Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

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

(1, 2) , (1) , (2)
1
2

Abstract

Zero-shot learning aims to recognize instances of unseen classes, for which no visual instance is available during training, by learning mul-timodal relations between samples from seen classes and corresponding class semantic representations. These class representations usually consist of either attributes, which do not scale well to large datasets, or word embeddings, which lead to poorer performance. A good trade-off could be to employ short sentences in natural language as class descriptions. We explore different solutions to use such short descriptions in a ZSL setting and show that while simple methods cannot achieve very good results with sentences alone, a combination of usual word embeddings and sentences can significantly outperform current state-of-the-art 3 .
Fichier principal
Vignette du fichier
2010.02959.pdf (818.83 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03003689 , version 1 (13-11-2020)

Identifiers

  • HAL Id : hal-03003689 , version 1

Cite

Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu. Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning. ECCV 2020 workshop Transferring and adapting source knowledge in computer vision (TASK-CV), Aug 2020, Glasgow, United Kingdom. ⟨hal-03003689⟩
44 View
36 Download

Share

Gmail Facebook Twitter LinkedIn More