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20172022
most citedvon Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification

61 citations · 118 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CV2018

Taking Control of Intra-class Variation in Conditional GANs Under Weak Supervision

Richard T. Marriott, Sami Romdhani, Liming Chen

Generative Adversarial Networks (GANs) are able to learn mappings between simple, relatively low-dimensional, random distributions and points on the manifold of realistic images in…

cs.CV2018

Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition

Asim Jan, Huaxiong Ding, Hongying Meng +2

Meaningful facial parts can convey key cues for both facial action unit detection and expression prediction. Textured 3D face scan can provide both detailed 3D geometric shape and…

cs.CV2018

Discriminative Label Consistent Domain Adaptation

Lingkun Luo, Liming Chen, Ying lu +1

Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target.…

cs.CV2018

Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure

Ying Lu, Liming Chen, Alexandre Saidi +1

Correctly estimating the discrepancy between two data distributions has always been an important task in Machine Learning. Recently, Cuturi proposed the Sinkhorn distance which mak…

cs.CV201712 cited

Discriminative and Geometry Aware Unsupervised Domain Adaptation

Lingkun Luo, Liming Chen, Shiqiang Hu +2

Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation o…

cs.CV201712 cited

Improving Heterogeneous Face Recognition with Conditional Adversarial Networks

Wuming Zhang, Zhixin Shu, Dimitris Samaras +1

Heterogeneous face recognition between color image and depth image is a much desired capacity for real world applications where shape information is looked upon as merely involved…