80 citations · 104 across the 17 of their papers we have counts for
3 papers · 1 filter
Semantic Generative Augmentations for Few-Shot Counting
Perla Doubinsky, Nicolas Audebert, Michel Crucianu +1
With the availability of powerful text-to-image diffusion models, recent works have explored the use of synthetic data to improve image classification performances. These works sho…
TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation
Paul Grimal, Hervé Le Borgne, Olivier Ferret +1
The progress in the generation of synthetic images has made it crucial to assess their quality. While several metrics have been proposed to assess the rendering of images, it is cr…
Wasserstein Loss for Semantic Editing in the Latent Space of GANs
Perla Doubinsky, Nicolas Audebert, Michel Crucianu +1
The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus a…