no code implementations • ECCV 2020 • Jian Hu, Hongya Tuo, Chao Wang, Lingfeng Qiao, Haowen Zhong, Junchi Yan, Zhongliang Jing, Henry Leung
Previous methods typically match the whole source domain to target domain, which causes negative transfer due to the source-negative classes in source domain that does not exist in target domain.
no code implementations • CVPR 2023 • Ye Liu, Lingfeng Qiao, Changchong Lu, Di Yin, Chen Lin, Haoyuan Peng, Bo Ren
An intuitive way to handle these two problems is to fulfill these tasks in two separate stages: aligning modalities followed by domain adaptation, or vice versa.
no code implementations • 14 Nov 2022 • Lingfeng Qiao, Chen Wu, Ye Liu, Haoyuan Peng, Di Yin, Bo Ren
In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model.
no code implementations • 10 Oct 2022 • Zhuoxuan Jiang, Lingfeng Qiao, Di Yin, Shanshan Feng, Bo Ren
Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale.
no code implementations • 4 Jul 2022 • Ye Liu, Lingfeng Qiao, Di Yin, Zhuoxuan Jiang, Xinghua Jiang, Deqiang Jiang, Bo Ren
In this paper, from an alternate perspective to overcome the above challenges, we unite these two tasks into one task by a new form of predicting shots link: a link connects two adjacent shots, indicating that they belong to the same scene or category.