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Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
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Proceedings Article
Real-Time Face Detection Using Boosting Learning in Hierarchical Feature Spaces
TL;DR: It is argued that global features, like those derived from Principal Component Analysis, can be advantageously used in the later stages of boosting, when local features do not provide any further benefit, without affecting computational complexity.
Journal ArticleDOI
SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning
Cheng Tan,Zhan Gao,Stan Z. Li +2 more
TL;DR: SimVP as mentioned in this paper is a simple spatio-temporal predictive baseline model that is completely built upon convolutional networks without recurrent architectures and trained by common mean squared error loss in an end-to-end fashion.
Journal ArticleDOI
Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader
TL;DR: Zhang et al. as discussed by the authors designed a three-track alignment between predicted distance profiles and structure profiles extracted from PDB and AlphaFold DB, to improve the recognition accuracy of remote templates.
Book ChapterDOI
Feature correlation filter for face recognition
TL;DR: This paper proposes a novel method, called "feature correlation filter (FCF)", by extending the concept of correlation filter to feature spaces, which preserves the benefits of conventional correlation filters, i.e., shift-invariant, occlusion-insensitive, and closed-form solution and also inherits virtues of the feature representations.
Posted Content
Fast Matching by 2 Lines of Code for Large Scale Face Recognition Systems
TL;DR: This paper proposes a method to apply the popular cascade classifier into face recognition to improve the computational efficiency while keeping high recognition rate, which can be easily generalized to other biometrics as a post-processing module.