6 citations · 14 across the 8 of their papers we have counts for
8 papers
Fine-grained Text to Image Synthesis
Xu Ouyang, Ying Chen, Kaiyue Zhu +1
Fine-grained text to image synthesis involves generating images from texts that belong to different categories. In contrast to general text to image synthesis, in fine-grained synt…
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…
"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization
Tianlong Chen, Zhenyu Zhang, Xu Ouyang +3
Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typica…
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…
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…