15 citations · 26 across the 9 of their papers we have counts for
9 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…
Multi-Attribute Balanced Sampling for Disentangled GAN Controls
Perla Doubinsky, Nicolas Audebert, Michel Crucianu +1
Various controls over the generated data can be extracted from the latent space of a pre-trained GAN, as it implicitly encodes the semantics of the training data. The discovered co…
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…
From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process
Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu
Zero-shot learning (ZSL) is concerned with the recognition of previously unseen classes. It relies on additional semantic knowledge for which a mapping can be learned with training…