2 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
Multimodal Representations for Teacher-Guided Compositional Visual Reasoning
Wafa Aissa, Marin Ferecatu, Michel Crucianu
Neural Module Networks (NMN) are a compelling method for visual question answering, enabling the translation of a question into a program consisting of a series of reasoning sub-ta…
Curriculum Learning for Compositional Visual Reasoning
Wafa Aissa, Marin Ferecatu, Michel Crucianu
Visual Question Answering (VQA) is a complex task requiring large datasets and expensive training. Neural Module Networks (NMN) first translate the question to a reasoning path, th…
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…
Why is the prediction wrong? Towards underfitting case explanation via meta-classification
Sheng Zhou, Pierre Blanchart, Michel Crucianu +1
In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier. Sinc…