2 citations · 2 across the 8 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
cs.CV2023
Latent Space Editing in Transformer-Based Flow Matching
Vincent Tao Hu, David W Zhang, Pascal Mettes +3
This paper strives for image editing via generative models. Flow Matching is an emerging generative modeling technique that offers the advantage of simple and efficient training. S…
cs.CV2023
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
Jacob Schnell, Jieke Wang, Lu Qi +2
Recent advances in generative models, such as diffusion models, have made generating high-quality synthetic images widely accessible. Prior works have shown that training on synthe…