F
Feng Zhou
Researcher at University of Michigan
Publications - 287
Citations - 10503
Feng Zhou is an academic researcher from University of Michigan. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 41, co-authored 174 publications receiving 7579 citations. Previous affiliations of Feng Zhou include Nanyang Technological University & Princeton University.
Papers
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Proceedings ArticleDOI
Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR
TL;DR: This paper reviews the ten-year development of the work presented at the ISMAR conference and its predecessors with a particular focus on tracking, interaction and display research, providing a roadmap for future augmented reality research.
Proceedings ArticleDOI
Detecting depression from facial actions and vocal prosody
Jeffrey F. Cohn,Tomas Simon Kruez,Iain Matthews,Ying Yang,Minh Hoai Nguyen,Margara Tejera Padilla,Feng Zhou,Fernando De la Torre +7 more
TL;DR: The findings suggest the feasibility of automatic detection of depression, raise new issues in automated facial image analysis and machine learning, and have exciting implications for clinical theory and practice.
Proceedings ArticleDOI
Deep Metric Learning with Angular Loss
TL;DR: Zhang et al. as discussed by the authors proposed a novel angular loss, which takes angle relationship into account, for learning better similarity metric, which aims at constraining the angle at the negative point of triplet triangles.
Proceedings ArticleDOI
Factorized graph matching
Feng Zhou,Fernando De la Torre +1 more
TL;DR: Factorized graph matching (FGM) is proposed, which factorizes the large pairwise affinity matrix into smaller matrices that encode the local structure of each graph and the Pairwise affinity between edges.
Journal ArticleDOI
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion
TL;DR: This work poses the problem of learning motion primitives as one of temporal clustering, and derives an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA), which finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters.