8 citations · 16 across the 4 of their papers we have counts for
6 papers · 1 filter
ControlCom: Controllable Image Composition using Diffusion Model
Bo Zhang, Yuxuan Duan, Jun Lan +4
Image composition targets at synthesizing a realistic composite image from a pair of foreground and background images. Recently, generative composition methods are built on large p…
WeditGAN: Few-Shot Image Generation via Latent Space Relocation
Yuxuan Duan, Li Niu, Yan Hong +1
In few-shot image generation, directly training GAN models on just a handful of images faces the risk of overfitting. A popular solution is to transfer the models pretrained on lar…
Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation
Yuxuan Duan, Yan Hong, Li Niu +1
The performances of defect inspection have been severely hindered by insufficient defect images in industries, which can be alleviated by generating more samples as data augmentati…
F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation
Yan Hong, Li Niu, Jianfu Zhang +3
In order to generate images for a given category, existing deep generative models generally rely on abundant training images. However, extensive data acquisition is expensive and f…
Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets
Yan Hong, Li Niu, Jianfu Zhang +1
Multi-task Learning (MTL) for classification with disjoint datasets aims to explore MTL when one task only has one labeled dataset. In existing methods, for each task, the unlabele…
MatchingGAN: Matching-based Few-shot Image Generation
Yan Hong, Li Niu, Jianfu Zhang +1
To generate new images for a given category, most deep generative models require abundant training images from this category, which are often too expensive to acquire. To achieve t…