28 citations · 37 across the 5 of their papers we have counts for
8 papers
Semi-supervised Domain Adaptation for Semantic Segmentation
Ying Chen, Xu Ouyang, Kaiyue Zhu +1
Deep learning approaches for semantic segmentation rely primarily on supervised learning approaches and require substantial efforts in producing pixel-level annotations. Further, s…
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation
Ying Chen, Xu Ouyang, Kaiyue Zhu +1
Semantic segmentation using convolutional neural networks (CNN) is a crucial component in image analysis. Training a CNN to perform semantic segmentation requires a large amount of…
Domain Adaptation on Semantic Segmentation for Aerial Images
Ying Chen, Xu Ouyang, Kaiyue Zhu +1
Semantic segmentation has achieved significant advances in recent years. While deep neural networks perform semantic segmentation well, their success rely on pixel level supervisio…
Accelerated WGAN update strategy with loss change rate balancing
Xu Ouyang, Gady Agam
Optimizing the discriminator in Generative Adversarial Networks (GANs) to completion in the inner training loop is computationally prohibitive, and on finite datasets would result…
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…