-level features of the input video pair, and then detect and refine the boundaries of copied segments on similarity matrix under temporal constraints.
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 • 12 May 2022 • Tianshu Wang, Hongyu Lin, Cheng Fu, Xianpei Han, Le Sun, Feiyu Xiong, Hui Chen, Minlong Lu, Xiuwen Zhu
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
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.