Modulating early visual processing by language
Document type :
Communication dans un congrès avec actes
Title :
Modulating early visual processing by language
Author(s) :
de Vries, Harm [Auteur]
Department of Computer Science and Operations Research [Montreal]
Strub, Florian [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Mary, Jérémie [Auteur]
Sequential Learning [SEQUEL]
Larochelle, Hugo [Auteur]
Google Brain
Pietquin, Olivier [Auteur]
DeepMind [London]
Courville, Aaron [Auteur]
Department of Computer Science and Operations Research [Montreal]
Department of Computer Science and Operations Research [Montreal]
Strub, Florian [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Mary, Jérémie [Auteur]
Sequential Learning [SEQUEL]
Larochelle, Hugo [Auteur]
Google Brain
Pietquin, Olivier [Auteur]
DeepMind [London]
Courville, Aaron [Auteur]
Department of Computer Science and Operations Research [Montreal]
Conference title :
NIPS 2017 - Conference on Neural Information Processing Systems
City :
Long Beach
Country :
Etats-Unis d'Amérique
Start date of the conference :
2017-12-04
HAL domain(s) :
Informatique [cs]/Réseau de neurones [cs.NE]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [en]
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, ...
Show more >It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic inputs are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the entire visual processing by a linguistic input. Specifically, we introduce Conditional Batch Normalization (CBN) as an efficient mechanism to modulate convolutional feature maps by a linguistic embedding. We apply CBN to a pre-trained Residual Network (ResNet), leading to the MODulatEd ResNet (MODERN) architecture, and show that this significantly improves strong baselines on two visual question answering tasks. Our ablation study confirms that modulating from the early stages of the visual processing is beneficial.Show less >
Show more >It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic inputs are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the entire visual processing by a linguistic input. Specifically, we introduce Conditional Batch Normalization (CBN) as an efficient mechanism to modulate convolutional feature maps by a linguistic embedding. We apply CBN to a pre-trained Residual Network (ResNet), leading to the MODulatEd ResNet (MODERN) architecture, and show that this significantly improves strong baselines on two visual question answering tasks. Our ablation study confirms that modulating from the early stages of the visual processing is beneficial.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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