2 code implementations • CVPR 2020 • Yuming Shen, Jie Qin, Jiaxin Chen, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao
One bottleneck (i. e., binary codes) conveys the high-level intrinsic data structure captured by the code-driven graph to the other (i. e., continuous variables for low-level detail information), which in turn propagates the updated network feedback for the encoder to learn more discriminative binary codes.
no code implementations • NeurIPS 2019 • Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong liu, Yu Li, Ling Shao
DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution.
no code implementations • 16 Sep 2019 • Huan Xiong, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao
Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning.
1 code implementation • 16 Jun 2019 • Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, Ling Shao
A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.
1 code implementation • ECCV 2018 • Jingyi Zhang, Fumin Shen, Li Liu, Fan Zhu, Mengyang Yu, Ling Shao, Heng Tao Shen, Luc van Gool
The generative model learns a mapping that the distributions of sketches can be indistinguishable from the distribution of natural images using an adversarial loss, and simultaneously learns an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability.
3 code implementations • 26 Jul 2018 • Jun Xu, Mengyang Yu, Ling Shao, WangMeng Zuo, Deyu Meng, Lei Zhang, David Zhang
However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations.
no code implementations • CVPR 2017 • Li Liu, Ling Shao, Fumin Shen, Mengyang Yu
Learning to hash has been recognized to accomplish highly efficient storage and retrieval for large-scale visual data.
no code implementations • ICCV 2015 • Li Liu, Mengyang Yu, Ling Shao
Recently, very high-dimensional feature representations, e. g., Fisher Vector, have achieved excellent performance for visual recognition and retrieval.
no code implementations • 3 Aug 2015 • Mengyang Yu, Li Liu, Ling Shao
Conventional vision algorithms adopt a single type of feature or a simple concatenation of multiple features, which is always represented in a high-dimensional space.
no code implementations • CVPR 2015 • Xiantong Zhen, Zhijie Wang, Mengyang Yu, Shuo Li
In this paper, we propose a novel supervised descriptor learning (SDL) algorithm to establish a discriminative and compact feature representation for multi-output regression.