1 code implementation • 25 Apr 2022 • Junshan Hu, Chaoxu Guo, Liansheng Zhuang, Biao Wang, Tiezheng Ge, Yuning Jiang, Houqiang Li
For the region perspective, we introduce Region Evaluate Module (REM) which uses a new and efficient sampling method for proposal feature representation containing more contextual information compared with point feature to refine category score and proposal boundary.
no code implementations • 26 Sep 2021 • Minghong Yao, Zhiguang Liu, Liangwei Wang, Houqiang Li, Liansheng Zhuang
However, collecting and labeling a large dataset is time-consuming and is not a user-friendly requirement for many cloud platforms.
2 code implementations • 6 Feb 2020 • Qiwei He, Liansheng Zhuang, Houqiang Li
However, due to the brittleness of deterministic methods, HER and its variants typically suffer from a major challenge for stability and convergence, which significantly affects the final performance.
no code implementations • 25 Sep 2019 • Minghong Yao, Liansheng Zhuang, Houqiang Li, Jian Yang, Shafei Wang
Results show that our model can outperform the dominant models consistently in these tasks.
no code implementations • 8 Jul 2016 • Liansheng Zhuang, Zihan Zhou, Jingwen Yin, Shenghua Gao, Zhouchen Lin, Yi Ma, Nenghai Yu
In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph.
no code implementations • 3 Sep 2014 • Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma
This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting.
no code implementations • 8 Feb 2014 • Liansheng Zhuang, Tsung-Han Chan, Allen Y. Yang, S. Shankar Sastry, Yi Ma
In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class.
no code implementations • CVPR 2013 • Liansheng Zhuang, Allen Y. Yang, Zihan Zhou, S. Shankar Sastry, Yi Ma
To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.