The use of foundation diffusion models as prior for image compression, however, is still an underexplored research area. Some works address this task [12,30] but operate at extremely low bitrates (less than 0.03
Diffusion models, including Glide, Dalle-2, Imagen, and Stable Diffusion, have spearheaded recent advances in AI-based image generation, taking the world of “AI Art generation” by storm. Generating high-quality images from text descriptions is a challenging task. It requires a deep understanding...
参考 ^Rombach, Robin, et al. "High-resolution image synthesis with latent diffusion models." *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*. 2022. ^Ho, Jonathan, Ajay Jain, and Pieter Abbeel. "Denoising diffusion probabilistic models." *Advances in Neural I...
Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diff
Image Guiding Mechanisms Additional Results Super-Resolution Implementation Details and Hyperparameters Computational Requirements Details on Autoencoder Models Additional Qualitative Results 摘要 Diffusion models(DMs)被证明在复杂自然场景的高分辨率图像合成能力优于以往的GAN或autoregressive (AR)transformer。作为基...
To adapt to limited uplink bandwidth, most media platforms opt to compress videos to bitrate streams for transmission. However, this compression often leads to significant texture loss and artifacts, which severely degrade the Quality of Experience (QoE). We propose a latent feature diffusion model...
Text-to-image Diffusion Model in Generative AI: A Survey Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon [14th Mar., 2023] [arXiv, 2023] [Paper]Diffusion Models for Non-autoregressive Text Generation: A Survey Yifan Li, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen [...
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medic
eliminating the need for retraining deep learning models at different operating points. Extensive experiments demonstrate the effectiveness of the proposed framework in both image reconstruction and downstream machine vision tasks such as object detection, segmentation, and facial landmark detection, achieving...
Latent Diffusion Models（潜在扩散模型）的整体框架如下图所示。首先需要训练一个自编码模型，这样就可以利用编码器对图片进行压缩，然后在潜在表示空间上进行扩散操作，最后再用解码器恢复到原始像素空间。这种方法被称为感知压缩（Perceptual Compression）。个人认为这种将高维特征压缩到低维，然后在低维空间上进行操作的...