BlitzNet: A Real-Time Deep Network for Scene Understanding

Nikita Dvornik; Konstantin Shmelkov; Julien Mairal; Cordelia Schmid

doi:10.1109/ICCV.2017.447

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 : Object detection Real-time Neural Networks Semantic Segmentation

Type de document :

Communication dans un congrès

ICCV 2017 - International Conference on Computer Vision, Oct 2017, Venise, Italy. International Conference on Computer Vision, pp.4174-4182, 2017, 〈10.1109/ICCV.2017.447〉

Domaine :

Informatique [cs]

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https://hal.archives-ouvertes.fr/hal-01573361
Contributeur : Nikita Dvornik <>
Soumis le : mercredi 9 août 2017 - 11:43:46
Dernière modification le : jeudi 13 septembre 2018 - 11:32:08

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