5 papers
Deep Manifold Hashing: A Divide-and-Conquer Approach for Semi-Paired Unsupervised Cross-Modal Retrieval
Yufeng Shi, Xinge You, Jiamiao Xu +3
Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed. Despite their empirical suc…
Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning
Jiamiao Xu, Fangzhao Wang, Qinmu Peng +4
Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods…
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
Peng Zhang, Shujian Yu, Jiamiao Xu +4
It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this pr…
Multi-view Common Component Discriminant Analysis for Cross-view Classification
Xinge You, Jiamiao Xu, Wei Yuan +3
Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision. A promising approach to handle this p…
Multi-view Hybrid Embedding: A Divide-and-Conquer Approach
Jiamiao Xu, Shujian Yu, Xinge You +3
We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multi-view subsp…