BlitzNet: A Real-Time Deep Network for Scene Understanding
This addresses the need for efficient scene understanding in applications like autonomous driving, though it is incremental as it builds on existing multi-task learning approaches.
The paper tackles real-time scene understanding by proposing BlitzNet, a deep architecture that jointly performs object detection and semantic segmentation in one forward pass, achieving state-of-the-art performance on VOC and COCO datasets among real-time systems.
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.