Work

BlitzNet: A Real-Time Deep Network for Scene Understanding

Year: 2017

Type: conference-paper

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.

Source: IEEE/CVF International Conference on Computer Vision (ICCV)

DOI: 10.1109/iccv.2017.447

Authors: Nikita Dvornik, Konstantin Shmelkov, Julien Mairal, Cordelia Schmid

Institutions: Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Laboratoire Jean Kuntzmann, THOTH: Apprentissage de modèles visuels à partir de données massives

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Nvidia, Agence Nationale de la Recherche

Awards: large-scale machine learning and applications, G8777520260