14 citations · 43 across the 8 of their papers we have counts for
7 papers · 1 filter
ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models
Jingyuan Zhu, Shiyu Li, Yuxuan Liu +4
Modern diffusion-based image generative models have made significant progress and become promising to enrich training data for the object detection task. However, the generation qu…
Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Large-scale text-to-image diffusion models have achieved great success in synthesizing high-quality and diverse images given target text prompts. Despite the revolutionary image ge…
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. Typic…
Few-shot 3D Shape Generation
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Realistic and diverse 3D shape generation is helpful for a wide variety of applications such as virtual reality, gaming, and animation. Modern generative models, such as GANs and d…
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Video generation has achieved rapid progress benefiting from high-quality renderings provided by powerful image generators. We regard the video synthesis task as generating a seque…
Few-shot Image Generation with Diffusion Models
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. Howev…
Few-shot Image Generation via Masked Discrimination
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Few-shot image generation aims to generate images of high quality and great diversity with limited data. However, it is difficult for modern GANs to avoid overfitting when trained…