Deep Graph Metric Learning for Weakly Supervised Person Re-Identification

Author(s):  
Jingke Meng ◽  
Wei-Shi Zheng ◽  
Jian-Huang Lai ◽  
Liang Wang
2021 ◽  
pp. 1-13
Author(s):  
Kai Zhuang ◽  
Sen Wu ◽  
Xiaonan Gao

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.


2021 ◽  
pp. 471-482
Author(s):  
Patrick P. K. Chan ◽  
Keke Chen ◽  
Linyi Xu ◽  
Xiaoman Hu ◽  
Daniel S. Yeung

Author(s):  
Davit Buniatyan ◽  
Sergiy Popovych ◽  
Dodam Ih ◽  
Thomas Macrina ◽  
Jonathan Zung ◽  
...  

Author(s):  
Rui Qian ◽  
Yunchao Wei ◽  
Honghui Shi ◽  
Jiachen Li ◽  
Jiaying Liu ◽  
...  

Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i.e. points within the same category should have more similar feature representations compared to those from different categories. We formulate such a characteristic into a simple distance metric loss, which collaborates with the point-wise cross-entropy loss to optimize the deep neural networks. Furthermore, to fully exploit the limited annotations, distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner. We conduct extensive experiments on two challenging scene parsing benchmarks of PASCALContext and ADE 20K to validate the effectiveness of our PDML, and competitive mIoU scores are achieved.


Sign in / Sign up

Export Citation Format

Share Document