Zero-shot Learning with Deep Neural Networks for Object Recognition

no code implementations • 5 Feb 2021 • Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu

The general approach is to learn a mapping from visual data to semantic prototypes, then use it at inference to classify visual samples from the class prototypes only.

Object Recognition Zero-Shot Learning

Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning

no code implementations • 6 Oct 2020 • 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 seen classes and corresponding class semantic representations.

Word Embeddings Zero-Shot Learning

Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning

no code implementations • 6 Aug 2020 • Yannick Le Cacheux, Adrian Popescu, Hervé Le Borgne

When the number of classes is large, classes are usually represented by semantic class prototypes learned automatically from unannotated text collections.

Object Recognition Zero-Shot Learning

Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning

no code implementations • ICCV 2019 • Yannick Le Cacheux, Herve Le Borgne, Michel Crucianu

Recognizing visual unseen classes, i. e. for which no training data is available, is known as Zero Shot Learning (ZSL).

Attribute Zero-Shot Learning

From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process

no code implementations • 26 Sep 2018 • Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu

Zero-shot learning (ZSL) is concerned with the recognition of previously unseen classes.

Generalized Zero-Shot Learning