Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Wang, Weilun"'
Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity). They may experience significant performa
Externí odkaz:
http://arxiv.org/abs/2307.08317
Autor:
Wang, Zhendong, Bao, Jianmin, Zhou, Wengang, Wang, Weilun, Hu, Hezhen, Chen, Hong, Li, Houqiang
Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images.
Externí odkaz:
http://arxiv.org/abs/2303.09295
In this work, we are dedicated to a new task, i.e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status. This task is challenging and research-
Externí odkaz:
http://arxiv.org/abs/2211.15663
In this work, we are dedicated to text-guided image generation and propose a novel framework, i.e., CLIP2GAN, by leveraging CLIP model and StyleGAN. The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input la
Externí odkaz:
http://arxiv.org/abs/2211.15045
Autor:
Wang, Weilun, Bao, Jianmin, Zhou, Wengang, Chen, Dongdong, Chen, Dong, Yuan, Lu, Li, Houqiang
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. SinDiffusion significantly improves the quality and diversity of generated samples compared with existing GAN-base
Externí odkaz:
http://arxiv.org/abs/2211.12445
Autor:
Wang, Weilun, Bao, Jianmin, Zhou, Wengang, Chen, Dongdong, Chen, Dong, Yuan, Lu, Li, Houqiang
Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks compared with Generative Adversarial Nets (GANs). Recent work on semantic image synthesis mainly follows the \emph{de facto} GAN-based
Externí odkaz:
http://arxiv.org/abs/2207.00050
Autor:
Zhang, Mingyang, Wang, Weilun
Publikováno v:
In Case Studies in Construction Materials July 2024 20
Generative adversarial networks have been widely used in image synthesis in recent years and the quality of the generated image has been greatly improved. However, the flexibility to control and decouple facial attributes (e.g., eyes, nose, mouth) is
Externí odkaz:
http://arxiv.org/abs/2108.11080
Contrastive learning shows great potential in unpaired image-to-image translation, but sometimes the translated results are in poor quality and the contents are not preserved consistently. In this paper, we uncover that the negative examples play a c
Externí odkaz:
http://arxiv.org/abs/2108.04547
Publikováno v:
In Structures June 2024 64