M
Ming Shao
Researcher at University of Massachusetts Dartmouth
Publications - 125
Citations - 4061
Ming Shao is an academic researcher from University of Massachusetts Dartmouth. The author has contributed to research in topics: Facial recognition system & Feature extraction. The author has an hindex of 31, co-authored 121 publications receiving 3298 citations. Previous affiliations of Ming Shao include Northeastern University & University of Massachusetts Amherst.
Papers
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Proceedings ArticleDOI
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
TL;DR: This paper presents a multi-stream bi-directional recurrent neural network for fine-grained action detection that significantly outperforms state-of-the-art action detection methods on both datasets.
Journal ArticleDOI
Generalized Transfer Subspace Learning Through Low-Rank Constraint
Ming Shao,Dmitry Kit,Yun Fu +2 more
TL;DR: Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.
Journal ArticleDOI
Understanding Kin Relationships in a Photo
TL;DR: Experimental results have shown that the proposed algorithms can effectively annotate the kin relationships among people in an image and semantic context can further improve the accuracy.
Proceedings ArticleDOI
Kinship verification through transfer learning
Siyu Xia,Ming Shao,Yun Fu +2 more
TL;DR: Experimental results show that the hypothesis on the role of young parents is valid and transfer learning is effective to enhance the verification accuracy and the large gap between distributions can be significantly reduced and kinship verification problem becomesmore discriminative.
Proceedings ArticleDOI
Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning
Zhengming Ding,Ming Shao,Yun Fu +2 more
TL;DR: This work forms a novel framework to jointly seek a low-rank embedding and semantic dictionary to link visual features with their semantic representations, which manages to capture shared features across different observed classes.