15 citations · 26 across the 9 of their papers we have counts for
4 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…
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…
Zero-shot Learning with Deep Neural Networks for Object Recognition
Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu
Zero-shot learning deals with the ability to recognize objects without any visual training sample. To counterbalance this lack of visual data, each class to recognize is associated…
Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning
Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu
Zero-shot learning aims to recognize instances of unseen classes, for which no visual instance is available during training, by learning multimodal relations between samples from s…