32 citations · 85 across the 6 of their papers we have counts for
10 papers
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
Daiqing Li, Huan Ling, Seung Wook Kim +4
Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative - to synthesize a large labeled dataset via…
EditGAN: High-Precision Semantic Image Editing
Huan Ling, Karsten Kreis, Daiqing Li +3
Generative adversarial networks (GANs) have recently found applications in image editing. However, most GAN based image editing methods often require large scale datasets with sema…
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
Yuxuan Zhang, Huan Ling, Jun Gao +5
We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are…
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Yuxuan Zhang, Wenzheng Chen, Huan Ling +4
Differentiable rendering has paved the way to training neural networks to perform "inverse graphics" tasks such as predicting 3D geometry from monocular photographs. To train high…
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
Bowen Chen, Huan Ling, Xiaohui Zeng +3
Manually labeling video datasets for segmentation tasks is extremely time consuming. In this paper, we introduce ScribbleBox, a novel interactive framework for annotating object in…
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen, Jun Gao, Huan Ling +4
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enab…