no code implementations • ECCV 2020 • Jianli Zhou, Chao Liang, Jun Chen
We utilize variational autoencoder (VAE) to estimate the lower bound of the log-likelihood of image and explore to project the input images back into the high probability regions of image manifold again.
no code implementations • 26 Mar 2024 • Chao Liang, Jianwen Jiang, Tianyun Zhong, Gaojie Lin, Zhengkun Rong, Jiaqi Yang, Yongming Zhu
Talking face generation technology creates talking videos from arbitrary appearance and motion signal, with the "arbitrary" offering ease of use but also introducing challenges in practical applications.
1 code implementation • 1 Feb 2024 • Chao Liang, Fan Ma, Linchao Zhu, Yingying Deng, Yi Yang
Moreover, we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner.
no code implementations • 16 Oct 2023 • Chao Liang, Linchao Zhu, Humphrey Shi, Yi Yang
Sample selection is an effective way to deal with label noise.
1 code implementation • 14 Sep 2023 • Yongyuan Li, Xiuyuan Qin, Chao Liang, Mingqiang Wei
In particular, we propose a Fine-Grained Feature Fusion (FGFF) module to effectively capture fine texture feature information around teeth and surrounding regions, and use these features to fine-grain the feature map to enhance the clarity of teeth.
1 code implementation • 3 Jul 2023 • Chao Liang, Zongxin Yang, Linchao Zhu, Yi Yang
In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution.
1 code implementation • 18 May 2023 • Wei Xiang, Chao Liang, Bang Wang
Although an auxiliary task is not used to directly output final prediction, we argue that during the joint training some of its learned features can be useful to boost the main task.
1 code implementation • The 33rd British Machine Vision Conference 2022 • Ji Huang, Chao Liang, Yue Zhang, Zhongyuan Wang, Chunjie Zhang
Existing RA work can be generally divided into unsupervised methods and fully-supervised methods.
no code implementations • 30 Oct 2021 • Yanrui Niu, Jingyao Yang, Ankang Lu, Baojin Huang, Yue Zhang, Ji Huang, Shishi Wen, Dongshu Xu, Chao Liang, Zhongyuan Wang, Jun Chen
We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper.
1 code implementation • 23 Oct 2021 • Yue Zhang, Chao Liang, Longxiang Jiang
To address this issue, we propose a confidence-aware active feedback method (CAAF) that is specifically designed for online RF in interactive INS tasks.
1 code implementation • 19 Apr 2021 • Chao Liang, Zhipeng Zhang, Xue Zhou, Bing Li, Weiming Hu
Eventually, it helps to reload the ``fake background'' and repair the broken tracklets.
no code implementations • 1 Jan 2021 • Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, Makoto Yamada
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
4 code implementations • 23 Oct 2020 • Chao Liang, Zhipeng Zhang, Xue Zhou, Bing Li, Shuyuan Zhu, Weiming Hu
However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
Ranked #1 on Multi-Object Tracking on HiEve (using extra training data)
1 code implementation • 25 May 2020 • Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, Makoto Yamada
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.