1 code implementation • 19 Mar 2024 • Linjiang Huang, Rongyao Fang, Aiping Zhang, Guanglu Song, Si Liu, Yu Liu, Hongsheng Li
In this study, we delve into the generation of high-resolution images from pre-trained diffusion models, addressing persistent challenges, such as repetitive patterns and structural distortions, that emerge when models are applied beyond their trained resolutions.
1 code implementation • CVPR 2023 • Jingqiu Zhou, Linjiang Huang, Liang Wang, Si Liu, Hongsheng Li
Besides, the generated pseudo-labels can be fluctuating and inaccurate at the early stage of training.
Pseudo Label Weakly-supervised Temporal Action Localization +1
1 code implementation • 22 Nov 2022 • Linjiang Huang, Kaixin Lu, Guanglu Song, Liang Wang, Si Liu, Yu Liu, Hongsheng Li
In this paper, we present a novel training scheme, namely Teach-DETR, to learn better DETR-based detectors from versatile teacher detectors.
1 code implementation • CVPR 2022 • Linjiang Huang, Liang Wang, Hongsheng Li
Our method seeks to mine the representative snippets in each video for propagating information between video snippets to generate better pseudo labels.
Pseudo Label Weakly-supervised Temporal Action Localization +1
1 code implementation • ICCV 2021 • Linjiang Huang, Liang Wang, Hongsheng Li
In this paper, we present a framework named FAC-Net based on the I3D backbone, on which three branches are appended, named class-wise foreground classification branch, class-agnostic attention branch and multiple instance learning branch.
no code implementations • 2 Nov 2020 • Jianhua Yang, Yan Huang, Kai Niu, Linjiang Huang, Zhanyu Ma, Liang Wang
Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of \emph{semantic asymmetry}.
Ranked #9 on Referring Expression Segmentation on J-HMDB