AMECON: Abstract meta-concept features for text-illustration

I. Chami; Y. Tamaazousti; H. Le Borgne

AMECON: Abstract meta-concept features for text-illustration

I. Chami Y. Tamaazousti ¹ H. Le Borgne ¹

1 DIASI - Département Intelligence Ambiante et Systèmes Interactifs

Abstract : Cross-media retrieval is a problem of high interest that is at the frontier between computer vision and natural language processing. The state-of-the-art in the domain consists of learning a common space with regard to some constraints of correlation or similarity from two textual and visual modalities that are processed in parallel and possibly jointly. This paper proposes a different approach that considers the cross-modal problem as a supervised mapping of visual modalities to textual ones. Each modality is thus seen as a particular projection of an abstract meta-concept, each of its dimension subsuming several semantic concepts ("meta" aspect) but may not correspond to an actual one ("abstract" aspect). In practice, the textual modality is used to generate a multi-label representation, further used to map the visual modality through a simple shallow neural network. While being quite easy to implement, the experiments show that our approach significantly outperforms the state-of-the-art on Flickr-8K and Flickr-30K datasets for the text-illustration task. The source code is available at http://perso.ecp.fr/~tamaazouy/.

Keywords : Natural language processing systems Neural networks Semantics Speech recognition Common spaces Cross-media retrieval Meta concepts Semantic concept Source codes State of the art Text-illustration Visual modalities Abstracting

Document type :

Conference papers

Domain :

Computer Science [cs]

Complete list of metadatas

https://hal-cea.archives-ouvertes.fr/cea-01813718
Contributor : Léna Le Roy <>
Submitted on : Tuesday, June 12, 2018 - 3:30:08 PM
Last modification on : Wednesday, January 23, 2019 - 2:39:24 PM

AMECON: Abstract meta-concept features for text-illustration

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AMECON: Abstract meta-concept features for text-illustration

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