BlitzNet: A Real-Time Deep Network for Scene Understanding
2017-08-09ICCV 2017Code Available0· sign in to hype
Nikita Dvornik, Konstantin Shmelkov, Julien Mairal, Cordelia Schmid
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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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| PASCAL VOC 2007 | BlitzNet512 + seg (s8) | MAP | 81.5 | — | Unverified |