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Found 8 papers, 1 papers with code
Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now
All three classifiers are denied access to image pixels, and look only at derived geometric features.
Make It So: Steering StyleGAN for Any Image Inversion and Editing
StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge.
StyLitGAN: Prompting StyleGAN to Produce New Illumination Conditions
We propose a novel method, StyLitGAN, for relighting and resurfacing generated images in the absence of labeled data.
SIRfyN: Single Image Relighting from your Neighbors
Novel theory shows that one can use similar scenes to estimate the different lightings that apply to a given scene, with bounded expected error.
Intrinsic Image Decomposition using Paradigms
The best modern intrinsic image methods learn a map from image to albedo using rendered models and human judgements.
Unrestricted Adversarial Examples via Semantic Manipulation
Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation.
Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech
We achieve the trifecta: (1) High accuracy for the diverse captions as evaluated by standard captioning metrics and user studies; (2) Faster computation of diverse captions compared to beam search and diverse beam search; and (3) High diversity as evaluated by counting novel sentences, distinct n-grams and mutual overlap (i. e., mBleu-4) scores.
Quantitative Evaluation of Style Transfer
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image.
