Name
Affiliation
Papers
MENGYI LIU
Chinese Acad Sci, Inst Comp, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
20
Collaborators
Citations 
PageRank 
29
291
13.21
Referers 
Referees 
References 
720
430
282
Search Limit
100720
Title
Citations
PageRank
Year
Binary Convolutional Neural Networks for Facial Action Unit Detection.00.342021
A comprehensive survey on automatic facial action unit analysis10.352020
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline.00.342018
Terminology Translation Error Identification and Correction.00.342017
Optimizing Topic Distributions of Descriptions for Image Description Translation.00.342017
Video modeling and learning on Riemannian manifold for emotion recognition in the wild30.392016
Topic Model Based Adaptation Data Selection for Domain-Specific Machine Translation.00.342016
Learning prototypes and similes on Grassmann manifold for spontaneous expression recognition.00.342016
Skipping Word: A Character-Sequential Representation based Framework for Question Answering00.342016
Self-paced Learning for Weakly Supervised Evidence Discovery in Multimedia Event Search.00.342016
Image-Image Search for Comparable Corpora Construction.00.342016
Learning Mid-level Words on Riemannian Manifold for Action Recognition.20.382015
Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition130.532015
Exploiting Feature Hierarchies With Convolutional Neural Networks for Cultural Event Recognition70.432015
Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild732.132014
Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition911.992014
Deeply Learning Deformable Facial Action Parts Model For Dynamic Expression Analysis120.622014
Partial least squares regression on grassmannian manifold for emotion recognition301.422013
Au-Aware Deep Networks For Facial Expression Recognition561.562013
Enhancing expression recognition in the wild with unlabeled reference data30.382012