Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Motamed, Saman"'
With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, manipulating the cross-attention layers of Text-to-Image (T2I) diffusio
Externí odkaz:
http://arxiv.org/abs/2404.05519
Autor:
Paskaleva, Reni, Holubakha, Mykyta, Ilic, Andela, Motamed, Saman, Van Gool, Luc, Paudel, Danda
Canonical emotions, such as happy, sad, and fearful, are easy to understand and annotate. However, emotions are often compound, e.g. happily surprised, and can be mapped to the action units (AUs) used for expressing emotions, and trivially to the can
Externí odkaz:
http://arxiv.org/abs/2404.01243
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques. Recently,
Externí odkaz:
http://arxiv.org/abs/2401.05465
Autor:
Xu, Jianjin, Motamed, Saman, Vaddamanu, Praneetha, Wu, Chen Henry, Haene, Christian, Bazin, Jean-Charles, de la Torre, Fernando
Face inpainting is important in various applications, such as photo restoration, image editing, and virtual reality. Despite the significant advances in face generative models, ensuring that a person's unique facial identity is maintained during the
Externí odkaz:
http://arxiv.org/abs/2312.03556
Diffusion models have revolutionized generative content creation and text-to-image (T2I) diffusion models in particular have increased the creative freedom of users by allowing scene synthesis using natural language. T2I models excel at synthesizing
Externí odkaz:
http://arxiv.org/abs/2311.13833
Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. However, current generative models for face inpainting often fail to pre
Externí odkaz:
http://arxiv.org/abs/2304.06107
Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a particular region of the output space or sampling evenly over a range of
Externí odkaz:
http://arxiv.org/abs/2209.06970
Autor:
Motamed, Saman, Khalvati, Farzad
Generative Adversarial Networks can learn the mapping of random noise to realistic images in a semi-supervised framework. This mapping ability can be used for semi-supervised image classification to detect images of an unknown class where there is no
Externí odkaz:
http://arxiv.org/abs/2103.02496
Autor:
Motamed, Saman, Khalvati, Farzad
From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. With the remarkable ability of GANs in learning the distribution and gene
Externí odkaz:
http://arxiv.org/abs/2102.06944
COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-r
Externí odkaz:
http://arxiv.org/abs/2010.06418