28 citations · 48 across the 6 of their papers we have counts for
6 papers
Generating Image Sequence from Description with LSTM Conditional GAN
Xu Ouyang, Xi Zhang, Di Ma +1
Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In thi…
Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask
Xi Zhang, Di Ma, Xu Ouyang +3
Using a layered representation for motion estimation has the advantage of being able to cope with discontinuities and occlusions. In this paper, we learn to estimate optical flow b…
Lecture video indexing using boosted margin maximizing neural networks
Di Ma, Xi Zhang, Xu Ouyang +1
This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide i…
CGMOS: Certainty Guided Minority OverSampling
Xi Zhang, Di Ma, Lin Gan +2
Handling imbalanced datasets is a challenging problem that if not treated correctly results in reduced classification performance. Imbalanced datasets are commonly handled using mi…
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
Xi Zhang, Yanwei Fu, Shanshan Jiang +2
Learning from synthetic data has many important and practical applications. An example of application is photo-sketch recognition. Using synthetic data is challenging due to the di…
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder
Xi Zhang, Yanwei Fu, Andi Zang +2
We propose a method for using synthetic data to help learning classifiers. Synthetic data, even is generated based on real data, normally results in a shift from the distribution o…