8 citations · 27 across the 10 of their papers we have counts for
10 papers
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…
Painterly Image Harmonization in Dual Domains
Junyan Cao, Yan Hong, Li Niu
Image harmonization aims to produce visually harmonious composite images by adjusting the foreground appearance to be compatible with the background. When the composite image has p…
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
Yan Hong, Li Niu, Jianfu Zhang +1
Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-th…
Few-shot Image Generation Using Discrete Content Representation
Yan Hong, Li Niu, Jianfu Zhang +1
Few-shot image generation and few-shot image translation are two related tasks, both of which aim to generate new images for an unseen category with only a few images. In this work…
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
Yan Hong, Li Niu, Jianfu Zhang +2
Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-th…
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…