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High-Resolution Image Synthesis with Latent Diffusion Models [PDF]
Computer Vision and Pattern Recognition, 2021 By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism
Robin Rombach +4 more
semanticscholar +1 more source
Taming Transformers for High-Resolution Image Synthesis [PDF]
Computer Vision and Pattern Recognition, 2020 Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions.
Patrick Esser, Robin Rombach, B. Ommer
semanticscholar +1 more source
Restormer: Efficient Transformer for High-Resolution Image Restoration [PDF]
Computer Vision and Pattern Recognition, 2021 Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Syed Waqas Zamir +5 more
semanticscholar +1 more source
Deep High-Resolution Representation Learning for Human Pose Estimation [PDF]
Computer Vision and Pattern Recognition, 2019 In this paper, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high ...
Ke Sun +3 more
semanticscholar +1 more source
Swin Transformer V2: Scaling Up Capacity and Resolution [PDF]
Computer Vision and Pattern Recognition, 2021 We present techniques for scaling Swin Transformer [35] up to 3 billion parameters and making it capable of training with images of up to 1,536x1,536 resolution.
Ze Liu +11 more
semanticscholar +1 more source
Light: Science & Applications, 2023 Regulation of perovskite growth plays a critical role in the development of high-performance optoelectronic devices. However, judicious control of the grain growth for perovskite light emitting diodes is elusive due to its multiple requirements in terms ...
Hao Wang +19 more
doaj +1 more source
Image Super-Resolution via Iterative Refinement [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al.
Chitwan Saharia +5 more
semanticscholar +1 more source
Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models [PDF]
Computer Vision and Pattern Recognition, 2023 Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation,
A. Blattmann +6 more
semanticscholar +1 more source
Current and Future Costs of Intractable Conflicts—Can They Create Attitude Change?
Frontiers in Psychology, 2021 Members of societies involved in an intractable conflict usually consider costs that stem from the continuation of the conflict as unavoidable and even justify for their collective existence.
Nimrod Rosler +4 more
doaj +1 more source
Deep High-Resolution Representation Learning for Visual Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
Jingdong Wang +11 more
semanticscholar +1 more source