no code implementations • 21 Jan 2024 • Yukun Zuo, Hantao Yao, Lu Yu, Liansheng Zhuang, Changsheng Xu
Nonetheless, these learnable prompts tend to concentrate on the discriminatory knowledge of the current task while ignoring past task knowledge, leading to that learnable prompts still suffering from catastrophic forgetting.
no code implementations • 11 Jan 2024 • Yukun Zuo, Hantao Yao, Liansheng Zhuang, Changsheng Xu
We introduce Hierarchical Augmentation and Distillation (HAD), which comprises the Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM) to efficiently utilize the hierarchical structure of data and models, respectively.
no code implementations • 18 May 2023 • Bochao Liu, Shiming Ge, Pengju Wang, Liansheng Zhuang, Tongliang Liu
In particular, we first train a model to fit the distribution of the training data and make it satisfy differential privacy by performing a randomized response mechanism during training process.
no code implementations • CVPR 2023 • Shang Chai, Liansheng Zhuang, Fengying Yan
Though existing methods based on generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) have progressed, they still leave much room for improving the quality and diversity of the results.
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.