no code implementations • CVPR 2023 • Hongwei Xue, Peng Gao, Hongyang Li, Yu Qiao, Hao Sun, Houqiang Li, Jiebo Luo
However, unlike the low-level features such as pixel values, we argue the features extracted by powerful teacher models already encode rich semantic correlation across regions in an intact image. This raises one question: is reconstruction necessary in Masked Image Modeling (MIM) with a teacher model?
1 code implementation • 12 Oct 2022 • Yuchong Sun, Hongwei Xue, Ruihua Song, Bei Liu, Huan Yang, Jianlong Fu
Large-scale video-language pre-training has shown significant improvement in video-language understanding tasks.
Ranked #2 on Video Retrieval on QuerYD (using extra training data)
1 code implementation • 14 Sep 2022 • Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, Jiebo Luo
and 2) how to mitigate the impact of these factors?
Ranked #2 on Video Retrieval on MSR-VTT-1kA (using extra training data)
1 code implementation • CVPR 2022 • Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, Baining Guo
To enable VL pre-training, we jointly optimize the HD-VILA model by a hybrid Transformer that learns rich spatiotemporal features, and a multimodal Transformer that enforces interactions of the learned video features with diversified texts.
Ranked #16 on Video Retrieval on MSR-VTT
1 code implementation • 19 Oct 2021 • Yupan Huang, Hongwei Xue, Bei Liu, Yutong Lu
We adopt Transformer as our unified architecture for its strong performance and task-agnostic design.
no code implementations • 6 Sep 2021 • Hongwei Xue, Bei Liu, Huan Yang, Jianlong Fu, Houqiang Li, Jiebo Luo
To tackle this problem, we propose a model named FGLA to generate high-quality and realistic videos by learning Fine-Grained motion embedding for Landscape Animation.
no code implementations • NeurIPS 2021 • Hongwei Xue, Yupan Huang, Bei Liu, Houwen Peng, Jianlong Fu, Houqiang Li, Jiebo Luo
To tackle this, we propose a fully Transformer visual embedding for VLP to better learn visual relation and further promote inter-modal alignment.
no code implementations • NeurIPS 2021 • Hongwei Xue, Yupan Huang, Bei Liu, Houwen Peng, Jianlong Fu, Houqiang Li, Jiebo Luo
To tackle this, we propose a fully Transformer visual embedding for VLP to better learn visual relation and further promote inter-modal alignment.