BlitzNet: A Real-Time Deep Network for Scene Understanding

08/09/2017 ∙ by Nikita Dvornik, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 8

page 10

page 11

Code Repositories

blitznet

Deep neural network for object detection and semantic segmentation in real-time. Official code for the paper "BlitzNet: A Real-Time Deep Network for Scene Understanding"


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.