BlitzNet: A Real-Time Deep Network for Scene Understanding

Nikita Dvornik; Konstantin Shmelkov; Julien Mairal; Cordelia Schmid

BlitzNet: A Real-Time Deep Network for Scene Understanding

Nikita Dvornik ¹ Konstantin Shmelkov ¹ Julien Mairal ¹ Cordelia Schmid ¹

1 Thoth - Apprentissage de modèles à partir de données massives

Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann

Abstract : Real-time scene understanding has become crucial in many applications such as autonomous driving. In this paper, we propose a deep architecture, called BlitzNet, that jointly performs object detection and semantic segmentation in one forward pass, allowing real-time computations. Besides the computational gain of having a single network to perform several tasks, we show that object detection and semantic segmentation benefit from each other in terms of accuracy. Experimental results for VOC and COCO datasets show state-of-the-art performance for object detection and segmentation among real time systems.

Keywords : Real-time Neural Networks Semantic Segmentation Object detection

Type de document :

Communication dans un congrès

ICCV 2017 - International Conference on Computer Vision, Oct 2017, Venise, Italy. International Conference on Computer Vision, pp.11, 2017

Domaine :

Informatique [cs]

Liste complète des métadonnées

Littérature citée [29 références] Voir Masquer Télécharger

https://hal.archives-ouvertes.fr/hal-01573361
Contributeur : Nikita Dvornik <>
Soumis le : mercredi 9 août 2017 - 11:43:46
Dernière modification le : mercredi 11 avril 2018 - 01:58:52

egpaper_final.pdf

Fichiers produits par l'(les) auteur(s)

BlitzNet: A Real-Time Deep Network for Scene Understanding

Fichier

Identifiants

Collections

Citation

Exporter

Métriques

671

1588

Contact

BlitzNet: A Real-Time Deep Network for Scene Understanding

Fichier

Identifiants

Collections

Citation

Exporter

Partager

Métriques

671

1588

Contact