Search Results for author:
Found 2 papers, 1 papers with code
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
We propose ScribbleGen, a generative data augmentation method that leverages a ControlNet diffusion model conditioned on semantic scribbles to produce high-quality training data.
Open-Vocabulary Universal Image Segmentation with MaskCLIP
In this paper, we tackle an emerging computer vision task, open-vocabulary universal image segmentation, that aims to perform semantic/instance/panoptic segmentation (background semantic labeling + foreground instance segmentation) for arbitrary categories of text-based descriptions in inference time.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
