80 citations · 80 across the 3 of their papers we have counts for
6 papers
Self-Improving SLAM in Dynamic Environments: Learning When to Mask
Adrian Bojko, Romain Dupont, Mohamed Tamaazousti +1
Visual SLAM - Simultaneous Localization and Mapping - in dynamic environments typically relies on identifying and masking image features on moving objects to prevent them from nega…
AVAE: Adversarial Variational Auto Encoder
Antoine Plumerault, Hervé Le Borgne, Céline Hudelot
Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic image…
Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning
Yannick Le Cacheux, Adrian Popescu, Hervé Le Borgne
Zero-shot learning (ZSL) makes object recognition in images possible in absence of visual training data for a part of the classes from a dataset. When the number of classes is larg…
Controlling generative models with continuous factors of variations
Antoine Plumerault, Hervé Le Borgne, Céline Hudelot
Recent deep generative models are able to provide photo-realistic images as well as visual or textual content embeddings useful to address various tasks of computer vision and natu…
Learning Finer-class Networks for Universal Representations
Julien Girard, Youssef Tamaazousti, Hervé Le Borgne +1
Many real-world visual recognition use-cases can not directly benefit from state-of-the-art CNN-based approaches because of the lack of many annotated data. The usual approach to d…
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…