Papers › BlitzNet: A Real-Time Deep Network for Scene Understanding
BlitzNet: A Real-Time Deep Network for Scene Understanding
Nikita Dvornik, Konstantin Shmelkov, Julien Mairal, Cordelia Schmid
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | PASCAL VOC 2007 | BlitzNet512 + seg (s8) | MAP | 81.5% | #9 of 30 | Archive leaderboard | report |
| Real-Time Object Detection | PASCAL VOC 2007 | BlitzNet512 (s4) | FPS | 24 | #2 of 4 | Archive leaderboard | report |
| Real-Time Object Detection | PASCAL VOC 2007 | BlitzNet512 (s4) | MAP | 79.1% | #2 of 4 | Archive leaderboard | report |
| Real-Time Object Detection | PASCAL VOC 2007 | BlitzNet512 (s8) | FPS | 19.5 | #3 of 4 | Archive leaderboard | report |
| Real-Time Object Detection | PASCAL VOC 2007 | BlitzNet512 (s8) | MAP | 81.5% | #3 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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