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Found 12 papers, 5 papers with code
Generative Models: What do they know? Do they know things? Let's find out!
Generative models have been shown to be capable of synthesizing highly detailed and realistic images.
A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization
This strategy enables the model to preserve fine details of the desired subjects, such as text and logos.
DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer
Neural Style Transfer (NST) is the field of study applying neural techniques to modify the artistic appearance of a content image to match the style of a reference style image.
NeAT: Neural Artistic Tracing for Beautiful Style Transfer
As a component of curating this data, we present a novel model able to classify if an image is stylistic.
Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis.
Neural Neighbor Style Transfer
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer.
Harnessing the Conditioning Sensorium for Improved Image Translation
Multi-modal domain translation typically refers to synthesizing a novel image that inherits certain localized attributes from a 'content' image (e. g. layout, semantics, or geometry), and inherits everything else (e. g. texture, lighting, sometimes even semantics) from a 'style' image.
Non-Parametric Neural Style Transfer
It seems easy to imagine a photograph of the Eiffel Tower painted in the style of Vincent van Gogh's 'The Starry Night', but upon introspection it is difficult to precisely define what this would entail.
Style Transfer by Relaxed Optimal Transport and Self-Similarity
Style transfer algorithms strive to render the content of one image using the style of another.
Training Deep Networks to be Spatially Sensitive
In many computer vision tasks, for example saliency prediction or semantic segmentation, the desired output is a foreground map that predicts pixels where some criteria is satisfied.
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
We propose an approach for learning category-level semantic segmentation purely from image-level classification tags indicating presence of categories.
