Zobrazeno 1 - 10
of 173
pro vyhledávání: '"Talebi, Hossein"'
Autor:
Mei, Kangfu, Tu, Zhengzhong, Delbracio, Mauricio, Talebi, Hossein, Patel, Vishal M., Milanfar, Peyman
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. While improved network architecture and inference algorithms have shown to effectively boost sampling efficiency of diffusion models, the
Externí odkaz:
http://arxiv.org/abs/2404.01367
Autor:
Qi, Chenyang, Tu, Zhengzhong, Ye, Keren, Delbracio, Mauricio, Milanfar, Peyman, Chen, Qifeng, Talebi, Hossein
Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement. However, it still remains an open research problem to adopt this language-vision paradigm for mo
Externí odkaz:
http://arxiv.org/abs/2312.11595
Autor:
Mei, Kangfu, Delbracio, Mauricio, Talebi, Hossein, Tu, Zhengzhong, Patel, Vishal M., Milanfar, Peyman
Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement, restoration, editing, and compositing. However, their widespread adoption is hinder
Externí odkaz:
http://arxiv.org/abs/2310.01407
Image resizing operation is a fundamental preprocessing module in modern computer vision. Throughout the deep learning revolution, researchers have overlooked the potential of alternative resizing methods beyond the commonly used resizers that are re
Externí odkaz:
http://arxiv.org/abs/2304.02859
Diffusion Probabilistic Models (DPMs) have recently been employed for image deblurring, formulated as an image-conditioned generation process that maps Gaussian noise to the high-quality image, conditioned on the blurry input. Image-conditioned DPMs
Externí odkaz:
http://arxiv.org/abs/2212.01789
Autor:
Daras, Giannis, Delbracio, Mauricio, Talebi, Hossein, Dimakis, Alexandros G., Milanfar, Peyman
We define a broader family of corruption processes that generalizes previously known diffusion models. To reverse these general diffusions, we propose a new objective called Soft Score Matching that provably learns the score function for any linear c
Externí odkaz:
http://arxiv.org/abs/2209.05442
Autor:
Tu, Zhengzhong, Talebi, Hossein, Zhang, Han, Yang, Feng, Milanfar, Peyman, Bovik, Alan, Li, Yinxiao
Transformers have recently gained significant attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size has limited their wide adoption in state-of-the-art vision backbones. I
Externí odkaz:
http://arxiv.org/abs/2204.01697
Publikováno v:
In Sustainable Cities and Society 15 October 2024 113
Autor:
Tu, Zhengzhong, Talebi, Hossein, Zhang, Han, Yang, Feng, Milanfar, Peyman, Bovik, Alan, Li, Yinxiao
Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effective in many vision tasks such as image recognition, there remain cha
Externí odkaz:
http://arxiv.org/abs/2201.02973
Autor:
Whang, Jay, Delbracio, Mauricio, Talebi, Hossein, Saharia, Chitwan, Dimakis, Alexandros G., Milanfar, Peyman
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics a
Externí odkaz:
http://arxiv.org/abs/2112.02475